Managing budgets effectively is vital for the financial health and ROI of any web or app development project. Every step, from design to post-launch, involves a complicated mix of costs that needs strategic handling. For businesses like Conferensity, aiming to expand their digital offerings, mastering budget control isn’t just good practice—it’s central to their strategic goals and project success.
You may not have heard of Conferensity. That’s okay, they don’t exist. We’re using them in this article (and other articles) as a way to discuss the strategies we cover. Their story in short:
Conferensity is currently a B2B event management platform with pretty basic features. Their primary goal:
“To become a comprehensive conference solution and enjoy the increased market share and revenue possibilities this will create.”
It’s a fine dream.
Their strategy involves expanding their web-based software’s capabilities and developing two apps – one for conference admins and another for attendees.
While these are three digital products, 1 website and 2 apps, there is actually a fourth project – the updated backend that will implement the features the website and apps will provide interfaces for. This backend will also require extensive work to provide all the necessary admin features to manage the new frontend features.
Now, understanding and managing development costs requires careful planning plus the flexibility to handle unforeseen financial challenges. Using Conferensity as our stand-in for just about any business, we’re going to talk about strategies for budget planning, staying within budget, and leveraging cost management to achieve business growth.
Yes, this is stating the obvious: Budget planning is crucial in web and app development. It is about investing the time (lots of time) and the brain power (also lots) to get the right numbers next to the right actions.
Once you’re through your first iteration of your budget (you will be updating it as you progress), you have:
For both startups and established businesses, budgeting is the key to generating ROI and avoiding financial missteps.
The primary purpose of budget planning is finding a way forward despite your constrained resources. Do it well and you will survive the inevitable financial challenges and be able to adapt to project shifts as the real world interferes with your plans, thereby keeping moving toward your goals.
Conferensity has decided to align their work on their site and apps with their growth objectives in three ways:
This three pronged approach allows them to make the best use of their developer resources while launching growth-focused features into the marketplace.
For software products, at least initially, development costs dominate. Staying on top of development costs is key to effective budget management, from initial design prototypes to post-launch updates.
Initial costs will involve user research (don’t skip this) accompanying prototype development. For Conferensity, development costs form the lion’s share of their budget, including the frontends (for the site and two apps) and the shared backend infrastructure.
Infrastructure expenses, including hosting, third party integrations, and payment gateways, are vital for Conferensity’s app performance, especially for real-time features.
Unexpected costs are inevitable, requiring a contingency budget for unforeseen challenges. But, if you’ve gone deep enough in your planning, these unexpected costs should be arising out of the truly unforeseen – accidents, natural disasters, and the like. Your development budget should have margins to account for the acknowledged “slippage” that always occurs in software development.
For these unexpected costs, reserving a portion of the budget as a contingency is crucial. It’s painful to pull this money out of line items and see your timeline shift into the future. But the contingency is a necessary de-risking step so you will make it to the end. If you’re betting your business’s future on a project you want to stack your odds so you can be sure you’ll finish it.
Budget control is about finding ways to accomplish as much as possible within your resource constraints. Key strategies include prioritising based on ROI to start generating revenue as quickly as possible, adopting lean development approaches to push value to your users, and making strategic decisions between a pure in-house development model or augmenting your developers with a software development team extension to compress your timeline.
For businesses like Conferensity, focusing on high-ROI features, such as an attendee app, can ensure immediate revenue impact. Employing a lean methodology helps in launching with essential features and iterating based on user feedback, optimising resources and aligning with market needs.
Because Conferensity was a web-first product, they had the inhouse skills for the web app and the backend. Rather than lose a big chunk of time to re-skilling their developers, and then losing more time spreading the same team across more products, they opted to run with a software development team extension.
Sourcing new team members from a near-shore provider, Conferensity had instant iOS and Android developer skills inhouse. These developers were also less costly, letting them stretch their development budget over more developer hours and advance production of both apps alongside the website.
Once a project is underway, you need effective cost management to keep it on the tracks. Continuous assessment of costs and scheduling is a necessity. There are too many software options for tracking these things, each with their own way of doing things. Too many for us to go into details on how to use any of them.
In broad strokes, part of your process needs to be the regular review of where your project is at and how that compares to your budget plan. Someone should be looking at the numbers weekly. How often you decide to act on those numbers depends on how you’ve structured your development. If you’re doing two week sprints, then you should know as you reach the end of the current sprint what needs to change before you start the next sprint, if anything.
Once you start launching features you need to incorporate their running costs and any revenue (we hope there is revenue) into your reviews. Unless you are incredibly astute, whatever estimates you had for those numbers will have to be updated. The effect of those new numbers on your budget plan are going to have to be followed through. Do you suddenly have more cash to allocate? Are you losing developers to bug fixes or unforeseen infrastructure upgrades?
Good problems or bad problems – your budget plan now has a life of its own. You need to keep on top of costs and schedules to keep the budget aligned with reality so it remains an effective tool for getting the project done.
Strategic budgeting is foundational for sustainable business growth. In digital products that strategy is built upon the development, serially or in parallel, of different platforms and different features on those platforms.
For Conferensity, aligning their budget with the platforms and features that will best contribute to their business objectives is essential for their big dream of becoming a leading conference solution provider with all the best apps and features.
Making informed investment decisions based on cost-benefit analysis informs the staging of the development of new features. Having the option to choose between inhouse development and development assisted by a software development team extension brings a new level of flexibility in both cost and time-to-market.
Effective budget management allows businesses to scale and adapt to changing market demands without putting themselves at risk. This strategic approach ensures that web and app development projects contribute directly to long-term success and business expansion.
Budget management is more than fiscal discipline aka bean counting; it’s a strategic element of innovation and long-term success. For businesses embarking on digital projects, like Conferensity, efficiently managing budgets is decisive for aligning development initiatives with business goals, ensuring project viability, and supporting growth.
Adopting strategic budgeting practices, enhanced by the latest management tools and continuous performance monitoring, is critical for navigating digital transformation. It enables businesses to get the most out of the resources they have, seize opportunities that appear beyond their reach, and achieve their vision.Planning on winning: Aligning product and business goals to drive ROI
This might be you. You know you need a website. Or you know you need an app. Or both. Whatever you choose, building these digital products is going to be a major investment and you need it to pay off.
At this stage, before you’ve committed resources, the most important thing to get right is the alignment between your business goals and your digital product development. Getting these aligned and keeping them aligned is the key to success and that success is driven by strategically maximising your ROI.
This alignment requires a deep and thorough analysis of your options and you might need to adopt new processes to get there. That’s why we’re going to give you a good overview of what you need to do. To illustrate the process we’ll be using a fictional B2B event management SaaS we’ve named “Conferensity”.
The first step in the process is determining the connections between your business goals and your potential digital products.
As we’ve said, the key to achieving successful outcomes with your digital products is ensuring they’re in sync with your overarching bsuiness objectives. These key objectives might be things like:
It’s up to you to decide which goals you should be chasing. Looking at that short list, they all seem worth pursuing. They are. But you need to prioritise your goals with an eye not just towards the possible ROI, but also the feasibility of execution by your team and solution partners.
To see how making these choices might work in practice, let’s look at Conferensity as an example of how to unite business goals and digital products.
Conferensity is currently a basic B2B event management platform. Their primary goal:
“To become a comprehensive conference solution and enjoy the increased market share and revenue possibilities this will create.”
Their strategy involves expanding their web-based software’s capabilities and developing two apps – one for conference admins and another for attendees.
While these are three digital products, 1 website and 2 apps, there is actually a fourth project – the updated backend that will implement the features the website and apps will provide interfaces for. This backend will also require extensive work to provide all the necessary admin features to manage the new frontend features.
By dedicating their resources to these four digital projects Conferensity is actively working towards achieving their primary business objective.
If Conferensity were in a different industry/market they might have different goals. Let’s look quickly at some other examples:
Returning to Conferensity, let’s go into a little more detail about their goals and plans. Here is some of what they want their products to do:
Their first app will be for conference admins, duplicating the client-accessible website’s functionality.
The second app, designed for attendees, will allow attendees to reserve seats, purchase tickets to the conference, and pay for any related services. It will also have features like schedules and venue maps, presentation start reminders, live questions via chat rooms, presentation feedback, and special features for speakers.
By selecting specific features for their user-facing digital products that will support their business goals, Conferensity is weaving together their business objectives and digital initiatives in a way that maximises the potential for success.
You can’t afford to waste time or burn money developing your digital products. So you need to identify and prioritise your key objectives and the initiatives that are going to achieve them.
Your key objectives are often broad and can sound obvious – “increase revenues by 12% YOY”, “Reduce monthly churn by 5%”, but each objective will require hundreds if not thousands of actions and even more decisions and judgements across your team. Too many things to specify beforehand. So keeping them simple and clear avoids “analysis paralysis”, enables autonomy across teams, and keeps everyone moving in the same direction.
Having established your objectives you then examine your proposed digital products and determine exactly how they are going to help you achieve them.
For products like apps and websites this is a bit of a two-way process. You know you need the digital products. You just need to ensure you get the link between their features and your objectives right.
The outcome of this process is the first high-level breakdown of your product development plan.
These are the general steps you need to take to identify your objectives and then to rank them by priority:
Returning to Conferensity as our example, their key priorities are increasing market share and creating new revenue streams by providing purchasing options in a new conference attendee app. The increased market share will be driven by new features and improved user experience on their site, as well as the introduction of an app that will allow conference admins to easily manage their conference on their phones on the day.
The attendee app will help streamline running and monitoring a conference, and by providing functionality to purchase conference tickets and pay for associated events and perhaps even merchandise, Conferensity can capture some of the value of that spending.
By focusing on these digital initiatives, they are working towards providing a comprehensive conference management solution that will attract more clients and set them apart from competitors.
These changes they are considering are broad and complex, including the upgrade of their backend and website plus the introduction of two new digital products. To help them set priorities that will allow them to build towards success Conferensity might consider the following:
Notice the interplay between points 2 and 3. It can be frustrating when the project with the highest predicted ROI cannot be the first project implemented.
By finding clear and solid answers to these questions Conferensity can create an implementation plan for their digital products that directly supports their business objectives while minimising risk.
Next, let’s discuss the importance of setting clear ROI-driven metrics and targets for digital project success.
Once you’ve determined your priorities for your digital product, the next step is to set up measurable targets to track your progress and success. This is where key performance indicators (KPIs) come into play. By setting KPIs for your web and app development projects, you’ll have a transparent benchmark to measure your achievements and make necessary adjustments.
Here’s how you can go about establishing your ROI-guided metrics and targets:
Let’s look again at Conferensity’s plans. They might establish targets for:
By having these KPIs in place, Conferensity can track their progress and if there are any issues they have the data to support the decisions they will need to make. This way they can adjust their strategies as needed and ensure they’re always moving towards their overarching goals.
Once you’ve established your key objectives and KPIs, the next crucial step is to create a project roadmap that contains clear milestones, deadlines, and deliverables. This roadmap needs to take into account your resources, budget constraints, and risk management, ensuring a realistic and achievable plan of action.
If you haven’t created such a roadmap before, here is the basic outline of the process:
In Conferensity’s case, their digital product roadmap might involve implementing current platform functionality in the conference admin app first, as it could provide immediate ROI by streamlining their clients’ management processes. Next, they could focus on the attendee app, starting with basic schedule and booking features to make it usable, but also including ticket purchasing as early as possible. This is one of the most impactful features for its ROI potential. By collecting a transaction fee for each ticket sold, Conferensity can generate additional revenue, further supporting their business growth.
By following this phased approach, Conferensity can keep their digital projects aligned with their business goals while efficiently managing resources and budgets. Regular progress reviews will help them stay agile and adapt to any changes in their business environment or project requirements.
It sounds so easy, doesn’t it? But really, that’s all there is to it. The challenge is in the quality of analysis and the judicious setting of priorities. Digital products are complex, but KPIs based on your goals and what you want your websites or apps to deliver will keep you on track. And those KPIs come out of establishing clear ROI-driven metrics and targets.
The process is clear. Thousands and thousands of businesses have gone through it. The challenge is in knowing your business, knowing your market, and finding those clear steps to get from where you are now to your final goals. Monitoring your progress and being prepared to shift your strategy and adjust plans is as big a part of success as making the plan in the first place.AI is going to make remote teams and video calls less frustrating
While COVID changed the nature of work for lots of people, at SoftwareSeni online and remote work has always been a part of our experience.
Team extensions for software development is the key service we provide our partners. Regular virtual stand-ups, code reviews, as well as one-to-one meetings for things like discussing design decisions and troubleshooting are part of the normal working day for our teams.
As comfortable as we are with video meetings, we are quick to adopt any practices or tools that can help us streamline and get the most from them. So we’re looking forward to trying out the new AI powered features announced by Zoom and Google.
Let’s have a look at the features we think will make a real difference for everyone who relies on video meetings. Having said that, this quick overview is biased towards mixed local/remote software development teams.
This one seems like it is targeted at meeting with multiple participants, but we think this will help everyone. First, being freed from taking notes so you can focus on what is being discussed is a big win. Second, having a thorough summary of even a one-on-one meeting means no-one has to worry about the quality of their note-taking skills or their memory.
We’re also assuming that these summaries end up being incorporated into, say, your Google Drive, if you are using Google Meet. Then they become searchable and discoverable and easy to incorporate into your planning and follow-ups.
This is an extension of the previous feature, but is worth pointing out. What we like about this is, again, documenting the meeting is handled by Google’s or Zoom’s AI. They’ll extract the major talking points and action items for anyone who missed it.
If that’s not enough detail, both services offer transcripts if you need the full text of the meeting.
Like watching Netflix with subtitles on, but more useful. Captions on meetings can really help with the inconsistent audio across multiple speakers.
Both Google Meet and Zoom also provide the option to deliver translated captions. We find that despite our multilingual team’s high level of language mastery, translated captions improve the clarity and quality of communications in video meetings.
The captioning and translation is not perfect, but like watching movies, the combination of speech and text reduces the effort and concentration needed to follow what is happening.
This isn’t really a thing we care about, but maintaining privacy by having your background blurred out or replaced is an option we can get behind. We should all want video calls to be comfortable for everyone.
Duet for Google Meet is also promising “studio lighting” and other image enhancements, as is Zoom. This is starting to veer into the world of “filters” and how we appear on camera compared to “reality” (remember the lockdown boomers rendered as potatoes on call?). But again, if these features help team members feel more comfortable on call or even simply enhance everyone’s experience by providing clearer views of each other, then we’re all for it.
Zoom’s AI Companion is all about deepening integration with their suite of calendar and messaging tools. We’re a Google Workspace shop, so Zoom’s features like AI-powered meeting scheduling is not going to impact us. The nature of software development doesn’t really require it. You’re either participating in scheduled stand-ups, etc, or you’re hopping on a call you’ve just coordinated via chat.
Zoom also has features that monitor and report on conversations. They sell it as a feature to help improve the quality of your sales team’s calls. Not the kind of thing our developers need for their day-to-day interactions with other team members.
We’ll let you know, but we suspect that, despite the immense investment in tech and infrastructure these features represent, we’re only going to see an incremental improvement, more of a quality of life increase than an efficiency multiplier.
Calls will go more smoothly because captions and translations, as well as transcripts and summaries, will allow everyone to focus on the purpose of the call instead of the technology of the call. Those transcripts and summaries should reduce the flurry of follow-up emails and messages after calls, which will be welcome.
Once your business gets above a handful of people, communication becomes the key component in your success. Our headcount is north of 150 so we know this firsthand. In the balancing act that is making your business work, a balancing act that includes not just optimizing who you work with but where they work from, video calls are going to be part of your internal comms mix. These new features should help you bring them closer to the quality and effectiveness of in-person meetings.
But we’ll wait and see if it turns out that way.WFH vs RTO: How and why we transitioned
As more and more businesses require employees to return to the office (RTO) there have been two popular, but cynical, takes on the trend. The first is that the businesses don’t want their offices sitting empty, especially if they own them rather than lease them. The second is that the businesses are control freaks and want to be able to once again monitor their employees every moment.
These takes are not completely wrong. There will always be businesses that make decisions based on dubious reasoning. But for most businesses that have more than a handful of employees, bringing them back to the office can be a matter of survival.
As a company that provides software development services and employs a large and growing team of software developers, we’ve felt the impact of Work From Home. It’s affected our staff and our operations to the point where we are now implementing practices to counteract it.
This article is a discussion about what we experienced during the COVID WFH period and its aftermath, what we’ve learned from it, and how we are changing the way we work to address it.
During the early part of WFH under COVID we experienced minimal impact in our transition away from the office. This was due to all of our staff transitioning directly from the in-office experience where they all knew each other well and were part of the culture.
It was during the later phase of COVID WFH, when everyone had undergone extended isolation and new staff members had joined us, that we started to see the impact on our culture and our mission.
The impact can be summarised in 4 words – Continuity, Clarity, Capability and Growth.
Continuity, in this context, includes maintaining a business’s culture, practices and institutional knowledge while also preserving staff development and advancement and integrating new hires into the business.
Clarity is having a clear view into the operations of the business. Some parts of this view can be found in reports. Other parts are found in meetings. Some parts are found only by in-person interactions and observations.
Capability is being able to execute at the level your business needs. The big challenge of running a business is you never have the perfect person for every role. You need to be able to work with the resources you have. But these resources might limit your options.
Growth is exactly what it is. It might be growth in product or service offerings or growth in size or growth in profitability.
No-one can deny that people like working from home. At the same time, no-one can claim that everyone likes working from home or that everyone is better off working from home or that there are no negative consequences created by WFH strategies.
What we found with our large team was that the people who fared best under work from home were in senior roles, had strong communication skills, and were in positions that required only minimal collaboration or reporting. Senior developers are an example of this kind of role.
On the other hand, we found junior developers were struggling to complete work. This caused them stress and in some cases anxiety. They suffered, their work suffered, and more pressure was put on senior developers to fill in gaps. But it was during lockdown and we all just soldiered on.
When we began bringing employees back to the office we found that many of our junior developers were still just that – junior developers. Despite the amount of time they had been with SoftwareSeni they had made little advancement in their skillset, including not just programming but the soft skills that are part of working within a team, as well as in carving out their own unique career path. We saw a similar slowdown in our mid-tier developers as well.
We use a mix of formal and informal mentoring. Formal mentoring includes activities like shared planning sessions and code reviews. Informal mentoring covers all those little moments of learning and experience transfer that happen in an office. In an office environment it is easy to turn to someone more experienced for help when you get stuck. In an isolated environment like WFH, where finding a free coworker with the right experience and organising a video call and screen sharing is necessary to answer a question, that question might go unanswered, creating delays or resulting in a poorer quality solution.
There is also learning by osmosis that takes place in an office environment. Being present while more experienced coworkers discuss problem solving and strategies for the problems they face is itself an opportunity for learning.
You may be reading these examples and imagining solutions for each of them that don’t require working in an office. Just put a call out on Slack. Invite junior developers to Zoom calls. They look like solutions, but the level of friction in using them and the quality of the experience they provide results in worse results than simply being in the same space together. We know this because we had staff working from home for nearly two years using these “solutions” and things are going so much better now most of the team is back in the office.
Bringing the team into the office is necessary for Continuity: continuity of our team members’ careers, continuity of inhouse experience, and continuity of “institutional knowledge” and our culture.
Without this emphasis on maintaining continuity a business like ours with a team in the hundreds could see a collapse of our capabilities and our quality of execution drop to the point where we can no longer be competitive.
Goodhart’s law states “When a measure becomes a target, it ceases to be a good measure”. This law is about how when given a target, like a KPI, people’s natural tendency is to optimise their actions and find ways to game the system to hit the target.
During Work From Home we found something similar happening in our regular meetings. It wasn’t malicious, it was just the outcome of the team striving for efficiency.
Since, of course, all our meetings were video meetings, we adopted clear agendas for running our regular meetings. We all knew in advance what needed to be discussed and we got through them quickly and cleanly despite the inherent clumsiness of group video calls.
But once we returned to the office it became apparent that what was being reported in the meetings had become a streamlined, tightly focused delivery of the agenda items. Issues of all kinds had developed in the business but they had fallen through the cracks of our carefully planned and cleanly executed meetings.
The issues had been allowed to develop because as everyone was sitting alone in their home offices they weren’t just physically isolated from each other, they were physically isolated from the issues. And almost all the issues were complex and identifying them as issues from a single perspective was unlikely.
You can’t have a report for everything. You can’t have a KPI for every facet of your business. Workflows can’t cover every situation. But there are still countless factors that need to be monitored and dealt with.
They’re different for every business, they’re different for every department, and they’re different for every team. Seal everyone off from each other and you may never be able to connect the dots.
Clarity comes from having multiple views of the same shared experience. Clarity comes from secondary sources, back channels, passing comments, and interactions with the team. This is where recognising problems and solutions begins. From here they end up in reports.
This is where Capability comes into play. Your business needs to be able to run, survive and thrive with the team you have available. What you can do is constrained by the skill and experience of your team.
We found performance under WFH was dependent on the skills of the manager. WFH, and remote work in general, requires a higher level of skill to pull off than normal in-office team management.
It takes time to adjust to the different management style. Again, like our meetings discussed above, reducing all interactions to video calls that need to be scheduled or coordinated changes outcomes.
Simple things that don’t need a meeting to be shared, like job status, hours worked, and so on, are easy to monitor. Addressing them, however, along with other more subtle concerns, like engagement, role clarity, skill acquisition, morale, and work quality, is made more difficult by the inherently confrontational format of a video call.
One of our main services is software development team extensions. Remote teams as a service, basically. You would expect that we would be masters of remote team management. But the teams are remote teams only for our clients. All of our team members, before COVID, worked out of our offices. Part of our service is acting as a secondary level of management or as a team support role for our clients. This was done in-person until the lockdowns. Which meant our team support staff had to learn the new skills required to manage remotely. Some adapted better than others.
Some businesses rely on employees executing in roles with basic skill requirements. Other businesses rely on a small number of highly skilled employees who can execute independently of each other. These businesses can continue to do well under Work From Home or with a fully remote team.
If you’re like us, where your team is growing, where you need employees advancing between roles, and you need the resilience of a team where knowledge is shared instead of siloed, then Work From Home is not the best option.
Growth for a business relies on having a long term vision and the strategies that support it. For SoftwareSeni, our team is the heart of our strategy: growing our team, helping them develop their skills, supporting them as they grow to be important contributors to our company and to our clients.
With people at the centre of our growth and success, Work From Home has been a disadvantage. It didn’t stop us from delivering for our clients, but it did slow us down as an organisation. A big part of that slowing was the result of difficulties being faced by so many of our employees.
In January 2023 we started bringing the team back to the office. With a couple years of remote work experience throughout the organisation, we have been able to approach it with flexibility.
Some roles are able to split time between WFH and RTO, and most roles are able to WFH on the occasion where a stint of independent work aligns with current requirements.
Clarity and morale, which is really the keystone of continuity, were the first and most obvious wins as the bulk of our team returned to the office. No-one would say they enjoy a return to commuting, but the ease of working in a team that is physically present, in terms of efficiency, quality and camaraderie, does help ease the pain.
The WFH period has left us a more flexible team and a more resilient team. This increase in capability is contributing to a new burst of growth as projects and inhouse programs that were planned during COVID and were waiting on our return to the office are now being implemented.
Not every business needs to work from an office. And there are probably very few businesses that need every team member in the office every day of the week.
Looking at your business through the lenses of Clarity, Continuity, Capability and Growth, like we did, provides a clear structure for helping you decide where on the continuum between Work From Home and Return To Office your business needs to place itself.How are LLMs like ChatGPT going to revolutionize real estate?
Here is how AIs like ChatGPT are going to revolutionise real estate: invisibly, by increasing efficiency in the market, and visibly, by making customer services a bit cheaper and higher quality.
AI is already in real estate, and has been for a while, but it’s not AI like ChatGPT. The AI in real estate is a collection of technology and techniques known generically as Machine Learning (ML) and it uses the same basic building blocks, called neural networks, that ChatGPT uses, but with different structures and different arrangements of the building blocks because they have a different purpose to ChatGPT.
Trulia is an example of a proptech company leveraging the power of ML. They’ve used it to build their property recommendation engine, for predicting user engagement, and for identifying property features from photographs.
These are things ChatGPT can’t do. ChatGPT is a Large Language Model (LLM). It’s a multi-layer neural network that has been fed a tremendous amount of text and it is very good at stringing words together into sentences in response to some input text.
It has been fed so much text that it is very good at masquerading as an under-trained lawyer, a sub-par business analyst, a mediocre marketer, a hack journalist, etc, simply by stringing together pieces of the text it has been trained on.
While being very good at generating text, LLMs like ChatGPT aren’t very good at math. You can’t use them to do things like predict user engagement or identify property features from photographs.
What LLMs are good at is bridging the vast gulf between human written text and the kind of numerical inputs ML systems can deal with.
This can be as simple as basic sentiment analysis, turning a review like “This is so not the best hamburger joint in town” into a -1, and a review like “I hate how good their fries are” into a +1 – simple numbers that can be used in a prediction model.
Stepping beyond this, LLMs can be used to turn text into ranks. An LLM can consistently turn, say, countertop descriptions like “mustard yellow formica”, “granite”, “hand-poured, hand-polished concrete” into a range from 1 to 10. The real value in this is that when it sees a new description, like “clear resin featuring embedded seashells”, it can fit it into the same range.
Moving beyond simple ranking, we can combine it with data extraction. Here is an example where an LLM pulls specific data from a police report. It could just as easily be a property listing:
This kind of “encoding” has normally been a job for humans. This drove up the price for the production and updating of useful databases, making them expensive, rare and proprietary. Spending the money and the time to do the encoding and compile a database was a nice way to build a moat.
So getting data isn’t going to be much of a problem any more. That’s going to bring efficiency increases. Maybe some parts of the market will eke out a few more percentage points in returns, but only if they can use the data.
Trulia, for example, isn’t running their services on LLMs. LLMs will only be part of the pipeline. The real work and real value will still be done with classic ML models fed with higher quality data encoded by LLMs.
So, that’s the invisible part of AIs impact on real estate. Let’s now look at the likely visible impact.
This visible impact is going to be based on the highly visible features of LLMs – their ability to generate text and respond in a conversational manner.
This is one of the recurring gotchas of building products with LLMs. It is so easy to get text out of an LLM that whatever product idea you have, if it is just producing text then your business model is in danger of becoming another company’s bullet point.
Chat is the big, consumer-facing application of LLMs like ChatGPT, Bing and Bard. With the ability to train LLMs on your business’s documentation via fine-tuning (OpenAI’s page on it) or using embeddings to constrain the information an LLM can work from, it seems we can all turn over our customer enquiries to software, saving us huge sums of money.
If only it were that simple.
The problem with using LLMs to “chat” to your customers is that you need to rely on raw text from the LLM. LLMs don’t have any judgement. They just generate text. The result is that they “hallucinate” – produce wrong or even wildly wrong responses – and there is no way to guard against this except having a human in the loop.
If you are building your customer service chatbot on top of an LLM like GPT-4, even with fine-tuning, the data you supply (chat logs, FAQs, blog articles) will be such a tiny percentage of the LLMs “knowledge” that it will inevitably “hallucinate” when answering questions. It might even insert information from a competitor’s products (that were documented on the internet prior to September 2021).
The other problem is that prompt jailbreaks – strategically written text that tricks the LLM into leaking information or hijacks it for the user’s own purposes – are not a solved problem.
The end result is that LLMs are not predictable enough to be trusted and so are not going to replace staff in customer service roles. Instead, they are going to act as an assistant to them. They’re going to help customer service staff find answers faster, they’re going to help customer service staff provide better answers, and they’re going to help customer service staff manage complex and ongoing interactions.
Crisp wrote up a fantastic deep dive into their efforts to add AI to their customer service. It’s not easy. It’s not cheap. It will get cheaper, but when you look at the steps they had to go through it is hard to imagine it getting easier.
Zendesk, the customer service SaaS, have also found that AI and LLMs are going to augment staff, helping to improve customer experience, instead of replacing them.
So AI chat as it is popularly imagined – replacing customer service staff with a bit of code – is not going to happen any time soon. For anything other than the most basic and pro forma customer enquiries, where an LLM chatbot might be limited to being a high-powered FAQ regurgitation engine, humans will still be in the loop and on the payroll.
That’s the big question. The AI services landscape is already fracturing into service providers who will run LLMs for you, fine-tune them for you, host your data for you, etc. AI tools are generic tools. At the coal face they work just as well for any industry, be it real estate or livestock management. So the shovel sellers are already busy selling shovels.
Proptech startups need to be looking at what untapped data is out there, preferably in text format if you want to jump on the LLM bandwagon. That text could be in paper format. Have you noticed how good OCR has gotten in the last year or two?
The data might already be sitting on your server. A database of transactions is a prediction model waiting to be trained. Prediction gives you the opportunity to optimise and de-risk, two things every industry wants to pay for.
Whether you want to reduce the cost of comms with tenants or streamline nine figure real estate transactions or improve compliance in building maintenance schedules, you need to be asking yourself where you can scrape, collect, licence or buy the data you need.
That, we expect, will be easier to do than hire the data scientists you’re going to need to make it work. Maybe the shovel sellers will fix that for us.How to harness AI and save your business from the future
Generative AIs like ChatGPT, Bard and Bing are changing the world faster than we can imagine. So fast that there are now ChatGPT-like AIs that can run on smartphones. So fast that the cost of training a ChatGPT-like AI has dropped from $4.6 million in 2020 to $450k today. And it’s happening so fast that startups are seeing their business model trashed by Google and Microsoft before they can get traction. The speed of change is making people suspect OpenAI is using ChatGPT to speed up their development of new AI and features.
As a startup or a small to medium business generative AI is going to accelerate and empower you. You and your team are going to work smarter and work faster. You’re going to do more with less or grow and do much, much more than you imagine possible.
AI is going to make it possible for a lone founder to do what a medium-sized company does today, and it will allow a medium-sized company to do what right now only a big company can do.
This shift in ability to execute is making people worried that jobs are going to be lost as everyone incorporates AI into their workflows. However, as has been pointed out by many commentators, if your revenue per employee keeps going up as they complete work faster and do more using AI, why would you fire anyone?
How much of a change in productivity will we see? In a draft of a working paper titled “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” released on March 27, 2023 by Eloundou et al, the following estimations are made:
“Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. … The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. … Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks.“
To help you understand how you can make the most of the changes generative AI is going to bring we’re going to start with the basics and give you a quick background on ChatGPT and how it and other generative AIs work (this includes Microsoft’s Bing and Google’s Bard).
After that we’ll go through the ways you can take advantage of AI and not be left behind.
To paraphrase a random internet commenter – “People would be shocked if they understood how simple the software behind ChatGPT really is”. If you’re technically minded this will show you how to build something similar to ChatGPT in 60 lines of code. It can even load the data used by GPT-2, one of ChatGPT’s predecessors.
ChatGPT was built by OpenAI. It’s a type of Large Language Model (LLM) and part of the class of AIs called “generative AI”. A language model is a computer program designed to “understand” and generate human language (thus “generative AI”). Language models take as input a bunch of text and build statistics based on that text – things like which letter is most likely to appear next to the letter “k” or which word is most likely to come before “banana” – then use those statistics to generate new text on demand.
When a language model is generating text, like in response to a question, at the most basic level it is simply looking at the text, in this case a question, and using the statistics it has generated to choose the word most likely to come next.
A Large Language Model (LLM) is just a language model trained on a large amount of text. It is estimated that the LLM that underlies the initial version ChatGPT, GPT-3, was trained on 300-400 billion words of text taken from the internet and books. That training was, basically, showing it a word from a document, like this article, and showing it the approximately 100-500 words that preceded it in the document (only OpenAI knows the actual number).
So if an LLM was fed this very article, it might be shown the word “human” and also the words “ChatGPT was built by OpenAI … and generate” that led up to it.
It turns out that when an LLM is fed nearly half a trillion words and their preceding text to build statistics with, those statistics capture quite complex and subtle features of language. That isn’t really a surprise. Human language isn’t random. It has a predictable structure, otherwise we couldn’t talk to each other.
It’s not just predictable. There is a lot of repetition. Repetition in the phrases we use, like “how’s the weather”, but also in sentence structure, “The cat sat on the mat. The bat spat on the hat.”. Even document conventions. Imagine how many website privacy policies ChatGPT would have been trained on by using the internet as a source of text.
When you ask ChatGPT a question, the words of your question become the preceding text for the next word. This preceding text is called the “context”. It’s also known as “the prompt”.
Your question, the context, is used by ChatGPT to find the most statistically probable word that would begin the answer.
Let’s say your question is “Why is the sky blue?”. First, imagine how many times that question appears on the internet and in books. ChatGPT has definitely incorporated it many times into its statistics.
“Why is the sky blue?” is a 5 word question, and forms the 5 word context. So what is the 6th word of the context going to be? It’s going to be the word most likely to appear 5 words after “why” in all the text ChatGPT has ever seen, as well as 4 words after “is” and 3 words after “the” and 2 words after “sky” and 1 word after “blue”.
(The question mark is also important, but we’re ignoring that for this simple explanation)
That word, the most probable word to fit all those conditions at the same time might be “The”. It’s a common way to start an answer. Now our context has 6 words:
“Why is the sky blue? The”
And the process is repeated:
“Why is the sky blue? The sky”
“Why is the sky blue? The sky is”
“Why is the sky blue? The sky is blue”
“Why is the sky blue? The sky is blue because”
The context grows one word at a time until the answer is completed. ChatGPT has learned what a complete answer looks like from all the text it has been fed (plus some extra training provided by OpenAI).
You may have heard about “prompts” and “prompt engineering”. Because every word in the context has an effect on finding the next most probable word for the answer, every word you include will act to constrain or shape the possibilities for the next word. For example, here is a prompt and answer from ChatGPT:
Add a few related words and the answer shifts in a predictable way:
This, in a nutshell, is what prompt engineering is about. You are trying to choose the best words to use to constrain ChatGPT’s output to the type of content you are interested in. Take a common prompt like this:
“Imagine you are an expert copywriter and digital marketer with extensive experience in crafting engaging and persuasive ad copy for Facebook ads. Your goal is to create captivating ad copy for promoting a specific product or service”
Don’t be fooled into thinking that there is some kind of software brain on a server in a giant data centre in the Pacific Northwest imagining it is an expert copywriter. Instead, think of all the websites run by copywriters and digital marketers and their blog articles where they discuss Facebook ads or writing engaging copy.
It is without doubt amazing that ChatGPT does what it does. It is also amazing that the process is so deceptively simple – looking at which words come before other words. But it takes nearly half a trillion words of human-to-human communication to provide the data to make it happen.
This is a simplification and leaves out important details, but what you need to know is that generative AIs like ChatGPT, Bard and Bing are always only choosing the next most likely word to add to a reply. It’s a mechanical process prone to producing false information. Added to this unavoidable feature of blind, probabilistic output, to make generative AI output more “creative” or “interesting” actual randomness is added to their choices.
Generative AIs have been trained on enough logic and reasoning examples to mimic how we use language to communicate logic and reason. But they are, in the end, text production programs. There is no logic or reasoning as humans use it involved in producing that text. Even if the text contains a logical argument. So always review carefully what text they produce for you.
As one person who spends a lot of time producing text said:
Having said all that, there is an argument that generative AIs, in particular LLMs, might be doing more than just producing text. This argument says they might be building a model of the world and the features of the world as they build their billions of statistics about text. And that these models might be what is making generative AIs so powerful.
Giving some partial support for this argument is the number of “emergent” abilities generative AI is showing. The backers of this argument say these are proof there is more going on than picking which word should come next. The emergent abilities are quite specialised. Such as naming geometric shapes. It’s not like it is teaching itself how to pilot a plane. You can find a list of the emergent abilities documented so far in this article. Be warned, they aren’t very impressive.
For these next sections we’re going to mostly refer to ChatGPT, but it applies to any publicly available generative AI including Google Bard and Microsoft Bing.
ChatGPT has ingested more pro forma correspondence, business documentation, business books, corporate communications, RFPs, agreements, contracts, pitch decks, etc, than you can possibly imagine.
This makes it the ultimate tool for producing the first draft of just about any document, including replies to emails, white papers, case studies, grant proposals, RFPs, etc. It can also serve as an editor, helping to turn your rambling sketch of an email or an article introduction or anything into clear, coherent sentences and paragraphs you can further revise.
With ChatGPT you never have to delay writing an email or starting a document because you don’t know where to begin. Or because you’re completely out of your comfort zone, in over your head, or have no idea what you’re supposed to say. ChatGPT has seen it all and can help you write whatever you need to write.
Now, this does come at a slight penalty, which for most things will not matter now, and in the long run will probably be welcome: ChatGPT has a distinctive “voice” that if you’re familiar with it will be noticeable.
Also, because ChatGPT is always choosing the most probable word each and every time, what it outputs can be quite boring or cliché. Sometimes this is a good thing. Clear communication is based on convention. But don’t expect creativity or original ideas.
ChatGPT is perfect for streamlining your production of all those necessary business communications that humans use to keep things running. By adopting ChatGPT as part of your process, you will be able to execute on these faster and at a higher level, leaving you more time for the work that really moves the needle.
If you’re not sure how to command ChatGPT to produce what you need, this prompt engineering guide will help.
Of course everyone else you interact with will be doing the same thing. So expect the speed of business to increase and hope all your third parties prompt ChatGPT to keep their emails brief.
As Sam Altman, the CEO of OpenAI tweeted:
In this section we are going to focus on data and text related tools. The AI-generated image and video space is also huge: Dall-e, Midjourney, Adobe Firefly, Stable Diffusion, etc. There are hundreds of them, but for most businesses image and video is a part of marketing rather than their core product, so we’re sticking with the most common use cases.
There are already lots of startups offering AI-powered tools of every variety. But they’re about to face the twin behemoths of Google WorkSpace and Microsoft Teams. Both have recently announced the integration of AI assistants into their offerings (Google’s, Microsoft’s).
How will these work? Imagine ChatGPT knows everything about your business. It has memorised every report, every spreadsheet, every presentation, and every email. You can ask it for numbers or summaries or ask it to create presentations or documents.
Some of these features will help reduce the time you spend on the boring necessities of keeping your business running. Others, like integration with spreadsheets, will help you find answers, create forecasts and analyse trends faster and easier than you could before. It’s even possible you can’t even do regular forecasts because your team has no-one with the expertise. That’s going to change.
Again, this is going to give you more time to spend on doing the things that really make a difference to your business – planning, strategy, talking to customers, building relationships with partners. Unless you make the mistake of burying yourself under all the reports it will be so easy to create. But you can always ask the system to summarise them for you.
At the time of writing there isn’t even a beta program for Microsoft and Google’s AI-powered offerings. They haven’t provided a rough date when they will be generally available. On the other hand, lots of startups are developing services based on OpenAI’s APIs, using the same LLM behind ChatGPT to create new products.
The site Super Tools has a database ]AI-based startups. You might be able to find some products in there that can help you.
If we continue to focus on text (Super Tools includes image, audio and video tools as well) these products fall into two main categories of functionality: content generation and search.
Content generation covers things like chatbots, writing assistants and coding assistants. Some of these services are nothing more than a website that adds a detailed prompt (or context) to be sent along with your own instructions/queries to ChatGPT’s backend and the response is then passed back to you.
An example of this is VenturusAI. At least it’s free. Think of these services as a lightly tailored version of the standard ChatGPT experience already provided by OpenAI. This might be obscured by design or presentation. A few hours fiddling with a prompt in OpenAI’s ChatGPT interface might get you the same result without the cost of another SAAS subscription.
If the output is short enough, and like VenturusAI they’re nice enough to show you example results, you can just paste their examples into ChatGPT and ask it to duplicate the result but for your own inputs.
Content generation is already impacting programming, legal services, not to mention copywriting of all kinds, including real estate and catalogue listings.
The impact of content generation tools is already being felt. According to Microsoft, for projects using their Github Copilot code generation assistant, 40% of the code in those projects is now AI generated. Given that code probably took a fifth or a tenth of the time it would take a normal programmer to write it, the productivity increase is enormous.
Search is just what it sounds like, but imagine a search engine that’s smarter than Google and can respond to your search request with exactly the information you need written in a way that’s easy to understand.
Dedicated search tools are springing up based on OpenAI’s APIs. Some target specific use cases, like Elicit for searching scientific papers, others, like Libraria, are more general – upload any documents you want and it’ll index them and give you a “virtual assistant” to use as a chat interface to query them.
There is no reason you can’t use OpenAI’s APIs yourself. They offer methods to fine-tune a model and to create embeddings. You’ll need a programmer to do this. Or, if you’re feeling brave and/or patient, you can ask ChatGPT to help you build a solution.
Fine-tuning uses hundreds of prompt-response pairs (which you supply) in order to train an LLM to do things like answer chat queries based on information you care about. For example, you may get the question-response pairs from transcriptions of customer service enquiries. You upload them to OpenAI. It uses them to create a new, specialised version of one of their base models that is stored and runs on their servers. Once it is built you use the API to pass chat-based enquiries to your dedicated model and get responses back in return.
If you’re clever, some of these responses contain a message signalling that a human is needed to deal with the enquiry and your chat system can make that happen.
Embeddings are used in querying across large amounts of text. Imagine asking normal language questions about information hidden in your company’s folder of reports and getting back answers.
For example, if you have hundreds of PDFs with details about the different models of widget you produce, you can create embeddings for all the documents, and then you will be able to ask things like “Which widget is best for sub-zero environments?” or “Which widget is green and 2 metres long?”. Even better, your customers will be able to ask those questions themselves.
These next few paragraphs give a basic information on embeddings and how they are used. You might want to skip it the first time through.
Embeddings are basically a list of numbers. An embedding can be thought of as an address in “idea space”. Instead of having 4 dimensions like the space we live in, this “idea space” has over 1500. Every word or chunk of text can have a unique embedding generated for it by the LLM. Texts that are conceptually close together will have embeddings that are close together (based on a distance algorithm that works for 1500 dimensions).
For example, the embeddings for “apple” and “orange” will be close together because they are both fruit. But the embeddings for “mandarin” and “orange” will be even closer together because they are both citrus fruits.
Surprisingly, this also works when you get the embeddings for hundreds of words of text.
Once you have embeddings for every chunk in every document you want to search stored in a database that can do those multi-dimensional distance calculations, like pinecone, or FAISS, you’re ready to do the actual search.
This is a bit clever. To do the search, you take the user’s query, and with a selection of pieces of your documents you send it to your LLM, like ChatGPT, to generate an answer that is probably not completely correct but is close.
Then you get the embedding for that answer and use that to search your database for the chunks whose embeddings are closest in distance to it. You can then either present the pieces of document to your user, or send the question and those chunks (limited to the size of the allowed context), to ChatGPT for it to generate a properly structured answer.
If you’re more technically minded, you can connect ChatGPT to any number of tools using a library called Langchain. It cleverly uses prompts to direct ChatGPT to output calls to external services, like calculators, databases, web searches, etc, and Langchain handles call the service, collecting the results and then adding them to the current conversation with ChatGPT.
This pre-dates and is similar to OpenAI’s plugins, but it’s more versatile and can be tailored to your specific needs.
Using LLMs to create autonomous agents has grabbed a lot of recent attention. By integrating a generative AI like ChatGPT into a system that can do things like search the web, run commands in a terminal, post to social media and other actions that can be driven by software, you can build a system that can make simple plans and execute them. Kind of.
These systems, like AutoGPT and BabyAGI work by using a special prompt (that you can see an example of here) that tells ChatGPT what it is to do and includes a list of external commands it can call. It also tells it to only output information in JSON format instead of human readable text – so it can be easily read by other programs.
AutoGPT feeds the prompt to ChatGPT and collects its response in JSON format. It executes any commands it finds in the response and incorporates the results from those to create the next prompt to feed to ChatGPT.
It uses ChatGPT’s context, which is about 3000 words, to create a short term memory for ChatGPT that can hold the goal you’ve given it, the remaining steps in its “plan” and any intermediate results it needs. This makes it somewhat capable of devising and executing short plans. We use the word “somewhat” because it needs constant monitoring and errors can cause it to go off track.
The short term memory can be extended using embeddings as we discussed above. And there is an active community working hard to make the LLM agents more robust and more capable. But for now, you may be able to use an agent to automate a simple workflow. It is particularly good at compiling and summarising information from the internet. Just be sure to do lots of testing and don’t make it an essential part of your infrastructure.
Because the OpenAI API returns JSON for your requests, you can access it from all the best no-code/low code app builders which support third party APIs.
If you don’t need apps but just want to integrate generative AI into your workflows, Zapier and Make now support OpenAI in their integrations. You can use it to automatically draft emails or generate customer service tickets based on incoming emails. Anywhere in your workflows where a human has been needed to make a basic planning or routing decision is a candidate for being automated now.
Using the same tools and techniques you would use to build inhouse AI tools you can build a product.
But that’s just the first level. Beyond leveraging OpenAI’s APIs you can use services like Cerebras to build and train your own model.
Training your own model might be out of reach, but fine-tuning an existing model might be all you need. Fine-tuning a model for a specific domain already has a name – “models as a service”. These models can help users in a particular domain do everything from fix their spelling to estimate the cost of repairs to design new molecules. Of course your use case will dictate the model you fine-tune which will impact your costs.
The power of having your own fine-tuned model that users interact with is that once you have users you now have a source for even more training data, creating an ongoing cycle of fine-tuning and model performance improvement that can build a moat for you in your market.
We hope this article has given you the understanding, inspiration and links that you need to get started using generative AI in your business.
Start small, use ChatGPT to draft a few emails (but double check them). Browse Super Tools and see if there are any tools on there that might address one of your workflow or business process pain points.
Small steps and strategic integration of generative AI is key to taking advantage of this huge technological leap forward. Start today and see how fast and far it can take you.The best no code / low code tools and strategies for building your MVP
This is a quick rundown to help you choose the best no code/low code tool to start building your MVP and the best strategy to follow.
To keep it simple we’re just going to call them no code tools. No code tools can be divided into 3 main categories – workflow automation for business/enterprise, website builders, and app builders. We’re going to focus on app builders.
After running through hundreds of pages of documentation and community forum posts as well as days worth of tutorials, these are the app builders we would recommend:
None are perfect. They all have their own unique pain points. But they all follow a similar model for structuring an app. If the functionality that your MVP requires can fit within that structure you will have little trouble producing a working prototype in weeks instead of months.
Here is the main reason why no code tools are best used for building prototypes and MVPs and why you will eventually turn to or integrate custom code as your app matures:
No code tools focus on the lowest common functionality, abbreviated as CRUD.
CRUD stands for “Create, Read, Update, Delete”, which are the four operations you can perform on a database, which is where your app is going to store any data.
How this looks in an app like a marketplace:
No code tools are further limited in that the interfaces you can build with them tend to be restricted to displaying: 1) static pages, 2) a list of items pulled from a database (like a list of homes for sale in the suburb of Erinsborough), and 3) a detailed view of a single item pulled from a database (like that home on 24 Ramsay Street).
They’re basic standard views. Exactly what you would use in a prototype to make sure you’re pulling the data you expect. While they can be designed and tailored for your app and brand, your UX is still being decided for you.
If you find this too limiting you will need to look towards more advanced no code tools that allow you to also do low coding (or even full coding) like FlutterFlow or Draftbit. But should you code portions of your app from scratch? Do you have the inhouse skills? Can you hire someone who is familiar with your tool of choice?
Obviously, for an MVP, the decision to expand from no code to low code will delay launch and should be considered carefully. Once you start coding your options become unlimited, but so does the time it will take to finish your app unless you have access to the talent to write the code and the discipline to manage your feature list.
If you want to validate your product market fit as quickly as possible, then you can build your MVP using a no code tool with the intention to throw it away and rebuild later from scratch when you have found product market fit.
This gives you the speed of a no code tool. That speed makes it easier to avoid the sunk cost fallacy so you feel okay throwing it away and starting again from scratch using proper tools and a proper framework.
“Throwing it away” isn’t literal. You’ll keep it running while you build the next version.
If you already have users and a market and want to provide an app, then you want to use a tool that allows you to continue to grow your app’s functionality beyond what the drag and drop interface provides. You want to build to keep.
If you want to build to keep make sure you choose a tool that gives you the features you need to implement the complete product you envision. No code tools are simple because they are often limited.
To avoid these limitations you will want to use a no code tool that lets you export the code for your app. See the end of the article to see which tools allow this.
Success depends on finishing your app and getting it into the marketplace. Your best chance for success depends on matching the no code tool to your technical expertise. Here is what we would recommend for different levels of programming ability. Choose the one that best describes you:
Of course there’s nothing stopping you from using a less complicated no code tool. It might give you a speed advantage.
Like all options in a crowded marketplace, every choice has its trade offs. This table summarises what we think are the main factors that should guide your decision on choosing a no code tool.
No code tools continue to add features (like integrating third party APIs) as they compete with each other. What can be built with no code tools today couldn’t be built a year ago.
But don’t let possibility fool you into making your MVP more complex than it needs to be to launch. If you can work within the standard set of CRUD actions combined with list and detail views (which 9X% of apps can) then you will be able to get your MVP to launch with any one of the tools we discuss.
As your experience with no code tools and your understanding of your app grows, you might find it worthwhile to move up to the more powerful no code tools on the next iteration. Combined with a no code backend, you may be able to delay the transition to bespoke code until revenues are high enough to make cutting platform costs worth the investment.
Whatever path you take to build your MVP with no code tools, it’s sure to be shorter than the alternatives.Freelancers vs team extension – why freelancers always lose
IF you’re an SMB that needs to step up technically, either by building an app, creating a website or even just updating your online presence, this article might save you from freelancer frustration.
As an SMB you’re always at a disadvantage when it comes to tech. You can’t afford to have the expertise you need in house, and hiring freelancers is an exercise in frustration. How are you to stand out in your market, or even compete in it, when tech is now a major factor in success?
We’re going to fill you in on the advantages of a team extension versus hiring freelancers. And you’re going to be glad you’ve learned about it.
A team extension is basically remote workers as a service. A team extension can be a single software developer working a few hours a week to a full stack dev team numbering in the dozens.
What differentiates a team extension from outsourcing is that the members of your team extension report directly to you and they are integrated with your in house team, tools and systems. They attend meetings (virtually), they work directly on your projects, and, if they are full time, they work only on your project.
One of the biggest benefits of the team extension is that you have the full support of your team extension service provider. They want you to succeed with their team members as much as you do.
Hiring freelancers requires you to become an expert in IT recruitment. Choosing the wrong freelance software developer can sabotage your plans.
When hiring for a team extension you are hiring out of a proven talent pool with a track record of helping businesses like yours build apps and websites and keep their tech infrastructure running.
With hundreds of completed projects under their belts, your service provider knows the skill set you need and can help you estimate the hours or headcount as well. They are also able to put forward proven candidates for you to interview and pick from.
And unlike with a freelancer, if someone doesn’t work out you don’t lose time or code. You can work with your service provider to rapidly replace them and keep moving forward.
This is a subtle but important difference. A team extension really is an extension of your team. They’re like employees working out of a different office. They’re output is your output. At all times you have complete visibility into their progress and the work they’ve completed.
Freelance software developers, for good reasons, tend to deny access to any code they’ve written for you until they have been paid in full. There might be online demos they’ve built for you to comment on, but the code remains untouchable.
This gives your project a “bus factor” of 1. Which is how many people need to be hit by a bus to derail your project. Or how many people need to get sick or get sick of the project, to derail it.
With a team extension you have an entire organisation supporting you and your team members. All the code being written is yours and is always accessible. You don’t need to worry about “bus factor”.
Another major benefit of a team extension is that you get the increase in headcount and productivity without the increase in office space, resources and HR burden.
Your team extension members work out of the service provider’s offices. They supply the workspace and the equipment. They also provide all of the HR and employee support services required. This includes all kinds of work management, training and personal support.
The situation for you is not unlike becoming a team leader where upper management is devoted to helping you get the results you need. You have a second level of oversight to help you successfully negotiate all the challenges of the increased headcount.
If you’ve been unable to move forward on the app or website that your business needs because of the challenges in hiring developers, we hope this article has opened some doors for you.
You can build the app or website you need. You can do it while minimising the risks by adopting the team extension model.
If you want to discuss extending your team with some of our talented developers don’t hesitate to get in contact with us.Building your app with an extended team
We work with you to sketch out the development strategy for your app, web app or website.
You start with a shortlist of talent that you can be sure have the skills and experience you need. You meet one-on-one for a video interview so you can get to know the candidates.
Wireframes and business rules are finalised. Feedback during the process ensures balance between budget, features and viability. Full project estimates are in place. You know exactly what needs to be built.
Weekly sessions with your SoftwareSeni product consultant and project manager keep the project advancing smoothly.
You get daily updates as well as weekly and monthly reconciliation reports so you always know how the project is tracking financially and towards your deadline.
This includes payments, CRM, ERP, customer support, reporting, shipping, and more.
Web servers, database servers, load balancers, CDNs – all the technical services you need are set up, tested, and placed under monitoring.
Another round of full QA is conducted to ensure there are no incompatibility issues in the live environment.
The first version of your product goes live. Your marketing campaign goes into action. You start signing up users.
The first version of your product goes live. Your marketing campaign goes into action. You start signing up users.
Need 24/7 uptime? Our team can monitor and respond to any incidents as they occur. Traffic to your product grows and it begins to generate revenue for your business.
Your smaller team continues to add features at a pace that matches your budget and incoming revenue.
Release your extended development team, but retain a part-time developer familiar with your product to handle dependency updates, API changes, service provider issues, etc.
Just like your extended development team, use a team extension for customer service to handle the increased enquiries and interest following your successful product launch