Solution Market Fit - Building a Product That Delivers
The Awkward Truth About Early Sales
You have validated the market through conversations. You have aligned on pricing and seen clear intent from prospects. Now you face the next challenge: building something that delivers on what you sold.
This is where many startups struggle. There is often a gap between the promise made during sales and the reality of what can be delivered.
Solution Market Fit is the stage where that gap closes. It is when your product consistently delivers the outcomes you committed to, customers continue to renew, and over time, they begin to recommend you to others.
It is not about building a perfect product. It is about delivering reliable value, consistently.
The Gap Between Sales and Product
In early sales conversations, you are often selling a vision of what your product could become, not what it fully is today. You describe outcomes such as time saved, efficiency improved, or revenue increased. The prospect connects with that vision, sees the potential value, and decides to move forward.
But once the agreement is made, execution begins.
The challenge is that building software is complex, and AI-native products introduce an additional layer of unpredictability. Outputs are not always consistent, integrations take longer than expected, and customer workflows often reveal edge cases you did not anticipate. What seemed straightforward during sales becomes significantly more nuanced in practice.
This creates a gap between expectation and delivery.
Solution Market Fit exists to close that gap. It is the process of continuously refining the product based on real customer usage, feedback, and constraints, so that over time, what you deliver aligns closely with what you initially promised.
The Services Trap
One of the most difficult parts of delivering a solution is understanding the boundary between product and service. In the early stages, your product will not cover every use case, and you may need to provide additional support or custom work to deliver the expected outcome.
This is normal, but it requires discipline.
You need to identify which parts of that effort are repeatable across customers and which are truly one-off. The repeatable elements signal opportunities to build product features or even expand into adjacent products.
At the same time, you need to avoid going too far into custom work that is specific to a single customer. That path turns your company into a service business rather than a scalable product company.
The goal is to use early services as a way to learn, standardise, and convert common requirements into product capabilities, while staying focused on building something that can scale beyond individual customer needs.
Building Solution Market Fit
Here’s the process:
1. Deliver the core promise
Stay focused on the outcome you committed to during sales. This becomes your North Star Metric. If the promise was time savings or efficiency, everything you build should reinforce that. Remove features that do not directly contribute to delivering that outcome.
2. Measure actual outcomes
Do not rely on assumptions. Track real impact for every customer. Measure what matters, whether it is time saved, cost reduced, or revenue improved. Use this data consistently across customers to validate success and to strengthen future sales conversations.
3. Get feedback from users
Engage regularly with the people using the product, not just the decision-makers. Understand what is working, what is breaking, and what is missing. These insights will guide where you need to improve.
4. Iterate with focus
Prioritise changes that directly improve the core outcome. Fix issues that create friction, add features that strengthen value delivery, and remove unnecessary complexity that slows users down.
5. Establish retention and expansion
The clearest signal of Solution Market Fit is when customers continue to renew and expand usage over time. If customers increase usage or move to higher pricing tiers, value is being delivered. If usage declines or customers churn, it indicates gaps in the product experience that need to be addressed.
Key Metrics for Solution Market Fit
Track these to measure Solution Market Fit:
The Notchup Path to Solution Market Fit
Our first customers at Notchup came in with high expectations. We had positioned the product as a talent intelligence platform that would transform how recruiting worked.
The reality was more complex. The matching algorithm was still improving, integrations were not seamless, and several real-world use cases were not fully supported.
Instead of trying to solve everything at once, we narrowed our focus to the core promise: helping teams find quality candidates faster. That became the single metric we optimised for.
We collected continuous feedback around that specific outcome. We improved the accuracy of matching, simplified the experience for the most common workflows, and added only the integrations that were essential to unblock adoption.
Over time, the shift became visible. Retention improved. Conversations changed. Customers moved from seeing the product as interesting but incomplete to recognising it as something that was actively improving how they recruited.
That was the point where we knew we had reached Solution Market Fit.
Solution Market Fit for AI Products
AI products face a distinct set of challenges on the path to Solution Market Fit, and each of these needs to be managed deliberately:
1. Cold start
AI-native products often require access to proprietary enterprise data and some level of model tuning before they deliver meaningful value. This can introduce delays, dependencies on external systems, and friction during onboarding. It is important to set clear expectations with customers, document these constraints, and communicate progress consistently.
2. Model performance variability
AI systems do not always behave consistently. A solution might perform well most of the time but still fail in certain scenarios. Achieving Solution Market Fit requires reducing this variability and ensuring that the product delivers reliable outcomes across common use cases.
3. Hallucinations and edge cases
Customers will inevitably encounter situations where the AI produces incorrect or unexpected outputs. These edge cases highlight gaps in understanding and reliability. Continuous monitoring, testing, and refinement are required to improve accuracy and build trust over time.
4. Human-in-the-loop design
Full automation is not always the optimal approach. In many cases, the most effective solution combines AI with human oversight. Designing systems where users can review, guide, or correct outputs helps maintain quality and increases confidence in the product.
5. Continuous learning
Strong AI products improve with usage. This requires systems that can learn from customer data while maintaining privacy and security. Building mechanisms for safe data collection and model improvement is essential to delivering increasing value over time.
When You Don’t Have Solution Market Fit
Red flags:
- Customers churn soon after implementation or within the first few months. This indicates the product is not delivering enough ongoing value
- Usage drops significantly after the initial onboarding phase. Early interest is not translating into sustained engagement
- Support effort becomes excessive. Each customer requires heavy custom work to realise value, which limits scalability
- The product consistently falls short of the outcomes that were promised during sales
If you see these patterns, pause before scaling further. Go back to your customers and understand what is not working in practice. You may have validated demand or pricing, but not the solution itself. Closing that gap is essential before moving forward.
What Comes Next
Once you have Solution Market Fit, when your product consistently delivers the outcomes you promised and customers not only stay but expand their usage, you are ready to move into the final stage: Scaling Market Fit.
At this stage, the focus shifts from proving value to multiplying it. It is about growing from a handful of customers to hundreds or thousands while maintaining the same level of product quality, customer satisfaction, and unit economics that helped you get here.
In Part 7, we will explore how to scale in a way that preserves the fundamentals, so growth does not come at the cost of experience or sustainability.
Key Takeaways
- Solution Market Fit is about consistently delivering the outcomes you promised, not just building features
- Focus your roadmap on the core promise. Go deep on what matters instead of spreading effort across too many areas
- Measure real outcomes for customers, not just product activity or feature releases
- Strong retention and expansion revenue are clear signals that customers are seeing ongoing value
- For AI products, reliability of outputs and thoughtful human-in-the-loop design are essential to maintain trust and usability
Are your customers consistently achieving the outcomes you committed to? That is the clearest signal of Solution Market Fit
