I spent 6 months solving the wrong problem. Here's how I finally figured it out.

First post here. Looking for advice from founders who've been through this.

I am three months into building Deepmost AI with my co-founder, and I just realized we have been solving a problem that does not actually exist. This realization came last Tuesday during what should have been a routine customer discovery call.

The technical work we completed over the past six months is impressive from an engineering perspective. We built infrastructure for enterprise AI deployment that handles complex data pipelines, manages model versioning elegantly, and provides comprehensive monitoring capabilities. The architecture is clean. The code quality is strong. Several engineers who reviewed our technical approach expressed genuine admiration for the implementation decisions we made.

None of this matters because we built it for a problem that enterprises do not prioritize fixing right now.

The conversation that changed everything

I was speaking with a VP of Engineering at a company we thought represented our ideal customer profile. They operate at scale, have sophisticated technical teams, and publicly discuss their AI initiatives. Every signal suggested they would value what we built.

Fifteen minutes into the conversation, he interrupted my explanation of our technical capabilities with a question that stopped me completely. He asked whether we could help them figure out which AI projects to fund in the first place, because they had twelve proposals from different departments and no framework for evaluating which ones actually mattered for the business.

I explained that our product addressed deployment and operational challenges after they decided to build something. He acknowledged that those challenges existed but noted they were far less urgent than the strategic question of what to build. His company had failed three AI pilots in the past year not because deployment was difficult but because they chose initiatives without clear business value.

He thanked me for my time and said to reconnect if we ever addressed the strategic planning problem. The technical deployment infrastructure we spent six months building was solving step five of a process where his company remained stuck on step one.

How we arrived at the wrong problem

The mistake began with our backgrounds. My co-founder and I both worked at companies where AI deployment was already a priority and where substantial technical infrastructure existed. The problems we encountered daily involved making deployment more efficient, monitoring more comprehensive, and operations more reliable. These problems felt urgent and important because they consumed significant engineering effort at our previous employers.

We made the classic error of building solutions for problems we personally experienced rather than investigating whether those problems existed for our target customers. Worse, we built for companies at the maturity level of our previous employers rather than companies earlier in their AI adoption journey where most of the market opportunity actually exists.

The second error involved confusing technical sophistication with market value. We believed that companies would appreciate elegant technical solutions and would recognize the engineering quality we delivered. This assumption fails completely in enterprise sales where buyers care primarily about business outcomes rather than technical implementation details. A technically mediocre solution that addresses urgent business needs wins consistently over technically excellent solutions that address less pressing concerns.

The third error was moving too quickly into building without sufficient customer discovery. We conducted interviews with potential customers, but those conversations focused on validating our technical approach rather than deeply understanding their actual priorities and pain points. We heard what we wanted to hear rather than listening carefully to what customers were telling us about their real challenges.

The uncomfortable questions I am asking now

This realization forces several difficult questions that I do not have clear answers for. I am sharing them here because I suspect other founders face similar situations and because the Indie Hackers community has collective experience that might provide perspective.

Should we completely restart with a different problem focus, essentially throwing away six months of technical work? The infrastructure we built has genuine value for some subset of companies, but those companies may not represent a large enough market or accessible enough segment to build a viable business. Pivoting completely would mean acknowledging that six months of effort produced learning rather than product.

Can we reframe what we built to address the problems customers actually prioritize? Perhaps the technical infrastructure could be repositioned or the capabilities could be packaged differently to solve more urgent needs. However, this risks another version of the same mistake where we try to fit our solution to customer problems rather than building what customers actually need.

Should we continue pursuing the technical infrastructure problem but target different customers who are further along in AI adoption? Companies at Google or Netflix scale likely do face the deployment challenges we built for, but reaching and selling to organizations at that maturity level requires different approaches than we are prepared to execute currently.

How do we evaluate these options objectively when we have substantial emotional and time investment in our current direction? The sunk cost fallacy is obvious in theory but difficult to overcome when you have spent half a year building something you believed would matter.

What I am learning about customer discovery

The most valuable lesson from this experience involves the difference between customer interviews that validate your assumptions versus conversations that genuinely explore customer reality. I thought we were doing customer discovery properly because we spoke with potential buyers and asked about their challenges. However, our question framing and conversation approach led participants toward confirming what we already believed rather than revealing their actual priorities.

Effective customer discovery apparently requires suspending your solution entirely during initial conversations and instead investigating the customer's world without attempting to sell them anything or steer them toward your predefined problems. This approach feels counterintuitive when you have already built something and want to validate product-market fit rather than question fundamental direction.

The second lesson involves distinguishing between problems that customers acknowledge exist versus problems they actively prioritize solving. Many executives will agree that AI deployment infrastructure could be better, that monitoring could be more comprehensive, and that operations could be more efficient. This acknowledgment does not indicate willingness to invest time or money addressing these concerns right now. The urgent problems that drive purchasing decisions often differ substantially from the comprehensive list of things that could theoretically be improved.

The third lesson relates to market timing and maturity curves. Even if our technical infrastructure solves real problems, those problems may not be urgent for most potential customers currently because they have not yet reached the maturity stage where deployment infrastructure becomes their primary constraint. Building for where the market will be in two years rather than where it exists today creates the challenge of either surviving until the market catches up or finding the small segment of early adopters who already face those future problems.

Specific help I need from this community

I am hoping to learn from founders who have encountered similar situations where their initial product direction missed the market need. Several specific questions would benefit from community perspective.

How do you distinguish between needing a minor pivot versus requiring complete restart with a different problem? I am struggling to evaluate this objectively because I want to believe the work we completed has value rather than accept that we may need to start over substantially.

What approaches have worked for you in conducting customer discovery that actually uncovers priorities rather than validates assumptions? I clearly need to improve my customer research approach but most resources I have found provide generic advice rather than specific tactical techniques.

Have other technical founders successfully made the transition from building what they find technically interesting to building what customers actually need? The psychological shift involved in this transition feels more difficult than I anticipated.

For founders who pivoted significantly from their initial direction, how did you maintain team morale and personal motivation through the restart process? The emotional challenge of acknowledging six months of misdirection while maintaining confidence that you can identify the right direction moving forward feels substantial.

Why I am sharing this publicly

I am posting this on Indie Hackers despite the embarrassment of admitting fundamental strategic errors because I believe this community values transparency about failures alongside celebration of successes. The polished narratives where founders describe linear progress from idea to product-market fit rarely reflect actual experience. Most successful companies apparently navigate multiple failed directions before finding approaches that work.

I am also sharing because I suspect many founders face similar situations where their technical capabilities and personal interests lead them toward problems that do not represent viable markets. Creating space for honest discussion about these challenges might help others avoid similar mistakes or provide encouragement to founders currently working through their own misdirection.

Additionally, I hope that sharing this struggle publicly creates accountability that prevents me from continuing down the current path simply because changing direction feels difficult. Sometimes external perspectives provide clarity that internal analysis cannot achieve when you are deeply invested in a particular approach.

What happens next

I am conducting twenty customer discovery conversations over the next four weeks using a completely different approach that prioritizes understanding customer priorities before introducing any solutions. These conversations will focus on enterprises that are considering AI initiatives but have not yet deployed significant capabilities, as this segment appears to face more urgent challenges than the deployment infrastructure problems we initially targeted.

Based on what I learn from these conversations, I will decide whether to pivot our technical infrastructure toward different customer segments, rebuild completely to address different problems, or potentially conclude that we should pursue different opportunities entirely. I am attempting to remain genuinely open to any of these outcomes rather than anchoring on my preference to preserve the work we have completed.

I will share updates about what I learn from this customer discovery process and what decisions we make about product direction. I hope these updates provide value to other founders navigating similar uncertainty while giving me additional motivation to execute this exploration thoroughly rather than superficially.

Questions for you

Have you ever realized you were solving the wrong problem after significant investment in a direction? How did you recover from that realization and what did you learn about avoiding similar mistakes in future ventures?

What specific questions or conversation approaches have you found most effective for understanding customer priorities versus validating your predetermined assumptions? I am building a new customer discovery framework and would value practical tactical advice from founders who have done this successfully.

For technical founders specifically, how do you balance the natural tendency to build technically interesting solutions with the discipline required to focus exclusively on customer value even when that means building less sophisticated products?

I appreciate any perspectives this community can share. This is my first post on Indie Hackers but I have been reading founder stories here for the past year and have learned substantially from the transparent discussions about both successes and failures.

Sherin Joseph Roy
Co-founder, Head of Products @ Deepmost AI
Building in the enterprise AI space, currently figuring out what that actually means