When we founded Caudals, our mission was clear: help teams build reliable artificial intelligence by providing high-quality, specialized datasets. But the road to product-market fit was far from linear. In less than a year, we had to rethink our core business model three times before discovering where real enterprise demand lives.
Here is the story of how our strategy evolved, what failed along the way, and the lessons we took from each iteration.
Crowdsourced Marketplace
We started with a B2C crowdsourcing model inspired by platforms like Scale AI and Mercor. The mechanics were straightforward: individual contributors would record, annotate, or upload the specific data samples needed for a project and get paid for their work. Companies looking for custom datasets would fund the task pools, and we would take a commission on every approved submission.
While the platform worked mechanically, we ran into significant friction. Managing a double-sided crowd required constant moderation, and the quality of open contributor data was noisy. More importantly, serious enterprise clients did not need generic crowdsourced tasks. They needed deep domain expertise, strict compliance, and rigorous data consistency that micro-task platforms simply cannot guarantee.
B2B Proprietary Data Exchange
Recognizing the limitations of open crowdsourcing, we shifted our focus entirely to B2B. Many established companies sit on vast archives of valuable, proprietary operational data that they never monetize. At the same time, AI teams building vertical applications were desperate for clean, real-world industry data.
We pivoted to act as a trusted intermediary: sourcing and acquiring reliable, compliant data directly from asset holders, cleaning and structuring it, and selling it to companies building specialized AI solutions.
This solved the data quality problem, but it introduced a different commercial roadblock. Datasets are an abstract input. Most companies do not have a dedicated budget line for speculative training data. When we spoke with engineering and product leaders, they could rarely define their exact data gap. They simply knew their deployed AI was making errors in production. Trying to sell large data contracts before an organization had even diagnosed its problem was an uphill battle.
The Evaluation Wedge
That friction led to our defining pivot: leading with AI evaluations.
Instead of trying to sell datasets upfront, we changed our entry point completely. We take a company's existing technical manuals, policy documents, and real customer queries to construct a rigorous golden test set. Then, we benchmark their live assistant, chatbot, or internal LLM pipeline against it.
We hand the team a detailed evaluation report showing exactly what failed, why it failed, and what it would take to fix it.
This shift transformed the customer relationship:
- Immediate value: The evaluation delivers tangible, empirical findings before asking for a large financial commitment.
- Technical trust: Walking an engineering team through concrete failure cases and retrieval errors establishes immediate credibility.
- A clear bridge to custom data: Once a company sees where its model breaks, the solution becomes obvious. We coordinate with vetted freelance domain experts to construct the complete, custom datasets needed to close those proven gaps.
- Future data suppliers: As we build trust and help clients understand the value of their own proprietary information, those same companies naturally become future data suppliers, licensing their structured data back through our network.
What We Learned
Pivoting early taught us three practical lessons about building an early-stage company:
- Sell the diagnosis before the cure: Companies rarely buy abstract inputs. Once you show an organization the exact cost of its errors, solving the problem becomes an urgent priority.
- Sequence builds trust: You cannot close a complex deal until you have proven your value on step one. Evaluations gave us a reliable wedge to earn that trust.
- Listen to commercial resistance: When customers hesitate, the solution is rarely to pitch harder. It usually means the entry point is wrong.
Finding product-market fit is rarely about the first idea you write on a whiteboard. It is about staying close enough to real user pain to recognize when your strategy needs to evolve.