For AI companies
From model to product. Without burning the round.
The demo works in the notebook. The product does not. Hallucinations in front of customers. Inference cost eating the unit economics. The model-to-product gap, and how to close it.
The pattern in AI teams
The model works. The eval set looks great. In production, half the queries fall outside the eval distribution and the team has no system to detect or fix it.
Inference cost is unpredictable. A single power user can spike monthly burn. Pricing is unclear because the underlying cost basis moves with every prompt change.
The product team wants to ship features. The ML team wants to improve evals. Neither knows whose decision determines what ships. Three months in, you have a demo nobody wants to put in front of an enterprise customer.
The bottleneck is not the model. It is that nobody has decided what reliability, observability, and cost guarantees the product is selling.
What we bring to AI teams
01
Decisions that close the model-to-product gap
The Clarity Sprint forces explicit choices on reliability thresholds, observability scope, prompt-management discipline, fallback behavior, and what the user sees when the model fails. Documented before any feature ships.
02
Cost-aware architecture from day one
Token budgets per user tier. Caching strategy that actually fires. Model routing between fast-and-cheap and slow-and-correct. Inference observability so you know which prompt is eating margin. We build with the cost model that lets you stay in business.
03
UX that handles model uncertainty honestly
Confidence surfaces. Editable outputs. Source citations when warranted. Disclosure UX for hallucination-prone tasks. We design the interface that earns user trust the second the model gets something wrong.
What this has looked like
Pattern 1
LLM-native SaaS, inference cost above MRR
Each user query hit the most expensive model by default. No caching. No tier-based routing. Gross margin was negative for any user above the median.
Clarity Sprint isolated three prompt classes that could be served by a cheaper model with no quality loss. Added a router and a semantic cache. Inference cost dropped meaningfully. Gross margin turned positive within one billing cycle.
Pattern 2
AI feature inside SaaS, low adoption
Feature shipped to all users. Adoption flat. Surveys said users did not trust the output.
Redesigned the surface to show source data, allow inline editing, and disclose confidence. Same model, same prompts. Adoption climbed because the UX gave users a way to verify and override. The model itself did not change; the contract with the user did.
If this sounds like your AI build
Start with a Clarity Sprint. Two weeks, fixed price. You leave with a decision document on reliability, cost, observability, and UX commitments, plus a fixed quote for the build.