Custom AI Product Development & Consulting
We build custom AI products for startups and established businesses — from AI strategy and AI MVP development to a production system you can trust. Every build includes LLM evaluation and observability, so you can measure answer quality, speed and cost before launch and keep watching them after real users arrive.
What We Usually See
Many businesses know AI could help but aren't sure where to start, which approach fits their problem, or how to go from a promising prototype to something reliable enough to put in front of real customers.
How We Take AI Products from Idea to Production
We work with you from strategy through production: identifying the highest-value AI opportunities, building an MVP fast, then hardening it with proper evaluation, observability and monitoring before it scales.
What's Included
AI opportunity assessment and strategy workshops
Rapid MVP development (weeks, not months)
LLM evaluation frameworks to measure quality before launch
Production observability — track cost, latency and output quality live
Ongoing support as your product and usage scale
Where This Fits in a Real Business
Validating an AI product idea before a larger investment
Taking an internal prototype to a production-ready product
Building evaluation pipelines for an existing AI feature
Setting up observability for LLM costs and output quality
What You Walk Away With
A working MVP or production system, an evaluation and observability setup, technical documentation, and a roadmap for what's next.
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Most AI MVPs take 6–10 weeks from kick-off to a version real users can try. A narrow proof of concept to test feasibility can often be shown in about two weeks.
Evaluation means testing your AI feature against a set of real examples and scoring the results, so you know its quality before launch. Observability means tracking every live request — answer quality, latency and cost — so issues are caught early.
We work with Claude, OpenAI and Gemini models as well as open-source models, and choose per feature based on quality, cost, speed and where your data is allowed to go.
Yes. We review the code, add evaluations and monitoring, fix reliability gaps and then extend it — often faster than starting again.
Ownership of code, models and data is agreed in writing before work starts, so there are no surprises. We discuss this openly during scoping.