Building the wrong AI system just got a lot faster.
Your agents can build almost anything. Can you tell which build is right?
We help you find the right one before it reaches real users.
Dr. Nishant Sinha — CMU PhD, ex-IBM Research, 20+ years building production AI systems
Spinning up a RAG or agentic search system isn't the hard part anymore — an agent can wire up an agentic search pipeline, demoable in an afternoon. What doesn't get any easier is knowing whether it actually works: whether it retrieves the right passage from your documents, handles the questions your users actually ask, and holds up once real traffic arrives. That's the gap where most of these projects quietly fail.
OffNote helps teams design, review, and de-risk real AI systems across RAG, agents, search, voice, and enterprise workflows.
The clearer your company describes what it knows, the better it searches.
Code comes and goes. What you learn from evaluating it is the only thing that compounds.
Practical notes on real AI systems: what works, what breaks, and what it costs.
Some engagements need judgment. Some need working code.
Depending on how well-defined the problem is, engagements take one of two forms.
- Diagnose the decision, not the code
- Map architecture options and trade-offs
- Identify failure modes before they get expensive
- Review cycles alongside your team
- I build the core system or the riskiest backend piece
- Working code, not just recommendations
- Pair directly with your engineers
- Hand off with docs, evals, and a roadmap