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

Dr. Nishant Sinha, founder of OffNote Consulting Labs
What keeps going wrong

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.

Your RAG prototype doesn't survive production.
No evaluation loop, brittle retrieval, answers that looked fine in the demo.
Agent complexity got added before reliability.
More agents, more prompts, no clearer path to a dependable system.
Your team locked into a vendor too early.
Wrong abstraction, expensive to unwind, no outside read before committing.
Your agent can generate ten architectures overnight.
Nobody catches the one that fails in production. Generation got fast. Verification didn't.
What we help with

OffNote helps teams design, review, and de-risk real AI systems across RAG, agents, search, voice, and enterprise workflows.

AI System Review
A fixed-scope diagnosis of your current or planned architecture, risks, and next steps.
RAG / Agent Risk Review
For teams with an existing prototype or production system. Failure modes, missing evals, complexity traps.
Build Advisory / Fractional AI Architect
Longer engagement. Architecture, roadmap, implementation guidance, review cycles during build.
Search is only as good as how well it's organized.
The clearer your company describes what it knows, the better it searches.
What's shipped

Code comes and goes. What you learn from evaluating it is the only thing that compounds.

01
20%
Latency reduction
S&P 500 e-commerce · agentic RAG
02
2X
Faster ingestion
Same engagement, better schema
03
20%
Less manual review
Legal RAG · Court judgments, Telecom policies
04
Weeks
Full search implementation
Agentic, spec-driven build — not months of engineering

Workshops

Short live sessions on harness design, search with agents, and low-latency voice applications.

Writings

Practical notes on real AI systems: what works, what breaks, and what it costs.

What clients say

Nishant's contributions were crucial in laying the foundation for our AI search and recommendation system. He was not only a strategic thinker but also a professional who added significant value to our project. His thoughtful approach and commitment to excellence greatly enhanced our team's capabilities and drove us towards innovative solutions.

Arasan, Vinovoss

Nishant is incredible to work with. He is a fount of machine learning wisdom and knowhow. His involvement with our project was instrumental in taking it to new heights.

Edo, TouchCast

Nishant consistently demonstrated a deep understanding of machine learning and engineering principles, which was evident in the insightful and challenging questions he posed during our meetings.

Ana, BetterAI
How I work

Some engagements need judgment. Some need working code.

Depending on how well-defined the problem is, engagements take one of two forms.

As advisor
  • Diagnose the decision, not the code
  • Map architecture options and trade-offs
  • Identify failure modes before they get expensive
  • Review cycles alongside your team
Best when the problem is scoped and you need a second opinion.
As co-builder
  • 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
Best when the problem is ambiguous and no one owns the risky part yet.
Built with AI-native, spec-driven workflows.
Your AI architecture decisions are solvable.