You already know AI can help your business. The hard part is knowing what to build, why it matters, and how to do it without burning months and budget on the wrong thing. That's where I come in.
Almost every team I talk to already believes in AI. They're stuck on the next question: what's actually worth building, and how do you get there without an expensive mistake? The hype makes that harder, not easier.
These are real situations I keep running into. Different companies, same root problem.
A healthcare startup spends months and millions building an internal AI tool. It still doesn't work, because the hard parts (clean data, evaluation, the real workflow) got skipped for a demo that looked good in a meeting.
How it should go: a small, evaluated prototype that proves the workflow in weeks, before anyone spends millions.
At a mental-health company, leadership won't approve new AI tools or models, even when someone inside has mapped out exactly where they'd save real money. Caution wins by default, and the savings never happen.
How it should go: a low-risk pilot with real guardrails that earns leadership's trust, then unlocks the savings.
A CPG founder wants agentic AI handling customer support, but isn't sure what an “agent” even is or how you'd set one up. That's a completely fair question, and most consultants make it worse, not clearer.
How it should go: a plain-English walkthrough, then one support flow handled by a real, scoped agent.
The gap is always the same: knowing AI matters is easy. Knowing what to actually do about it is the part nobody hands you.
I help teams cross that gap. I dig into how your business actually works, separate the high-ROI use cases from the hype, and tell you plainly what's worth doing and what isn't.
Then I build it, with the evaluation, guardrails, and honesty that keep it from becoming another expensive miss. You get working software your team owns and understands. Not a 50-page deck.
Hands-on, from first idea to production. Engage me for one workstream or as your embedded AI engineer.
Grounded answers over your actual data, with the evals to know it's right instead of hoping.
Multi-step agents wired into your stack, with the guardrails and audit trails that matter once one is touching real systems or money.
The high-ROI internal work: triage, drafting, research, back-office flows. Measured in hours saved per week.
Get a promising prototype to reliable: evals, cost and latency control, observability, versioning.
Which model, when to prompt vs. fine-tune, decided by testing on your data, not vendor marketing.
Guardrails, PII handling, and the supervision that lets cautious teams say yes with confidence.
Who you're hiring
I'm Quinn Osha, a 3x founder who builds AI for a living, not for slides. I built DebuggAI and autonomous agents that take real action safely, in production.
More about who you're hiringField notes from actually building this stuff.
A production guide to semantic caching for RAG: answer vs retrieval caching, ACL isolation, grounding revalidation, freshness invalidation, hit risk scoring, and observability to cut cost and latency safely.
A practical guide to treating prompt optimization as an engineering discipline: experiments, versioning, routing, evals, regression gates, and observability before fine-tuning.
A practitioner’s guide to query rewriting in production RAG: when to rewrite, expand, or decompose queries; how to preserve intent and permissions; and how to evaluate cost, latency, and failure modes.
Straight answers to what these services are, how they differ from general AI consulting, and how to get real value from them.
That's the conversation. Tell me what you're working on and I'll tell you straight what's realistic, what it costs, and whether it's even worth doing.
Book a working session