Most AI pilots never make it to production. Ours ship.
For companies that are done with demos: Sachin Garg sets the direction — build-vs-buy, on-prem-vs-API, what AI honestly won't fix — and Rawzor associates build the pipelines, models, and production hardening. Strategy and delivery in one practice.
A small, honest first step: a clear picture of where AI actually helps, before you commit to anything bigger.
Does this sound familiar?
We built a pilot. Everyone loved the demo. It's been six months and it's still not in the product.
We have ideas and board pressure, but no team to build the AI.
Our data foundation can't support the AI we want.
We don't want to hire a whole AI team yet — but we need someone who's done this before.
None of these are model problems. They're delivery problems. That's the part we do.
I've been building software since I was 15, and it still feels like play. After 27 years, the work I enjoy most is taking the AI project everyone's stopped believing in and getting it quietly into production. Teams sometimes call me their last line of defense. I'd rather be the reason they don't need one.
Is this right for you?
This practice is usually the right call when
- You have a product AI should live inside — not a slide deck that needs an AI section.
- A pilot or initiative is stalled, and you want it shipped rather than re-scoped.
- You have a team, or are willing to hire one — we lead, build alongside, and hand off; we don't become your permanent dev shop.
- You want senior AI direction and build capacity, without committing to full-time AI hires yet.
Probably not a fit when
- You want a rented team with no path to owning it yourselves. That's the one thing we don't do — we build, hand off, and help you hire the people who replace us.
- You need a quick demo for a board meeting, or the cheapest possible offshore build. Plenty of shops will sell you that; we won't.
The boring questions
Before we write a line of code, we get honest answers to the unglamorous questions that decide whether AI survives contact with production. If an AI partner isn't asking these, they're building you a demo — not a product.
Data reality
Where does the data actually come from — and who owns keeping it clean?
Failure modes
What happens when the model is wrong? Who notices, what breaks, and who reviews the output before a customer sees it?
No silent failures
Unit economics
What does each call cost at real volume, and what latency can your product actually live with?
Margins survive real volume
Testing & evals
How do we test it — and how do we know a "better" prompt didn't make something else worse?
Better means measurably better
The honest no
Sometimes the answer is that AI won't help. You'll hear that in week one, not month six — in writing, in the first deliverable.
Sachin Garg sets the direction — build-vs-buy, on-prem-vs-API, what to ship first — and stays accountable for it as Head of AI. Rawzor associates build under his direct oversight: the pipelines, the models, the production hardening. When the capability is real, we help you hire the permanent people who replace us — on your payroll, as your team. A practice, not a prompt shop: we don't rent you developers.
AI in production, not on slides
Stalled 9 months → shipped in 2
A US VoIP company's self-hosted AI initiative — on-prem call transcription + LLM summarization (Whisper / Vosk / Llama) — had been stuck for nine months. We brought in the right specialists, optimized GPU batch throughput, and shipped it to production in two.
Realtime AI chat, in a live consumer app
Sub-10-second first response, realtime thereafter — shipped to the App Store for an early-stage consumer AI startup, with the subscriptions, auth, and encryption a real product needs.
Vibe-coded → App Store
A founder-built, AI-generated codebase reviewed, hardened, and launched: architecture cleanup, error handling, a test/eval framework, and a public release that held up.
Deep-tech heritage
Computer vision, GPGPU optimization (18.8x speedups), and compression research cited by the imaging community — the practice's judgment is built on decades of hard engineering, not a 2023 pivot.
Real engagements — clients described generically, never named.
A small, honest first step: a clear picture of where AI actually helps, before you commit to anything bigger.
Start with an AI Readiness Sprint →How we work — from strategy to shipped
Four phases. The last one is your team owning it.
- 1
Discovery & Assessment
The AI Readiness Sprint
Where AI realistically helps, build-vs-buy, on-prem-vs-API — and one quick win identified. This is the paid front door.
- 2
Quick Wins & Foundation
Ship the first win, stabilize the data foundation, put testing and review loops in place.
- 3
Scale & Optimize
Production hardening: cost, latency, throughput, monitoring, and the pipelines that keep it honest.
- 4
Transition & Hand-off
Built in from day one
We document, train your team, and help you hire the permanent people who replace us. Your team owns the system; we step back to oversight.
The whole model in one line: you own the team, the code, and the capability; Rawzor owns the leadership, the process, and the outcome — we don't rent you developers.
We bill only for the exact time we work — never a flat block of hours.
Book a short call. We'll talk through what you're trying to ship and whether the AI Readiness Sprint is the right first step — no pitch, no pressure.
Start with an AI Readiness Sprint →Start with the Sprint. On purpose.
AI Readiness Sprint
$6–12K fixed scope
A small, honest first step. You leave with a roadmap, a build-vs-buy call you can defend, and one quick win — whether or not you continue with us.
Book itData & AI Practice
$8–25K project / $4–8K/mo retainer
Strategy and delivery in one practice — pipelines, models, and production hardening, delivered by associates under Sachin's Head-of-AI oversight.
Sometimes the honest answer is that AI won't help — you'll hear that in week one, not month six. If the Sprint is all you need, that's a perfectly good outcome.
Fair questions
Another AI agency?
Led by someone who's shipped production AI for 20+ years — and we'll tell you where AI won't help, in writing, in the first deliverable.
Who actually builds it?
Rawzor associates build under Sachin's direct oversight — and we help you hire the permanent team that takes it over. We don't rent you developers.
How is this different from your Data & BI practice?
Data & AI ships AI features into your product; Data & BI builds the reporting and dashboards you run the business on. Same bench, different front door.
What if our data isn't ready?
That's what the Sprint finds out — fixing the foundation is usually phase one, not a reason to wait a year.
How do you bill?
Fixed scope for the Sprint and projects; retainers for the practice. We bill only for the exact time we work.
Find out where AI actually helps — and where it won't
Book a short call. We'll talk through what you're trying to ship and whether the AI Readiness Sprint is the right first step — no pitch, no pressure.
Email works best right now — write a couple of lines about where things stand and you’ll get a real reply from a real person, usually within a day.