Applied AI Lab
RocketTools is a small applied lab. We work out what's actually possible right now, build it, run it in production, and hand over the version that held up.
| System | Domain | Carries | Status |
|---|---|---|---|
| content-intelligence | Retail | 8 data sources → weekly roadmap | running |
| attribution-reconciler | Retail | 3 ad platforms + offline revenue | running |
| clinical-data-app | Healthcare | Genome + 15 yrs lab history | running |
| archive-search | Media | 10 yrs video, conversational | running |
| formulation-tool | Manufacturing | Production floor, daily use | running |
Applications in production
12
Research briefs delivered
25+
Sectors currently running our systems
Healthcare, retail, media
Counted from systems live in production as of August 2026 — not pilots, proofs of concept, or engagements that ended at a deck.
The premise
We're the other kind. Everything we sell is already running somewhere — carrying real load, under real accountability, measured against real numbers.
An intelligence engine pulling eight data sources to decide what a consumer brand publishes each week, and reporting the revenue against it. An attribution system reconciling ad spend across three platforms against offline, in-store purchases. A conversational clinical application built on a patient's whole genome and fifteen years of lab history. A production tool doing formulation math on a specialty manufacturing floor.
None of these were pilots. That's the standard.
Some we built for clients. Some we built because we needed them. All of them are still running.
What we do
When the answer doesn't exist yet: competitive landscapes, market positioning, buyer intelligence, technical feasibility. Days, not a quarter — because the research runs on tooling we built rather than on billable analyst hours.
MVPs, internal tools, and client-facing applications. Napkin to deployed in weeks. Small enough to throw away if the idea was wrong. Solid enough to run a business on if it wasn't.
Content systems that operate continuously and report on themselves. The engine underneath decides what's worth making; the output on top is measured, not guessed. You get the machine, not just the deliverables.
Specimens
Field notes
Not thought leadership. The actual notes — what worked, what broke, and what we'd do differently.
Stage
Conferences, workshops, and executive sessions on applied AI: what's actually working right now, what isn't, and why most of it fails in the same three ways.
That's the work. Tell us what you're trying to figure out, and we'll tell you whether we're the right shop for it.
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