Most enterprise AI pilots stall before production — not because the models are bad, but because nobody wires them into the real workflow. We're a small team of senior, ex-FAANG engineers who forward-deploy alongside your team, ship a working hybrid AI system in 6–12 weeks, and hand off the code — for the cost of a few months of a senior hire you can't find, not a $560K headcount or a $3M consulting program.
No faceless agency, no bait-and-switch to junior contractors. A small team of senior engineers with big-tech pedigree, forward-deployed into your codebase from week one.
Every engineer has shipped production systems at the scale and reliability bar of the largest tech companies — the experience you'd hire for, without the headcount.
We've taken AI systems past the demo and into production — handling real data, real load, and the edge cases that quietly kill pilots.
Deep across classical AI, data engineering, and modern LLMs — so the architecture is chosen for the problem, not for the hype cycle.
Frontier models now perform similarly. The risk — and the cost — lives in how you wrap them. My opinion, built into every system: a deterministic spine carries the load; the LLM is invited only where it earns its keep.
Rules, parsing, schema validation and math do the heavy lifting: auditable, repeatable, and impossible to hallucinate. This is the part you can put in front of a regulator.
The model is invoked only for reasoning and synthesis, where ambiguity is genuine. Used sparingly, it stays cheap — and your token bill stops paying an LLM to do a parser's job.
Every engagement starts with a fixed-fee audit one person can approve — no procurement, no leap of faith. Each step earns the next.
A fixed-scope read of your stack: current vs. hybrid cost, accuracy and latency deltas, and a prioritized, costed migration roadmap. The deliverable doubles as the build SOW — and it's yours to keep whatever you decide next.
A scoped PoC on your real data, targeting one agreed success metric. It de-risks the build for you and calibrates the estimate before any fixed bid is on the table.
A fixed-scope hybrid system shipped into production — with full code handoff and a written knowledge-transfer plan. Milestone-based payments. Your team runs it when we leave.
A fractional, forward-deployed engagement to build your next AI workstream — explicitly for new problems, never to maintain what was already handed off.
Indicative ranges, not a price list. Scope sets the number. The audit is deliberately priced so a single budget owner can say yes today — and everything you pay for is yours to walk away with.
The problem-shapes we've put into production — proof of method, not slideware. Details kept abstract by design; we discuss specifics under NDA.
A research and analysis platform where classical models do the quantitative heavy lifting and the LLM handles interpretation and natural-language querying. The statistical core is deliberately isolated from the language layer — so accuracy is never at the mercy of a model's mood.
A document-grounded assistant that ingests files and answers questions entirely on-device. Embeddings and retrieval run locally — no API calls, no data egress, no per-query cost. Built for teams who can't send sensitive material to a third-party cloud.
A system that runs small models on-device for the common cases and escalates to cloud models only for the genuinely hard ones. An explicit cost-and-privacy tiering strategy — most requests never leave the device, and the bill reflects it.
A multi-stage pipeline that researches, decides, and produces a finished deliverable end to end. Structured outputs drive each stage with validation between them, on a provider-portable backend that runs local or cloud at near-zero cost.
A deliberate route. Each stage produces something concrete and CFO-readable — no open-ended retainers, no vapor.
We map where on your stack the LLM is overpaying for work a deterministic layer should own — and what the fix is worth.
A PoC on your real data against one success metric, defined before any code — so we both know what "working" means.
We embed and ship a production hybrid system, validated against your data with deterministic guardrails throughout.
Full code, written knowledge transfer, your team trained. You own it and run it. We step back.
The biggest fear with an outside engineer is the one who never leaves — or the system you can't run without them. Both are engineered out from the start.
Every line ships to your repos with a written knowledge-transfer plan and your team trained to run it.
Your keys, your cloud. Data stays in your VPC — and there's no single-model vendor you're chained to.
You will not pay us the same in month 12 as month 1. The system is yours to operate the day we leave.
Any ongoing engagement exists only to build your next thing — never to maintain what we already handed off.
That's the whole deal, in writing, in the contract. Independence isn't a feature here — it's the structure of the engagement.
BYOK and on-device options by default — sensitive data never has to leave your environment.
The deterministic spine is repeatable and inspectable — the part you can put in front of a regulator.
Type II readiness in progress. We'll tell you exactly where it stands rather than imply a posture we don't yet hold.
A fixed-fee audit with an explicit exit clause — see senior work on your real data before any larger commitment.
Tell us the workstream that's costing you most. You'll get a fixed-fee, ~2-week audit: where AI fits, what it saves, and a costed roadmap you keep — whatever you decide to do next.