We build it, we prove it, we keep it true — Deploy, Assure, Operate — powered by a network of verified experts. Every engagement has a written scope, a named owner and an exit condition, and a thirty-minute working session in front of it that produces a written range before you commit to anything.
One loop, three doors in. Enter wherever it hurts — a workflow that will not ship, a system nobody dares to trust, or an agent that decayed after launch.
Mission Pods — a senior forward-deployed engineer plus a delivery lead, embedded in your team. One workflow, a written definition of done, first production ship inside two weeks.
Independent evaluation and red-teaming of the systems you are asked to trust — domain test sets built with named experts, findings ranked, reproducible and signed by people with no stake in your launch date.
Monitoring for truth, not uptime: accuracy drift, cost per task, exception volume, tool-use failures — with human escalation paths and a named owner who still knows your account in month nine.
All three practices run on the Expert Network — engineers, clinicians, lawyers, accountants and linguists who passed six vetting gates — and are counted on Q-Base, the platform we also sell as a product. Front door: a thirty-minute working session, and where useful a two-week readiness sprint that ends in a mission brief, ready to execute.
AI fails as a programme and succeeds as a single workflow. We take the one with the clearest economics, build it properly — data, evaluation, guardrails, integration — and then stay on to run it, because month nine is when everyone else has already left.
The forward-deployed engineer (FDE) — a senior AI engineer embedded inside your team, your stack and your rituals — is the role Palantir invented and the AI labs now hire by the hundreds. We supply it as a service: the FDE builds inside your stack and your rituals; a delivery lead owns scope, stakeholders and the evidence gate. Mission-scoped with a written definition of done, milestones that clear only on evidence, and progress you watch live on a shared delivery dashboard.
One senior AI engineer, embedded full-time. LLM integrations, agents, RAG, MCP servers, evaluation harnesses — shipped to production.
FDE + delivery lead with one mission and a written exit. The unit that turns "we bought AI" into a workflow that runs.
First production ship inside two weeks — deliberately small, deliberately real. If a milestone does not clear its evidence gate, the next one does not bill.
One production workflow end to end: built, integrated, tested against a written accuracy bar, shipped behind guardrails with an exception path to a human, handed over with runbooks.
The work that decides whether anything above it survives: sources connected, unstructured content prepared, retrieval built and tuned, quality gates measured against a stated threshold.
Front door for new clients: a two-week AI Readiness Sprint — workflows and data assessed, opportunities ranked, ending in an actionable mission brief. Sprint fees are credited against a mission booked within sixty days.
Independent evaluation is now a regulatory expectation in Europe and a board expectation everywhere else. The strongest evidence comes from qualified reviewers outside the build team — independent, credentialed, and human.
We write the accuracy bar with you, build a domain evaluation set your team could not assemble alone, run it, and report where the system falls short — expert-reviewed, in 20+ languages where needed.
A team whose job is to break the system: prompt injection, data exfiltration, tool misuse, and the domain-specific harms only a specialist would try. Findings ranked, with reproduction steps.
The substrate regulators actually ask for: system inventory, risk classification, evaluation evidence, logging and documentation — assembled so legal argues from a file, not from memory.
Native-speaker domain reviewers, per-language scorecards, and a clear statement of where you should not yet deploy — because accuracy does not survive translation intact.
Any of these can roll into Continuous Assurance — the quarterly programme above — once the first baseline is set. Boards, regulators and enterprise customers increasingly ask for evidence that testing recurs, not that it happened once before launch.
Models get swapped, prompts get edited, data drifts, and the audience for evidence — boards, regulators, enterprise customers — keeps asking for this quarter's proof, not last year's. These two programmes exist because AI is never finished.
Quarterly re-evaluation and adversarial testing against your evolving system, run by named experts with no stake in your roadmap. Every cycle updates a signed evidence pack — the artifact your board cites, your regulator requests, and your enterprise customers ask for in procurement.
Your cloud dashboard says the agent is up. It does not say the agent is right. We watch accuracy drift, cost per task, exception volume and tool-use failures — with human escalation paths, monthly re-evaluation, and a named owner who still knows your account in month nine.
Both programmes are where an engagement naturally lands: a mission ships the system, assurance proves it, and the retainers keep both true. Ranges are named on the first call, in writing.
Senior AI roles take three to five times longer to fill than ordinary software roles, and verifying real, working expertise has never mattered more. Both challenges are answered the same way: a smaller, deeply vetted pool, with a human at the final gate.
Clinicians, lawyers, accountants, engineers, linguists and scientists assembled to evaluate AI output — for model training, evaluation, preference data or acceptance testing. Credential-verified, paid at specialist rates, and briefed to challenge the system rather than confirm it.
Assessment as a standalone service, for candidates you sourced yourself. A senior specialist in the same discipline leads a live, unscripted session and issues a signed competency report — the one gate AI assistance cannot sit behind.
Vetted engineers, data and AI specialists working inside your team's rituals and tools — matched in days, productive in week one, scaled either direction as the work changes. Knowledge transfers by default, so you are never dependent on us to change a workflow.
Specialist permanent search for the roles the market cannot supply: AI engineers, forward-deployed engineers, ML platform and applied research hires. Structured scorecards, a live expert session on every finalist, and a signed competency report you keep whether or not you hire.
We are deliberately smaller than the marketplaces. Every person in the network has passed a live session with a senior specialist in their own discipline — a process that does not scale to millions and is not meant to. If you need ten thousand annotators, we are the wrong firm.
No engagement here is open-ended. Each has a written scope, a typical length, and an exit condition — and you get a written range on the first call, before you commit to anything.
Agent Launch and Data-Ready work run inside missions; multilingual evaluation runs inside any assurance engagement. Readiness-sprint fees are credited against a mission booked within sixty days; assessment fees against a placement. Third-party model and licence costs pass through at cost, itemised.
Credentials and automated screens tell only part of the story — real expertise shows in live conversation. That is why every gate in our process is designed to surface genuine, working knowledge, and why the final one is human.
So our final technical gate is deliberately expensive and deliberately human: a senior practitioner in the same discipline, leading a live, unscripted session. It is deliberately unscalable — and it produces a signed competency report the expert carries into every engagement after.
Gate 04 · live, on cameraSpecialists enter by referral, verified track record and targeted search. There is no open signup, and we do not buy lists.
Role-specific technical assessment scored against a rubric agreed with you in advance — not a conversation and a gut feel.
Portfolio and production work reviewed by a senior specialist in the same discipline, not by a recruiter with a checklist.
Unscripted, practitioner-led, probing depth rather than recall. Identity verified. This is the gate AI assistance cannot sit behind, and the one we will not skip for speed.
An experienced consultant validates every match against your actual context before it reaches you. No automated shortlists, ever.
Delivery outcomes are counted — utilisation, milestones, findings quality — and inform every future match.
If a step does not clear its criterion, we do not start the next one and you do not pay for the next one. That rule is in the contract, not just on this page.
The system you are unsure about, or the role you cannot fill. Thirty minutes, and a price range before you leave the call.
What good looks like, what failure looks like, who is qualified to decide, and what evidence you need at the end.
Experts sourced and verified, or engineers matched. Named individuals, with their assessment reports attached.
Evaluation run, system built, or role filled — against the criterion agreed in week one, tracked live on Q-Base — the same delivery platform we sell as a product.
An honest report including what we could not establish. Runbooks and evidence packs you keep and can hand to an auditor.
Quarterly re-evaluation, managed operations, or a replacement guarantee — depending on which practice you engaged.
AI does the volume — sourcing, screening, drafting, reconciling. People make the calls. Every meaningful recommendation and every finding carries a named human sign-off.
An evaluation designed for a system you built is a rehearsal, not a test. Where we built the thing, we say so at the top of the report — and sometimes tell you to hire someone else.
We report what happened — candidates assessed, findings raised, runs completed. We will not model business value from assumptions you never set.
Our people work inside your rituals and tools, and knowledge transfers by default. You should be able to run it without us. That is the test we set ourselves.
Thirty minutes. A scoped answer, a price range, and an honest view — including that you should wait, if that is what we think.