Questions enterprise buyers actually ask.

Most engagements open with a paid two-week discovery that ends in a working slice — an instrumented baseline, a thin vertical of the system, or both — not a slide deck. From there we ship in increments, usually putting something measurable in front of users inside the first 30 to 45 days. We instrument first, ship in increments, and prove what moved, so you are never waiting a quarter to learn whether it worked.

You get a dedicated team, not fractional — a named, partner-led pod that holds your context end to end. The senior people who scope the work are the senior people who build it: no junior hand-offs, no rotating bench, no account manager relaying messages. You meet the team that owns your number, and they stay accountable to the number on your board deck for the life of the engagement.

No. We build custom systems against your data, your workflows, and your guardrails — retrieval over your governed corpus, model-agnostic orchestration, and human-in-the-loop where the stakes require it. Every system ships with an evaluation harness so quality is measured, not assumed, and regressions are caught before they reach production. The model is one component; the governance, evals, and integration around it are the actual product.

We build security-review ready from day one: SSO, scoped role-based access, full audit logging, and SOC2-aligned practices your security team can sign off on. We are comfortable working inside your VPC, your cloud account, and your existing controls rather than asking you to bend to ours. Bring your questionnaire, your pen-test requirement, and your reviewer — we expect the scrutiny and engineer for it.

Sensitive data stays governed end to end. We enforce row-level and attribute-level access so the system only ever sees what a given user is entitled to, and we wire retrieval through governed RAG rather than dumping raw records into a prompt. Where it fits, we de-identify before data ever reaches a model, keep audit-ready access logs, and keep regulated workloads inside boundaries your compliance team already trusts.

You do. Code, infrastructure, model weights where applicable, prompts, evals, and documentation all live in your repositories and your cloud accounts — there is no lock-in and nothing held hostage to keep us engaged. We architect so your in-house team can run, extend, and audit the system after we step back, and we hand over knowledge deliberately so you are never dependent on us by accident.

We instrument first, ship in increments, and prove what moved — the baseline is captured before a dollar moves so the lift is defensible later. Every engagement is tied to a small set of business metrics agreed up front, and we report against them in language a CFO recognizes, not vanity dashboards. The goal is a number on your board deck we are both willing to stand behind, with an auditable trail back to its source.

Pricing is custom-scoped to the mandate, so it is always Contact for pricing rather than a published rate card. What drives it is team size, the SLA you need, and the depth of security and compliance work involved — a single embedded pod is a different shape than a multi-team program with a tight uptime commitment. We scope it transparently on the first calls and put it in writing before anyone signs anything.

Yes — embedding alongside your people is one of the most common ways we work. Our team joins your stand-ups, your repositories, and your tooling, pairs with your engineers, and transfers ownership as we go rather than walling off a black box. The aim is to leave your in-house team faster and more capable than we found it, not to make them spectators.

We focus on medium-to-large organizations in sectors where data is sensitive, regulation is real, and the stakes are high — among them healthcare, financial services, and B2B SaaS. The common thread is governance and accountability, not a single vertical. You can see the sectors and the kinds of problems we take on in detail on our Industries page.

It starts with a paid two-week discovery. In those two weeks the team gets into your data and systems, instruments a baseline, pressure-tests the assumptions, and produces a written path — scope, sequencing, risks, and the number we expect to move. You leave discovery with something you own and can act on whether or not you continue with us, so the decision to scale up is made on evidence rather than a pitch.

Then we will tell you. We would rather say “not yet” — or “a query and a dashboard will do this faster and cheaper” — than sell you a model you do not need. Plenty of problems we are brought in for turn out to be data, process, or instrumentation problems wearing an AI costume, and naming that honestly is part of the job. We are accountable to the number on your board deck, and the fastest path to it is often not the flashiest one.