Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
Six service lines, one reference architecture, and a single senior team from first audit to embedded build, each engagement scoped to the business outcome you are funding, not a list of deliverables.
Six capability areas where we've shipped to production, from copilots and agents to the eval suites that keep them honest.
Copilots and assistants embedded where your users already work, lifting conversion, deflecting support, and shortening workflows. Streaming UX, tool calling, citations, and a real eval suite behind every claim.
Goal-directed agents that take work off your team’s plate, structured tool use, sandboxed execution, and human checkpoints. Autonomy ratchets up only as the eval coverage, and your trust, does.
Answers grounded in your own documents, code, and knowledge, so customers and staff stop hunting and start deciding. Hybrid search and re-rankers tuned on your data; citations baked in, hallucinations measured and capped.
When prompt engineering hits its ceiling and inference cost hits your margins: SFT, DPO, LoRA, and distillation to smaller, cheaper models, gated by eval gains, not gut feel.
Interfaces for non-deterministic output that users actually adopt: confidence affordances, undo, graceful failure, and steering controls that turn an AI feature into an AI product.
The work that lets compliance, legal, and your board sign off: red-team suites, drift monitoring, audit trails, EU AI Act classification, and controls documentation ready to hand over.
Seven stages we ship with every AI engagement, your data stays yours, your outputs stay grounded, your costs stay predictable. The boring engineering most vendors skip.
Your sources stay yours · PII auto-redacted
Hybrid search · re-ranked for relevance
We route per query · cheapest that meets bar
Gated by tests · auto-paged on drift
24/7 monitoring · on-call rotation
Most AI projects ship a demo, then quietly drift. We attach an eval harness before model selection, treat regressions as bugs, and watch drift continuously. You get a number, every day, that says it's still good.
$ octal eval run --suite=copilot.v3 --model=claude-4.5-sonnet --against=baseline [14:22:08] running 412 cases across 6 suites · estimated 4m 12s ✓ faithfulness.v3 198/200 0.990 Δ +0.012 ✓ helpfulness.v2 194/200 0.970 Δ +0.008 ✓ citation.coverage 97/100 0.970 Δ +0.020 ✓ safety.redteam.v42 56/56 1.000 Δ ±0.000 ✓ latency.p95 184ms target ≤ 250ms ⚠ tool.success.refund 84/100 0.840 Δ −0.020 (auto-ticket #4218) [14:26:20] summary: pass · 411/412 · 0.997 overall · 4m 12s [14:26:20] regressions: 1 minor · gating: unblocked · artifact: s3://evals/2026-05-23-1422 [14:26:21] posting to #copilot-evals · paging on-call for tool.success regression › deploying claude-4.5-sonnet to prod (canary 5%, 30m)...
The two-week clarity sprint. Architecture review, eval coverage assessment, model selection, and a written engagement plan. Buy it when the next quarter’s spend is on the line.
Production engagement. Feasibility through deployment, eval harness, MLOps wiring, and a 6-month operations runway. Buy it when you’ve decided what to build and need it to actually ship.
Senior squad in 14 days. AI engineers and researchers embedded in your team. Per-seat pricing, quarterly scope. Buy it when you have momentum and need depth.
We treat AI like software, version-controlled, evaluated, monitored. The work that lets compliance sign off and operations stay calm.
Security controls mapped to the SOC 2 Trust Services Criteria and built audit-ready from day one.
Information security managed to the ISO 27001 framework, end to end.
PHI safeguards, de-identification, and audit logging engineered for HIPAA-grade healthcare work.
Data residency, DPIAs, and AI Act risk classifications baked into delivery from day one.
Six short answers to the questions that come up before every engagement. Anything missing? Bring it to the first call.
A senior engineer is on the first call within 48h of an inbound. Audits typically kick off in 7 days; builds in 14–21.
Yes. About 35% of our clients are non-technical. We translate, we don’t gatekeep.
Yes. We’ve inherited Django monoliths, Salesforce, mainframes, and worse. Our preferred stack is a starting point, not a requirement.
We’ll tell you. We run a build-vs-buy memo at the start of every engagement, and we’ve talked clients out of AI builds more than once.
You do. Always. Code, models, fine-tuned weights, evals, all yours under the MSA.
Yes. We have a mutual NDA ready in your inbox before the calendar invite if you ask.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.