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An AI engineering practice

Production AI systems for teams already shipping.

Agent architectures, automation pipelines, and focused AI products — built with the engineering discipline production actually demands. No demoware, no waterfall roadmaps, no hand-waving.

Engagements6–12 weeks
Cadence3 clients / quarter
TeamSenior engineers only
Production agent workflow — triage, routing, verification, and handoff.
Working with engineering teams at
  • Series B fintech
  • Fortune 500 retailer
  • Public research lab
  • Venture-backed mobility
  • Managed security platform
  • Healthcare analytics
What we build

Four disciplines. One delivery standard.

Each engagement is scoped to the piece that moves the needle. No generalist consulting, no theatre, no half-shipped prototypes.

All capabilities
01

AI engineering

Agent architectures, retrieval, and evaluation harnesses built to survive production traffic — not demo day.

  • Tool-calling agents with policy + contract checks
  • Hybrid retrieval, rerank, and offline eval suites
  • OpenTelemetry tracing and replayable regressions
02

Automation

Typed workflow systems that replace brittle manual ops with idempotent jobs and clean handoffs.

  • Durable queues, retries, and backoff by default
  • Typed IO contracts between every pipeline stage
  • Dashboards and alerts owned by your on-call
03

AI products

Focused internal tools and external surfaces that ship fast without sacrificing product fundamentals.

  • Thin, reviewable slices — no waterfall roadmaps
  • Design-system-first UI, accessible by default
  • Feature flags and staged rollouts from day one
04

Consulting

Technical reviews and scoped build sprints for teams already in motion who need a second engineer in the room.

  • Architecture review against production evidence
  • Eval + observability audits with a written report
  • Pair-engineering sprints on the hardest subsystem
Delivery standard

How systems leave our hands.

The things we refuse to compromise — whether the engagement is six weeks or six months.

Evaluation before release

Every system ships with a replayable eval suite. If it cannot be measured, it does not merge.

Observability from day one

Tracing, token accounting, and alert routes are wired before the first production request.

Handover you can actually run

Typed contracts, written runbooks, and a named owner on your team — not a 200-slide deck.

Approach

A four-step cadence, the same every time.

The scope changes from engagement to engagement. The operating rhythm does not.

Audit existing systems

Read the code, the traces, and the runbooks. Calibrate scope against real constraints, not a wishlist.

Design agent architecture

Settle the contracts before a line of production code lands — retrieval strategy, tool surface, guardrails, eval plan.

Deploy with observability

Ship behind flags with tracing, evaluation, and alerting wired in from the first request through to payload regressions.

Monitor and iterate

Hand over dashboards, runbooks, and an owner. We stay close for one iteration cycle, then get out of your way.

Based remote. Senior team, written process, receipts on request.Prefer email?