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Three ways we work with teams.

Consulting on the workflows your agents already live in. The typed spine that turns the chaos behind those agents into a governable system. And the bespoke applications you ship on top of it.

  • 01 · Product

    Coding-agent workflows

    The single biggest productivity unlock for your team is the workflow around Codex, Claude Code and Gemini — not the next model release.

    We sit with your engineers, designers, operators and editors and design the rituals, repo shapes, skill files and review loops that turn agent output into shippable work. The agents are extraordinary; teams using them at five percent of their potential are extraordinarily common.

    What’s included
    • Skill files and harness tuning for Codex desktop, Claude Code, Cowork
    • Repo conventions and instruction files (CLAUDE.md, AGENTS.md, .cursorrules)
    • Compound-engineering review patterns and PR queues
    • Token-budget discipline, context management, eval harnesses
    • Onboarding and training for the humans in the loop
    Outcome

    A team that ships compounding work with the agents they already use — not a new tool to learn.

    Talk to us
  • 02 · Product

    Data infrastructure & process mining

    You can’t put agents on production until you can name what production is.

    We map the entities your team actually talks about, mine the processes your business actually runs, and ship the typed spine they live in: a small domain-driven core, queries and commands with explicit contracts, and a complete event log. Then we host the MCP endpoint your agents act through.

    What’s included
    • Process mining and domain-driven design — entities in your vocabulary
    • Typed schema, 5–15 entities, fits on one page
    • Operations layer: queries (read) + commands (write + audit)
    • Append-only event log for audit, revert, and replay
    • Hosted MCP endpoint your existing surfaces point at
    • Postgres backend — we host, or hand it over
    Outcome

    A typed backbone that survives prompt changes, model swaps, and whatever surface your team adopts next.

    Talk to us
  • 03 · Product

    Bespoke AI applications

    Sometimes the right answer isn’t an agent in a chat window. It’s a purpose-built tool that does one thing well.

    We build production AI applications end-to-end — problem framing, model and provider selection, evaluation harness, UI, deployment — sized for your domain and tied into your spine so every change is measurable against real history.

    What’s included
    • Domain-specific RAG, structured output, tool-using agents
    • Embedded chat, internal dashboards, batch pipelines, decision tools
    • Evaluation tied to your event log — replay before you ship
    • Deployed on Vercel, your cloud, or ours — TypeScript or Python
    • Provider-agnostic via the AI Gateway — Anthropic, OpenAI, Google, open-source
    Outcome

    An application that solves a specific problem your team has — not a generic agent stapled onto a wiki.

    Talk to us
Engagement

How it actually runs.

The shape is the same across all three products: short, opinionated, grounded in the words your team already uses. Roughly six weeks from first call to live.

  1. Phase 01 · Listen
    Week 1

    We sit with the people doing the work. We don’t bring an ontology. We bring a notebook. The vocabulary is the deliverable.

  2. Phase 02 · Model
    Weeks 2–3

    We cut a schema small enough to fit on one page. We specify queries, commands and their event-log rows. Contracts first; implementations after.

  3. Phase 03 · Ship
    Weeks 4–6

    Postgres, operations layer, hosted MCP endpoint. Your existing surfaces — Codex, Claude Code, Cowork — point at it. Migration done behind the operations.

  4. Phase 04 · Govern
    Ongoing

    New functionality is a new operation, not a new schema. Replay last month before you ship next week. The spine is what you keep.

Tell us which of the three you need first.

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