Engineer

Turn possibility into production.

Build the applications, agents, integrations, and data infrastructure that put AI inside real business workflows. This is the center of what we do. A prototype proves a model can do something once. Production means it does it every time, for the right user, against live systems, with failures handled and results measured. We build that whole system, and we build it to outlast the model inside it.

Talk to an AI engineerSee the proof

Capabilities

Six capabilities. One team across all of them.

  • Applied AI engineering

    Production software built around models: authentication, context assembly, tool calls, retries, fallbacks, and measurement. We treat the model as one component in a system that has to meet the same bar as the rest of your software.

  • Agent & workflow engineering

    Agents that retrieve context, call tools, execute multi-step work, and escalate the cases that need a person. Each agent has a defined scope, clear handoffs, and a record of every action it took.

    Deep diveAgent Engineering

  • Forward-deployed engineering

    Our engineers work inside your team and your environment, in your repos, standups, and change process. You get delivery speed without handing your systems to an outside black box, and your team keeps the knowledge when we step back.

    Deep diveForward-Deployed Engineers

  • AI integration & modernization

    Most of the value sits in systems that were never designed for AI. We connect to them through APIs, HL7, databases, or the interface itself, and rebuild the parts that can’t carry the load.

    Deep diveAI-Enabled Custom Development

  • Data & knowledge engineering

    Policies, documents, records, and tribal knowledge turned into structured context a model can reason over, with sources attached. We build the retrieval layer, keep it current, and scope it to what each user is allowed to see.

    Deep diveAgent Knowledge Layer

  • Model adaptation & evaluation

    We choose models per task, tune prompts or weights where it pays, and test against evaluation sets built from your own work. When a better model ships, the evaluations tell you whether to switch.

What we build

Five kinds of agents. Each one named for the work it does.

Agents are how much of this engineering shows up in production. Each runs inside your systems, under your permissions, and hands people the calls that need judgment.

Process

How a build runs. Measured at every step.

  1. 01

    Frame

    Agree the workflow, the users, the systems involved, and the metric that defines success.

  2. 02

    Prove

    Build the thinnest end-to-end slice against real data and measure it with an evaluation set.

  3. 03

    Harden

    Add identity, permissions, integrations, failure handling, and observability.

  4. 04

    Roll out

    Release to a small group, run alongside the old process, then widen as the numbers hold.

  5. 05

    Hand over

    Move into Operate with us, or to your team with runbooks and dashboards.

What you get

Things you can use. Not a slide deck.

  • A production system

    Running in your environment, integrated with your systems of record, used by real people.

  • Evaluation suite

    Test sets drawn from your work, so quality is measured, not asserted.

  • Architecture you own

    Documented, model-agnostic, and in your repositories from day one.

  • Operating handoff

    Runbooks, dashboards, and alerts, whether we operate it or your team does.

Proof

Engineer in practice. Shipped and running.

All case studies

The rest of the lifecycle

Go deeper

Bring us the workflow. We’ll bring the engineers.

Talk to the people who would build it. We’ll look at your systems, your data, and your constraints, and tell you what production would take.

Talk to an AI engineerSee our work