Services›Forward Deployed Engineers

Our engineers, inside your team, putting AI to work.

AI is only worth what it does for your operation. Forward deployed engineers sit with your people, learn how your business actually runs, and turn that into agents and automations carrying real load.

Why forward deployed

The hard part of AI enablement isn't the LLM model. It's your business.

Which system holds the truth, who is allowed to decide what, which "quick" process has eleven exceptions: that is the actual problem. It lives in your people's heads and your tools' history. Someone has to sit close enough to learn it, and have built enough of this to know what will hold up in production.

However you want to build it

Your stack, or ours.

Some teams want a platform that arrives with the governance already built. Others have their models and their architecture settled, and want the work delivered inside them. Both are good answers, and which one fits depends on your systems, your constraints and how far along you already are. We build either way, so the advice you get is not shaped by what we would rather sell you.

On Forge

A governed platform, at a price you can forecast

Devtech Forge AI arrives with connectors, agents, approval gates, policies and an audit trail already built, in a single-tenant instance in your region. We run our own infrastructure and models, so it is a flat monthly fee per agent, however hard that agent works, with no token bill behind it.

On public frontier models

Claude and the models you already use

As an Anthropic partner we build with Claude, and we work with the other major providers and cloud AI services. Where you already have a model relationship, we build on it rather than around it.

In your own stack

AI inside the systems you run

Agents, retrieval and automation built into your product or your internal platform, on your cloud, under your existing security and procurement rules.

How an engagement runs

From a real process to work that runs itself.

Engagements are scoped in weeks, not quarters, and measured by work moved off people's plates rather than hours logged. A typical shape:

01 · WEEKS 1 TO 2

Learn the work, choose the approach

We sit with the teams whose work is in scope and map what actually happens, exception by exception. That decides the approach, the models and where the data has to stay, and the environment is set up accordingly.

02 · WEEKS 3 TO 6

First work live

The first agents and automations go into production against real tickets, alerts and requests, with a person approving what matters. Your people review the outputs; we tune until they'd sign their name to them.

03 · WEEKS 7 TO 12

Widen and hand over

More teams, more automations, work that runs overnight. Your engineers and admins take the controls; we stay on as needed, then step back to a lighter touch.

What they do

Whatever stands between your team and the work running itself.

Connect

Systems and scope

Get the agent to your code, tickets, documentation, chat, monitoring, support and CRM data; decide what is in scope for which use case; set the controls your admins will enforce.

Build

Agents and automations

Agents with the right instructions and reach, workflows with approvals where a person should decide, scheduled work for the recurring jobs nobody wants.

Evaluate

Model choice, tested not argued

Which model for which step, what it costs per run, where it fails. Measured on your own cases, so the choice survives contact with your data.

Prove

Trust, one output at a time

Run the new way alongside the old until your team trusts it; measure what moved, what got faster and what the humans stopped having to do.

Enable

Your team, in the driver's seat

Working sessions with your engineers and admins, so building the next automation is something they do without us.

Report

Outcomes, not activity

Regular reviews on what is now running without people, with the run history and audit trail to back it up.

Who it's for

Teams that want results this quarter, not a pilot next year.

Forward deployed engineers make the most sense when the work is clear and the appetite is there, but the hands are not.

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