---
type: "Service"
title: "Forward Deployed Engineering"
description: "An AI engineer inside your team until the solution runs in production"
resource: "https://www.contextstudios.ai/services/forward-deployed-engineering"
language: "en"
generated:
  by: "process:contextstudios-md/1"
  at: "2026-11-07T19:38:40.139Z"
status: "stable"
---

# Forward Deployed Engineering

Forward Deployed Engineering: we work directly in your systems and with your team, build the AI solution where it has to run, and hand it over in production – accountable for the outcome, not for billed hours.

Most AI projects don't fail because of the model but because of the environment: data access, permissions, processes that grew over years, and nobody in-house with the time to carry a pilot into operations. Forward Deployed Engineering starts exactly there. Instead of translating requirements into tickets and delivering from a distance, the engineer works in your repository, your ticket system and your communication channels, talks directly to the business team and builds the solution against real data. At Context Studios, the founder leads every engagement personally; specialists join when needed, and your point of contact stays the same. Work happens remotely, on site by arrangement – for example for the kick-off, workshops with the business team or go-live. An engagement typically runs 4 to 12 weeks and can be extended. We deliberately counter the known weaknesses of the model – dependence on one person and solutions only their builder understands: the code is yours from the start, decisions are documented, and your team works alongside so it can carry the solution forward after handover.

## Perfect For



For companies with a concrete AI use case – an agent, a knowledge search with sources, a process automation – where the real work sits in their own systems, data and workflows. For IT teams that lack AI experience, and for business units whose pilot never got past the demo.

## Benefits



- One accountable engineer instead of hand-offs between consulting, concept and delivery

- Work in your tools – repository, ticket system, chat – not in a parallel world

- Built against real data and real permissions, not a demo environment

- Remote day to day, on site by arrangement for kick-off, business workshops and go-live

- Documented decisions and your team working alongside, so no dependency remains after handover

- Specialists as needed, coordinated through the same point of contact

## How we work on it

- Sprint — 4 weeks · fixed price after scoping: Four weeks on one goal, with something that runs at the end.
- Build & Support — after scoping, ongoing · fixed price after scoping; support billed monthly: We build the project out and stay alongside you once it is live.

### Included

- Embedding in your team and tools (repository, ticket system, communication)
- Scoping the use case with a measurable acceptance criterion
- Building the AI solution in your environment, against real data
- Go-live with monitoring and approval checkpoints
- Documentation, handover and onboarding of your team

### Not included

- Training a custom AI model
- Permanent staffing or temporary agency work
- Running costs for AI services and infrastructure

### How it runs

- **W1 — Embedding & scoping**: Access, conversations with business and IT, a review of your systems; afterwards it is clear what will run in production at the end and how we measure it.
- **ongoing — Building in your systems**: Development in your repository in short cycles, regular check-ins with users, specialists as needed.
- **end — Go-live & handover**: Go-live with monitoring, documentation and onboarding of your team; if a sensible next step is due, the engagement can be extended.