The pilot works, but nobody uses it
The prototype proves the idea. It still needs real data, permissions, integrations, evaluation, error handling, and a place in someone's day.
Forward-deployed AI engineers who stay until it's live.
We learn the job from the people who do it and build it into the systems they already use. When it goes live, your team keeps the code and knows how it works.
Teams we've built for
4–8 wks
Typical first release
Most first releases go live 4–8 weeks after kickoff.
Your stack
Built where you work
Your codebase, your tools, your data, your permissions.
Your repo
Yours to run
The code, evaluation cases, docs, and runbook stay with you.
Similar work, already live
“Colin built the internal tools, API and SDK our researchers use every day, and it's made us a lot faster.”
“They built all our internal outreach tools.”
“Results get logged automatically when a run finishes now. Before that we were typing them into three different spreadsheets by hand.”
Founded by Colin Burke in 2018.
The people who learn the job are the people who build it.
A forward-deployed engineer starts with the work, not a pitch. They follow real cases, find the rules and the odd exceptions, and build with the data, permissions, and systems you already have.
One small team owns scope, product, engineering, integrations, evaluation, and rollout. You don't pay one firm to write a strategy and another to learn the problem again.
Bring in an embedded team when shipping is the bottleneck
The workflow is worth doing and someone owns it. Nobody on your team has time to take it to production.
Your people are the integration
Staff copy the same details between the CRM, spreadsheets, email, payments, support tools, and internal databases.
Your roadmap is already full
The workflow has an owner and a business case. Your product and engineering teams can't step off the core roadmap to build it.
Some calls still need a person
Someone has to approve the big decisions, handle unclear cases, and see why the system did what it did.
One team, from the first real case to launch
Each step removes one risk before the system takes on more.
- / 01
Follow the work
Walk a real case from start to finish with the people who do it. Note every decision, exception, delay, and handoff.
- / 02
Draw the line
Pick one path. Name the systems it touches, where a person stays in control, and what success looks like.
- / 03
Build in your stack
Work in your repository, connect the real tools early, and test normal, hard, and unsafe cases before launch.
- / 04
Launch and hand over
Release to a small group, fix what they find, document the system, and hand it to your team to run.
What your team gets at handover
It depends on the workflow. A typical handover includes:
- A workflow map with scope, exclusions, and success measures
- Production code in your repository
- Integrations with the systems and data you already use
- Versioned evaluation cases, deterministic checks, and error handling
- Review, exception, and escalation paths for your team
- Monitoring, deployment notes, and a runbook
Build only what the workflow needs
A first release should solve the job without a replatform or a vendor you can't leave.
Use the systems you already have
A sound ERP, CRM, warehouse, or internal platform stays. We connect to it instead of replacing it.
Show when it's unsure
Evaluation results, review steps, thresholds, and logs show when the system needs a person.
Good first projects have a clear before and after
Pick repeated work that costs time or money, with an owner who can judge the result.
Operations reporting
Pull data from approved sources, apply company rules, draft the report, and send odd cases to a person.
Client onboarding
Documents, messages, approvals, and fragile automations in one flow, with a status you can see.
Research operations
Capture results as runs finish, get them ready for analysis, and drop the nightly spreadsheet handoff.
Documents and approvals
Read what comes in, check it against the rules, prep the next step, and ask a person when it needs judgment.
Three fixed-price ways to start
Start with something real users can try. Then decide if it's worth going bigger.
Sprint
1 week$5,000
Map one workflow and ship a narrow working slice or proof in your repository.
MVP
~1 monthFrom $20K
One workflow, built and deployed end to end, with design, docs, and handover.
Scale-up
2+ months$75K–$250K
A dedicated team for work across several systems, teams, or higher-risk operations.
Questions before we start
How is a forward-deployed engineer different from an AI consultant?
A forward-deployed engineer is responsible for delivery. They scope, build, integrate, test, and launch the system. A consultant usually stops at recommendations. Titles vary, so read the statement of work.
Do your engineers work in our existing stack?
Yes. We start with the code, tools, data, permissions, and workflows you already have. We change only what the new system needs.
Are you tied to one AI model provider?
No. We use Claude, OpenAI, Gemini, smaller models, plain software, or a mix. We pick based on the quality, privacy, speed, and cost the workflow needs.
Who owns the code after launch?
You do. It lives in your repository, with deployment notes, docs, and a runbook your team can use.
How long does an engagement take?
A sprint is one week. Most first releases go live in four to eight weeks. Work across several systems usually takes two months or more.
Can you stay involved after launch?
Yes. We can hand over fully, stay on for monitoring and maintenance, or remain the team that runs the system.
galactus / contactaccepting projects
Send us the workflow your team keeps working around.
Tell us the steps, the systems involved, and where it stalls. We'll tell you what we'd test first and whether an embedded team fits.