The pilot works, but nobody can use it
The prototype behaves in isolation. It still needs integrations, permissions, failure handling, and a place in the daily workflow.
Forward-deployed AI engineering inside your stack.
We join the people who know the work, map one valuable workflow, and build it against the systems already in use. The code and documentation stay with your team.
4–8wk
Typical first release
A focused build can usually reach users within two months.
Your stack
Built where the work happens
We start with your codebase, tools, data, and permissions.
Your repo
No permanent dependency
Your team keeps the code, documentation, and runbook.
The engineer who maps the workflow also ships the system.
A forward-deployed engineer works inside the client environment. They learn the job from the people doing it, trace the edge cases, and build against real data and permissions.
One small team handles scoping, product decisions, engineering, integrations, and rollout. There is no handoff from a strategy team to a delivery team that has to learn the problem again.
When an embedded team makes sense
Bring us in when the opportunity matters but your team does not have the time or context to carry it into production.
People are the integration layer
Someone keeps moving information between the CRM, spreadsheets, email, payments, support tools, and internal databases.
The roadmap is already full
The work has a clear owner and business case, but product and engineering cannot pull away from the core roadmap.
The workflow still needs judgment
A person still needs to approve important decisions, handle uncertain cases, and understand why the system acted.
From observation to production
The same small team follows the work, builds the system, and stays through rollout.
- / 01
Follow the work
Trace the inputs, decisions, exceptions, and handoffs with the people doing the job.
- / 02
Set the boundary
Agree on what should change, which systems are involved, where a person stays in control, and how we will judge the result.
- / 03
Build in your environment
Work in your repository, connect the existing tools, and test the awkward cases before launch.
- / 04
Launch and transfer
Roll it out with the people using it, fix the rough edges, document the system, and hand over ownership.
What stays with your team
The exact list depends on the workflow. A typical handoff includes:
- A documented workflow, scope, and success measure
- Production code committed to your repository
- Integrations with the systems and data already in use
- Model evaluations, deterministic checks, and failure handling
- Human review, exception, and escalation paths
- Monitoring, deployment notes, and a practical runbook
Built to be owned by your team
We work inside your environment and avoid choices that make us a permanent dependency.
Use the systems you already have
A sound ERP, CRM, warehouse, or internal platform stays. We build around it instead of turning the project into a replatform.
Make uncertainty visible
Review paths, confidence thresholds, and logs show when the system needs a person.
Work that makes a good first project
Look for repeated work with a visible cost and an owner who knows what good output looks like.
Operations reporting
Pull data from the source systems, apply company rules, prepare the report, and flag unusual cases for review.
Client onboarding
Bring documents, messages, approvals, and brittle automations into one flow with a visible state.
Research operations
Capture results as work finishes, prepare them for analysis, and remove the nightly spreadsheet handoff.
Document and approval workflows
Read incoming material, check it against the rules, prepare the next action, and ask a person when judgment matters.
Choose the smallest useful scope
Start narrow enough to launch and learn. Expand when the result is working.
sprint1 week
$5,000
Map one workflow and ship a narrow working slice or technical proof in your repository.
mvp~1 month
From $20K
Build and deploy one end-to-end workflow, with product design, documentation, and handover.
scale-up2+ months
$75K–$250K
A dedicated team for work that spans several systems, teams, or higher-risk operations.
Questions we usually get
How is an FDE different from an AI consultant?
A forward-deployed engineer is responsible for the working system. The engagement includes technical scoping, engineering, integrations, testing, and rollout, rather than ending with recommendations.
Do your engineers work in our existing stack?
Yes. We start with the codebase, tools, data, permissions, and workflows already in use. We change only what the new system needs.
Are you tied to one AI model provider?
No. We use Claude, OpenAI, Gemini, smaller models, ordinary software, or a mix. We choose based on quality, privacy, speed, and cost.
Who owns the code after launch?
You do. The code lives in your repository, with deployment notes, documentation, and a handoff your team can use.
How long does an engagement take?
A focused sprint takes one week. Most first versions take four to eight weeks. Work across several systems usually takes two months or more.
Can you stay involved after production?
Yes. We can hand over completely, stay for monitoring and maintenance, or remain the engineering team responsible for the system.
Show us the workflow your team keeps working around.
It can be a manual process, a stalled pilot, or a handoff between systems. We will tell you what we would investigate first and whether we are a good fit.