The pilot works, but nobody can use it
The prototype proves a capability. It still needs real data, permissions, integrations, evaluation, failure handling, and a place in the daily job.
Forward-deployed AI engineers who stay through the live release.
We work beside the people who know the job, build against the systems they use, and launch one production workflow. Your team keeps the code, documentation, and operating knowledge.
4–8wk
Typical production release
One focused workflow can usually reach users within two months.
Your stack
Built in your environment
We build with your codebase, tools, data, and permissions.
Your repo
A system your team can own
The code, evaluation cases, documentation, and runbook stay with you.
The people who learn the workflow are the people who build it.
A forward-deployed engineer starts with the work, not a product pitch. They follow real cases, find the rules and awkward exceptions, and build with the data, permissions, and systems already in place.
The same small team owns scope, product decisions, engineering, integrations, evaluation, and rollout. You do not pay for one team to write a strategy and another team to learn the problem again.
Use an embedded team when delivery is the constraint
The best fit is a valuable workflow with a clear owner, a visible cost, and no focused team to take it into production.
People are the integration layer
Staff keep copying information between the CRM, spreadsheets, email, payments, support tools, and internal databases.
The roadmap is already full
The workflow has an owner and a business case, but the product and engineering teams cannot leave 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.
One team from the first case to the first live release
Each stage removes a specific risk before the system takes on more responsibility.
- / 01
Follow the work
Trace a real case from input to outcome with the people who do the job. Record decisions, exceptions, delays, and handoffs.
- / 02
Set the boundary
Choose one path, name the systems involved, keep a person in control where needed, and define what success means.
- / 03
Build in your environment
Work in your repository, connect the real tools early, and test ordinary, difficult, and unsafe cases before launch.
- / 04
Launch and transfer
Release with a small user group, fix the missing cases, document the system, and transfer operating ownership.
The handover is a system your team can operate
The exact artifacts depend on the workflow. A normal handover includes:
- A workflow map, production boundary, exclusions, and success measures
- Production code committed to your repository
- Integrations with the systems and data already in use
- Versioned evaluation cases, deterministic checks, and failure handling
- Human review, exception, and escalation paths
- Monitoring, deployment notes, and a practical runbook
Build only what the workflow needs
A first release should solve the job without forcing a replatform or creating a permanent vendor dependency.
Use the systems you already have
A sound ERP, CRM, warehouse, or internal platform stays. We connect it to the new workflow instead of replacing it without a reason.
Make uncertainty visible
Evaluation results, review paths, thresholds, and logs show when the system needs a person.
Good first projects have a visible before and after
Choose repeated work that costs time or money and has an owner who can judge the result.
Operations reporting
Pull data from approved sources, apply company rules, prepare the report, and send 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 runs finish, 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.
Stage the budget around specific decisions
Start with a scope that can reach users and show whether a wider investment is justified.
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 to settle before the work starts
How is an FDE different from an AI consultant?
The practical difference is responsibility for delivery. A forward-deployed engineer normally scopes, builds, integrates, evaluates, and launches the system instead of ending with recommendations. Titles vary, so check the statement of work.
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. The choice follows the quality, privacy, speed, and cost requirements of the workflow.
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.
Send the workflow your team keeps working around.
Include the current steps, the systems involved, and where the work stalls. We will tell you what we would test first and whether an embedded team is the right fit.