FDE —
engineers deployed to where the problem lives
Forward Deployed Engineering was pioneered at Palantir and is now the delivery model shared by the world's leading AI companies. Its core belief is simple: the answers to enterprise AI live in the field, not the conference room.
Why traditional models
can't deliver enterprise AI
Traditional consultants understand the business but don't write production code. Product companies have the tech but never enter your workflow. Enterprise AI sits exactly in between: it needs deep business understanding and serious production engineering — both, not either.
FDE solves both with one role: engineers embed in your team, see the problem first-hand, build the system with their own hands, and own the production outcome. Translation loss goes to zero; delivery cycles compress from quarters to weeks.
Three models, one comparison
| Traditional consulting | Generic AI products | MATRIXLABS FDE | |
|---|---|---|---|
| Delivery model | Decks and proposals | Standard features, integrate yourself | Embedded, on-site engineering |
| First working system | 3–6 months | Depends on internal resources | 14 days (on real data) |
| Business understanding | Interviews and surveys | Almost none | On-site co-creation, deep in the workflow |
| Production accountability | Handed to the client | Client's own responsibility | End-to-end, through production launch |
| Capability retention | A report | Vendor lock-in | Platform + enablement; capability stays with you |
Five phases, measured in weeks
Every phase has explicit deliverables and exit criteria — you always know where the project stands and what comes next.
Diagnose
Define the use case, business value, data feasibility, and success metrics.
- Deliverable: scenario value map
- Deliverable: success-metric definition
Embed
FDEs join the customer team to understand processes, systems, and how work actually happens.
- Deliverable: on-site process inventory
- Deliverable: data feasibility assessment
Build
Select MatrixLabs products and build the business capabilities the use case requires.
- Deliverable: real-data prototype
- Deliverable: scenario eval baseline v1
Ship
Complete evaluation, system integration, safety guardrails, and production deployment.
- Deliverable: production launch
- Deliverable: audit & compliance docs
Transfer
Transfer code, documentation, evaluation sets, and the operating playbook.
- Deliverable: scenario expansion blueprint
- Deliverable: team enablement plan
Anatomy of an FDE pod
Small but complete: one pod carries everything from business understanding to production operations, and owns the outcome directly.
Forward Deployed Engineers
Embedded at the client site — understanding the business, building the system, owning the result. The pod's core engine.
Solutions Architect
Designs the overall solution and integration path, holding the line on security, compliance and architectural quality.
Platform Engineer
Backs the front line with Matrix OS: connectors, environments and platform capabilities on call.
Delivery Lead
Owns cadence and priorities, manages risk, and makes sure every week ships verifiable progress.
Six delivery principles
Ship in weeks
Demonstrable progress every Friday. No “80% in progress” — only “live” or “not live”.
Field first
On site for the critical phases. One field observation beats ten requirement documents.
Validate on real data
Prototypes run real data from day one. Success on synthetic data proves nothing.
Production is the only bar
A demo isn't delivery. Only systems the business relies on daily count.
Transparent and auditable
Progress, eval scores and costs are fully visible to the client; every decision leaves a record.
Knowledge transfer built in
Paired with your team from week one. By project end, the capability lives in your organization.