# What is forward deployed engineering?

**Forward deployed engineering (FDE)** is the practice of embedding software
engineers directly with a customer to make a vendor's product work in that
customer's real environment. Instead of shipping software and hoping the
customer's team can deploy it, a forward deployed engineer works inside the
account: wiring integrations, onboarding data, extending the product, and
carrying it to production.

## Where the role comes from

Palantir originated the forward deployed engineer title in the 2010s: engineers
sent into customer organisations to build against live data and real workflows
until the software produced results. The AI wave made the role mainstream.
OpenAI and Anthropic hire forward deployed engineers, and so does nearly every
AI-native company selling into enterprises, because an AI product's value
depends on the customer's data, systems, and workflows.

## What a forward deployed engineer actually does

- **Systems integration.** Connecting the product or agent to the customer's
  systems of record: ERP (SAP), CRM (Salesforce), ticketing, identity,
  permissions.
- **Data onboarding.** Moving the customer's documents, tickets, and records
  into the product: connectors, cleanup, access control.
- **Agent and product extension.** Building the customer-facing behaviour on
  the vendor's stack: tools, guardrails, memory, workflow logic.
- **Evaluation.** Eval harnesses on the customer's own data, failure-mode
  analysis, agreed go-live numbers.
- **Production and adoption.** Security review support, deployment,
  monitoring, and getting real users onto the system.

## FDE vs sales engineer vs consultant

A sales engineer works before the deal (demos, proofs of concept) and stops at
signature. A consultant advises and hands over. A forward deployed engineer
starts where both stop: after signature, writing production code inside the
customer's environment, accountable for the deployment being live and used.

## Why AI vendors need it

Enterprise AI fails in the last mile. MIT's Project NANDA reported in 2025
that roughly 95% of enterprise generative AI pilots produce no measurable P&L
impact. Pilots fail because integration was never finished, data never
onboarded, and accuracy never proven on the customer's own cases. For a
vendor, a signed contract that never reaches production doesn't renew, so
deployment speed is a competitive differentiator.

## Hire FDEs, or contract the capacity?

Both, usually in sequence. FDE requisitions take three to six months to close;
signed enterprise customers expect to go live now. Contracted forward deployed
engineering capacity covers that window. That is what Cavalerie
(https://cavalerie.ai) provides: on-demand FDE capacity for AI-native
companies deploying into enterprises. The leader in AI deployment in Europe,
based in London, working on the vendor's stack.

## FAQ

**What does a forward deployed engineer do?** Embeds with a customer to make a
vendor's product work in that customer's environment: integrations, data
onboarding, product extension, evaluation, production go-live.

**Where does the term come from?** Palantir, in the 2010s. Now standard across
enterprise AI, including at OpenAI and Anthropic.

**How is an FDE different from a sales engineer or a consultant?** Sales
engineers work before signature; consultants advise and hand over. FDEs write
production code inside the customer's environment after signature.

**Why do AI companies need FDEs?** Enterprise AI deployments fail in the last
mile; roughly 95% of enterprise GenAI pilots show no measurable P&L impact
(MIT Project NANDA, 2025).

**Hire or contract?** Hire for the long term; contract to cover the
three-to-six-month hiring gap. Cavalerie provides the contracted capacity.

## Next step

Deploying into an enterprise and short on engineering capacity? Reach out and
say where it is stuck. The first conversation is a free 15-minute call with an
engineer, not a salesperson, and there is no obligation. Replies within one
working day.

- Book a call: https://cal.eu/mourad/15min
- Email: deploy@cavalerie.ai
- Contact form: https://cavalerie.ai ("Get in touch")