Forward Deployed Engineers
An experienced FDE squad, embedded in your business until the AI works in production
Forward deployed engineering is how Palantir took software into the hardest organisations in the world, and it is how OpenAI, Anthropic and Google now take AI into enterprises. A forward deployed engineer is a senior engineer who works inside the customer's business rather than at a distance: sitting with the people who do the work, reading the real data, and writing the production code that connects AI to the systems already running the company. We offer that as a squad. A small, senior team joins your standups, your Slack and your cloud, finds the workflow where AI pays back first, and stays until it is live, measured and owned by your own people. It is not consulting, which ends at a recommendation, and it is not staff augmentation, which builds whatever it is told. An FDE squad is accountable for the result.
Most AI pilots never reach production. The gap is deployment, not the model.
About 95% of generative AI pilots show no measurable profit impact, according to MIT's 2025 NANDA study. That is why Palantir's forward deployed model is now how OpenAI, Anthropic and Google take AI into enterprises.
Where pilots stall, and what a forward deployed squad does about it:
- 01The pilot never touches real data→ We work in your environment from week one, with real access and real records.
- 02It is not wired into the tools people use→ We integrate with the CRM, ERP, inbox and phone system your staff already open every day.
- 03Nobody can say whether it is good enough→ Evals built from your real cases turn quality into a number before anything ships.
- 04Security and compliance stall the sign-off→ Permissions, audit logs and data residency are designed in, so approval is a review, not a rebuild.
- 05No one owns it after the demo→ The squad owns the outcome, then pairs with your team until they own it.
Not consulting. Not staff augmentation. Accountable engineering.
The four ways companies usually try to get AI working, and what each one actually delivers.
- Consultancy · workshops
- Staff augmentation · builds to your spec
- AI software vendor · a sales demo
- Consultancy · a report
- Staff augmentation · if specified
- AI software vendor · configures its product
- Consultancy
- Staff augmentation · if told how
- AI software vendor · its own connectors
- Consultancy · for the advice
- Staff augmentation · for the hours
- AI software vendor · for the licence
- Consultancy
- Staff augmentation
- AI software vendor · its own platform
- Consultancy · a roadmap
- Staff augmentation · if you plan it
- AI software vendor · lock-in
- Consultancy · after the report
- Staff augmentation · depends on your spec
- AI software vendor · after integration
Twelve weeks, from first standup to your team in charge
Drag through the weeks. Timings vary with data access and approvals; this is the shape most engagements follow.
Embed
The squad joins your standups and channels, gets access, and meets the people who do the work today.
- Access and a recorded baseline
- A ranked use-case map you approve
- A working prototype and eval baseline
- First AI workflow in production
- Second workflow, runbooks, your team in charge
Three to five senior engineers, built around your experts
No juniors learning on your project and no rotating bench. Your domain experts sit at the centre, because they know where the work really goes.
An experienced FDE squad, not a new team learning the role
We build and run production AI ourselves
We built our own ready-made AI calling platform, with compliance built in, plus AI automation for clients. Our squads deploy the same production practices.
Senior people only
Every squad member has shipped production systems with us. The person in your first meeting is the person writing the code.
Model-agnostic
Claude, GPT, Gemini or open-weight models, chosen on your test set, your security rules and your budget. No platform to sell you.
Evals and guardrails from day one
Quality is measured before release, every action is permissioned and logged, and costs have hard limits.
Your code, your cloud
Everything lives in your repositories and accounts from the first commit. Nothing is locked inside our tools.
Building since 2017
Clients in 12 countries so far, with squads working worldwide and daily overlap in your time zone.
- A senior FDE squad embedded in your team, your tools and your cloud, with daily overlap in your time zone
- A workflow map of where the time, cost and errors really go, ranked by payback, in the first two weeks
- A first AI workflow live in production against your real data, not a sandbox demo
- Integrations into the systems you already run: CRM, ERP, ticketing, telephony, documents and data warehouse
- Evals, guardrails, permissions and observability built in, so quality is a number and every action is traceable
- Measured results against the baseline recorded on day one: time saved, cost per case, conversion, error rate
- Runbooks, documentation and pairing, so your own team can run and extend everything we ship
- 01
Embed
The squad joins your team in week one: your standups, your channels, your repositories and the people who do the work today.
- 02
Discover by doing
We shadow the real workflow and read the real data, then rank use cases by payback and feasibility. No questionnaire, no strategy deck.
- 03
Ship to production
The highest-value workflow goes live behind flags with a human in the loop, wired into your systems and measured from the first day.
- 04
Harden and expand
Evals, monitoring and cost controls go in, then the squad repeats the pattern on the next workflow, team or site.
- 05
Hand over
Your engineers pair with ours until they own it. We stay on call, or step back entirely. Your choice.
A forward deployed engineer is a senior software engineer who works inside a customer's business, not at a distance. They sit with the people who do the work, read the real data, and write and deploy the production code that makes a product, usually AI, work in that customer's environment. Palantir created the role; OpenAI, Anthropic, Google and most AI companies now hire FDEs because AI fails far more often in deployment than in the model.
Consultants analyse and recommend, then leave before anything is built. Staff augmentation adds engineers who build whatever they are told. An FDE squad does the discovery itself, writes the production code, integrates it with your systems and is accountable for the business result, then hands it over so your team can run it.
AI models are now capable enough that most failures are deployment failures: the wrong data access, missing integrations, no evals, no owner. MIT's 2025 NANDA study reported that around 95% of generative AI pilots delivered no measurable profit impact. Companies want engineers who close that gap inside the business, and experienced FDEs are scarce and expensive to hire directly.
A typical squad is three to five senior people: a deployment lead who owns the relationship and the outcome, an AI engineer for agents, retrieval and evals, an integration engineer for your systems and data, and a reliability engineer for guardrails, monitoring and cost. Your domain experts are part of the squad from day one.
The squad is embedded in the first week and you get a ranked map of use cases within two weeks. A first workflow is typically in production, behind a human-in-the-loop, four to eight weeks after the start, depending on data access and approvals.
We are remote-first from India with agreed daily overlap in your time zone, which keeps a senior squad affordable. On-site visits for discovery workshops and go-live can be arranged where they help.
We are model-agnostic: Anthropic Claude, OpenAI, Google Gemini, Azure OpenAI, AWS Bedrock and open-weight models, on AWS, Azure or GCP. We choose against your own test set, security rules and budget, and keep the model layer swappable.
You do. Everything the squad writes lives in your repositories and your cloud, under your accounts, from the first commit.
FDE squads are priced per month for the size of squad you need, with a short scoping call first so you get a number before anything starts. Most engagements run for one to three months per workflow.
Your engineers have paired with ours throughout, and you get runbooks, documentation and the eval suite. You can keep a smaller squad on call for monitoring and model upgrades, extend to the next workflow, or run it entirely yourselves.