Forward deployed engineers who ship AI to production.
Forward deployed engineering (FDE) means a small senior team embeds inside your business, learns how work actually happens, and builds AI systems on your own data and tools — then stays to run and improve them. Techcream FDE pods do exactly that, from first workflow to production, without you hiring an in-house AI team.
The model the AI leaders just bet billions on
Most AI projects stall between demo and production: messy data, legacy systems, unclear owners. Palantir pioneered forward deployed engineering to close that gap, and in 2026 OpenAI, AWS and Anthropic all launched FDE programmes of their own. Those programmes target the largest enterprises. Founder-led companies and mid-market teams need the same embedded model — at a size and price that fits.
How an FDE pod plugs into your business
The pod works inside your environment, on your systems, against a metric you agree on.
Your goal
- Cut manual ops
- Faster support
- More pipeline
- Inside your business
Embed & discover
- Shadow teams
- Map workflows
- Audit data
Build on your stack
- Agents
- RAG assistants
- Automations
Your systems
- CRM / ERP
- Docs & drives
- Databases
- Slack / email
Outcome
- Live in production
- Measured weekly
- Your team trained
Everything needed to go live.
A dedicated pod
A lead FDE plus 1–3 senior engineers who work in your Slack, your repos, and your standups.
Workflow discovery
We sit with the people doing the work, map the bottlenecks, and pick the use cases with real ROI.
Production AI systems
Agents, RAG assistants, and automations integrated with your CRM, ERP, docs and databases.
Evaluation & guardrails
Test sets, accuracy tracking, cost budgets and human-in-the-loop checkpoints from day one.
Runbooks & handover
Documentation and training so your team can own the system — or keep us on to run it.
Ongoing improvement
Monthly iteration on accuracy, coverage and cost as your business changes.
From first call to production.
- 1
Week 1 — Embed & discover
Shadow teams, audit data and systems, shortlist 2–3 high-ROI workflows.
- 2
Weeks 2–3 — Prototype on real data
Working prototype in your environment, measured against a baseline.
- 3
Weeks 4–6 — Ship to production
Integrations, guardrails, monitoring, and rollout to real users.
- 4
Month 2+ — Operate & expand
Improve accuracy and cost, then move to the next workflow.
Where it pays off first.
- ✓Automating document-heavy back-office work (invoices, claims, onboarding)
- ✓AI agents that triage and resolve support tickets
- ✓Internal knowledge assistants over contracts, SOPs and wikis
- ✓Sales and GTM automation wired into your CRM
- ✓Rescuing an AI pilot that never made it to production
We've built this before.
GrowthAsist
We run our own AI product in production — a fine-tuned LLM that learns each user's voice.
Askspot
Built a no-code platform for agencies to launch custom AI chatbots trained on client data.
Blurbb.ai
AI tool that automatically collects and summarises content from multiple sources.
Why teams choose Techcream for FDE Pods.
We build products, not just projects
Running our own SaaS means we think about adoption, cost and maintenance — not just delivery.
Senior-only pods
No juniors learning on your time. The people on the call are the people writing the code.
India-based, globally experienced
We've shipped for founders in the US, Australia and India — with enterprise-grade engineering at a sensible cost.
Outcome-led engagements
We agree on the metric that matters before we write code, and report against it.
| Techcream FDE pod | Hiring in-house | Big consultancy | |
|---|---|---|---|
| Time to first production system | Weeks | Months (hire first, then build) | Months of discovery |
| Who writes the code | Senior engineers, embedded | Your new hires | Often subcontracted |
| Commitment | Monthly, scale up or down | Full-time salaries | Large fixed SOWs |
| Product experience | We run our own AI SaaS | Varies | Rarely |
FDE Pods, explained.
A forward deployed engineer (FDE) is a software engineer who embeds directly with a customer to understand their workflows and build production systems on their data — combining engineering, product and consulting in one role. The term was popularised by Palantir.
Outsourcing delivers to a spec. An FDE pod works inside your business to find the right problem first, builds on your real systems, and stays accountable for the outcome in production.
Most pods ship a first production workflow in 4–6 weeks, then continue month-to-month to expand coverage and improve accuracy and cost.
Yes. We build on your cloud, your databases and your tools — OpenAI, Anthropic, open-source models, AWS, Azure or GCP — and keep data inside your environment where required.
Yes. Our team in India works with founders in the US, Australia and Europe, with overlap hours for standups and a written async rhythm for everything else.
Let's scope your FDE Pods project.
Tell us about the workflow or problem. We'll reply within 24 hours with initial thoughts and a time to talk — no sales pitch.
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