Engineers who sit where the problem lives.
Forward deployed engineering is how leading AI companies turn powerful models into working products inside customer organisations. Our FDE pods bring that model to your enterprise: hands-on engineers who learn your operations, ship weekly and leave your team stronger.
Week 1
Pod embedded and shipping
Weekly
Demos to business owners
Build → transfer
Your team owns what we build
What is a forward deployed engineer (FDE)?
A forward deployed engineer is a software engineer who works directly inside a customer's organisation rather than on a central product team. FDEs combine full-stack and AI engineering with consulting skills: they understand the user's workflow first-hand, build and integrate solutions on site, and feed real-world lessons back into the product. The model was popularised by data and AI platform companies and is now the fastest path from AI pilot to production.
Senior engineers who embed with your teams, sit with your users and ship AI into real workflows, then hand the capability over to you.
Our Forward Deployed Engineering capabilities.
FDE pods
A lead FDE, AI engineers and a product strategist embedded with one business unit, accountable for shipped outcomes.
User-embedded discovery
Shadowing operators and analysts to find where AI genuinely removes friction, not just where it impresses.
Integration engineering
Connecting models and agents to legacy systems, data warehouses, identity and approval flows.
Rapid prototyping to production
Clickable prototypes in days, production releases in weeks, behind the same quality gates as your core software.
Build-operate-transfer
Pairing, documentation and enablement so your engineers can run and extend the system independently.
AI platform partner deployments
Hands-on deployment and customisation of enterprise AI platforms and model-provider solutions.
Where it pays back first.
High-value starting points we see across industries. We will help you rank them against your own data, systems and risk appetite.
01
Stalled AI pilots
Rescue proofs-of-concept stuck before production and get them live.
02
AI-native products
Add copilots and agents to SaaS products with a dedicated pod.
03
Platform roll-outs
Deploy enterprise AI assistants with real integrations and adoption.
04
Regulated operations
Embed engineers who work within your security and compliance model.
A clear path to production.
Embed
Pod joins your team, tools and rituals within the first week.
Learn
Sit with users, map workflows and agree measurable targets.
Ship
Weekly releases to real users with evals and feedback loops.
Transfer
Hand over code, runbooks and skills; stay on as advisors if needed.
Tools we ship with.
Selected on merit for your context. We are model-, cloud- and vendor-agnostic.
- TypeScript
- Python
- Next.js
- React
- FastAPI
- Go
- Claude
- GPT
- Gemini
- LangGraph
- MCP
- Postgres
- pgvector
- Databricks
- Snowflake
- Kubernetes
- Terraform
Forward Deployed Engineering: your questions answered.
A typical pod is a lead forward deployed engineer, one to three AI or full-stack engineers and a part-time product strategist, with architecture and security specialists on call. Pods are sized to the workflow and can work on site, hybrid or remote.
Most engagements run in 12-week cycles with a defined outcome per cycle. Many clients keep a pod for multiple cycles as they expand AI to adjacent workflows, then transition to their in-house team.
Yes. Engineers work within your identity, device and data policies, use your repositories and environments, and follow your change-management process. We are comfortable in regulated industries such as financial services and healthcare.
Ready to put Forward Deployed Engineering to work? Start with one workflow.
Book a 45-minute strategy session with a Vidura principal. You'll leave with a shortlist of high-ROI AI use cases, the risks to manage, and a realistic path to production.