01ASTRA
ASTRA FDE
Senior engineer embedded. Builds and ships production code. Owns outcomes. Stays through operate. Multi-model. Productised IP. AIFT research bench behind them.
FDE
Forward
Deployed
Engineer.
We ship production code, not slides — embedded with you, on your problem. The delivery model that powers all four ASTRA pillars.
What it is
An engineer who joins your team, learns your work, and ships.
A Forward Deployed Engineer is a senior engineer who embeds with your team — on-site or near-fully integrated — to learn your workflow, your data, and your decision-making context. Then they build the production AI and data systems that fit.
The deliverable is not a deck. It's working code that runs in your environment — agentic systems, RAG pipelines, evaluation harnesses, production observability, integration with your existing stack. Built with you, not for you, and then handed over fully — without handing off the people who built it.
This is how Palantir's customers shipped Foundry in production. How Anthropic and OpenAI now deliver Claude and ChatGPT inside the enterprise. And how ASTRA has been delivering AI and data systems for clients in Asia since founding.
The ASTRA FDE
Five principles. Locked.
The FDE category is broad. The ASTRA FDE is specific.
- 01
Senior-only
No pyramid. No associate-led delivery. Every ASTRA FDE is a senior engineer or senior architect with production AI/data experience — the people who can both write the code and earn the room's trust on the strategic call.
- 02
R&D-backed
An ASTRA FDE is not a lone contractor. Behind every engagement sits AIFT — the only InnoHK-recognised FinTech research lab, co-founded by CityU, Columbia, and Tsinghua — and a 60+ engineer research bench at Hong Kong Science Park.
- 03
Productised IP
We don't start from a blank repo. Every engagement deploys against ASTRA QA, Forge, and Gate. The IP compresses time-to-production — same FDE depth, fewer days to live.
- 04
Multi-model
We're not locked to one vendor's model. Anthropic, OpenAI, Google, open-source — we deploy whichever combination fits your constraints. The FDE brings engineering judgment, not a sales bias.
- 05
No handoff
The team that builds it ships it. The team that ships it operates it. Same Slack, same engineers, same accountability — through Diagnose, Design, Build, Ship, and Operate.
What we ship
From diagnosis to live.
Every FDE engagement runs through the same five stages. Timing depends on scope; depth is locked.
- 01
Diagnose
We sit in the workflow. Map the data, the existing systems, the decision frames. Name the highest-leverage problem precisely. Output: a one-page diagnostic with the proposed agentic / data system to build.
- 02
Design
Architect the solution end-to-end: model selection (multi-model), agent topology, data pipelines, eval harnesses, deployment surface, governance hooks. Output: a system design + scope sketch you approve before we commit.
- 03
Build
Production code on day one. Agents, RAG pipelines, eval frameworks, integration, observability. ASTRA QA / Forge / Gate are deployed where they fit — accelerators, not lock-in. Iteration cycle is days, not weeks.
- 04
Ship
We move it into your environment. Real users, real data, real load. Cutover plan, rollback plan, governance sign-off. The engineers who built it are in the room on launch day.
- 05
Operate
Same engineers stay — incident response, regression eval, drift detection, iterative tuning, governance reporting. This is the Pillar 04 Agentic Operations motion: shipped systems that keep working.
Why this model
Why FDE — and why for AI / data specifically.
The big-firm consulting model was designed for slide-decks and PowerPoint-grade transformation. AI and data work doesn't survive that model. It needs senior engineers in the room, shipping code, owning outcomes.
02
Big-firm consulting
Pyramid teams led by senior partners and staffed by associates. Decks and slideware. Hand-off at "go-live" to a different team. Often paid by the hour, not the outcome.
03
Off-shore engineering
Strong coding capacity. Weak on domain context, governance, and AI/data product judgment. Limited ability to own outcomes. Communication and time-zone friction on iterative AI work.
04
Internal hire
The best long-term answer for stable workloads — but slow to staff (12–18 months for a senior AI engineer in Asia), expensive to scale, and hard to attract top talent for a single use case. ASTRA FDEs are often a bridge until your internal team is ready.
We're not better than Palantir, Anthropic, or OpenAI. We're the FDE practice anchored in Asia, multi-model, focused on AI and data.
Lineage
The category. And ASTRA's place in it.
~2010
Palantir
Invented the Forward Deployed Engineer role. Embedded senior engineers shipping Foundry and Gotham deployments alongside government and Fortune-500 customers. The model drove much of Palantir's commercial success.
May 2026
Anthropic & OpenAI
Both launched dedicated enterprise-AI joint ventures explicitly built on the FDE playbook. Anthropic's $1.5B venture with Blackstone, Hellman & Friedman, and Goldman Sachs. OpenAI's $4B raise at a $10B valuation from TPG, Brookfield, Advent, and Bain. The category went mainstream for AI in the same month.
Since founding
ASTRA
Delivering AI and data systems this way for clients in Asia. Anchored in Hong Kong. Backed by AIFT's 60+ engineer research bench. Multi-model by design. Productised on top of ASTRA QA · Forge · Gate. We didn't invent the FDE model. We've been quietly applying it to the AI/data work that needs it most.
Palantir, Anthropic, OpenAI, Foundry, Gotham are trademarks of their respective owners. Cited here as category lineage only; no partnership, equivalence, or comparison is implied.
Start
Embed an ASTRA FDE.
Tell us the problem, the data, and the deadline. We'll sketch the system to build and which ASTRA FDE leads the engagement.
FDE team
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