The best way to adopt AI is not to buy a deliverable — it is to build one with your own team. That is the idea behind the forward-deployed engineer (FDE): an AI engineer who works inside your organisation, shipping working software alongside your people. It is the delivery model that pairs with our AI consulting, and it is how capability actually transfers.
Two ways to get AI built
What a forward-deployed engineer does
Rather than gathering requirements and disappearing, an FDE sits with your team and builds — the automation, the integration, the agent — solving real problems in your real environment. The FDE role has become one of the most sought-after in AI precisely because it ships results; we wrote about it as a career in the AI role nobody trained for.
Why embedded beats outsourced
A remote vendor deliverable arrives late and leaves you dependent. An embedded engineer ships in weeks and, crucially, transfers the skill as they go — your team watches, learns and takes over. When the engagement ends, the capability stays.
What we build
AI automations and agents, integrations between your systems and LLMs, retrieval-augmented (RAG) assistants over your own data, and custom workflows in n8n, Claude and Copilot — prioritised by whatever moves your highest-value use case fastest.
Capability that stays
Every embedded engagement includes HRDC-claimable upskilling for the team working alongside our engineer, so the knowledge is designed to remain in-house. For the developer skills behind the role, see our AI Engineering training.
How an embedded engagement actually runs
A forward-deployed engagement is deliberately hands-on. The engineer works with your people — pairing on the first automation, wiring up the integration, building the RAG assistant over your data — while your team watches, asks and gradually takes the keyboard. By the end they are not holding a manual; they have shipped something and can ship the next thing. Engagements are scoped to fit SMEs as well as enterprises, and the upskilling built in is HRDC-claimable, which is what makes embedded AI engineering realistic for Malaysian mid-market teams, not just large ones.
What an embedded FDE actually brings
We hold our forward-deployed engineers to the same dual mandate the frontier labs do: real engineering depth and ownership of the business outcome. One without the other is a vendor, not an FDE. This is the competency matrix we hire and staff against.
The FDE Dual-Mandate Competency Matrix
An embedded engineer is only worth embedding if they clear a bar on both axes at once.
Technical Engineering
Build the thing
- Production code — Python plus one systems language
- LLMs & agents in production — prompting, RAG, evals, tool use
- Data-flow & pipeline design for messy, real data
- Integration, CI/CD & deployment inside your stack
- Debugging live, in your real environment
Outcome Ownership
Make it land
- Frame your actual problem, not the ticket that was raised
- Talk to a CTO and a CFO in the same meeting
- Design for adoption — a tool nobody uses is a failure
- Measure business value — time-to-value, cost, cycle time
- Leave the capability behind — your team can run it
How we measure a successful engagement
We do not report activity (“hours spent, tickets closed”). We report against outcomes. This is the scorecard we agree with you before a single line of code is written — the same output-to-outcome shift documented by Databricks and The Pragmatic Engineer.
The FDE Outcome Scorecard
Five signals that separate a deployment that “went live” from one that actually worked.
Time-to-Value
Shipped on the plan date
Value showing up in weeks, not quarters
Adoption
Delivered to spec
Your people use it daily, unprompted
Business Value
On budget
Measurable cost / cycle-time reduction (RM)
Capability Transfer
Handed to support
Your team can run & extend it after we leave
Product Feedback
Closed the ticket
What we learn feeds your next build
Where the model comes from: the forward-deployed approach was pioneered by Palantir and is now used by OpenAI, Anthropic, Google and Salesforce. For a neutral overview see Wikipedia's definition, and for how the metrics differ from a normal software role, Rocketlane's breakdown.