Frame
Choose one business outcome and define how we will know it improved.
Operating model
Close to the problem. Accountable for the outcome.
FDE is useful when the specification is incomplete because the real problem only becomes visible inside the workflow, the data and the systems.
Observe
Sit with the people doing the work and inspect the systems that make the process real.
Embed
Work in the existing repo, cloud, tools and delivery rhythm wherever practical.
Ship
Put working software in front of users early and keep the deployment moving weekly.
Measure
Compare the result with the baseline: time, error rate, throughput, conversion, cost or another agreed metric.
Transfer
Documentation, tests, infrastructure and operating knowledge remain with the client.
Principles
What stays true between projects.
Accountable senior-led delivery
The people closest to discovery stay close to implementation, without a sales-to-delivery handoff.
Production before theatre
A prototype is useful only if it teaches us how to reach the operating system that follows it.
Your environment
The work should fit your architecture and governance, not force the company into a private agency black box.
Measured outcomes
A deployment should have a before and after, not just a list of completed tickets.
No artificial dependency
Source, infrastructure, accounts, tests and documentation stay under client ownership.
Fixed scope where possible
When a problem is bounded, price and scope it. When learning is the work, use an embedded engagement with explicit goals.
Who leads delivery
Accountability sits with people who have done this inside real organisations.
Enable FDE engagements are led by our founder, with more than seven years delivering backend systems, data platforms and analytics inside financial services, automotive and aerospace programmes and professional services.
That background includes owning a roadmap with direct reports, working alongside Risk, Credit and Trading stakeholders, and moving a data function out of Operations into Technology so the client team kept ownership afterwards.
It also includes building and operating MyEscapePlan in production: AI decisioning, data pipelines, consumer and advisor applications, APIs, SDKs, third-party integrations and AWS infrastructure.
Have a problem that is hard to specify from outside?
That is exactly where embedded engineering is useful.
Discuss a deployment