Implementation
AI Forward Deployed Engineering
AI Forward Deployed Engineering is an embedded engineering engagement that takes AI from experiment to operating workflow inside a company’s real systems. The engagement follows eight stages: Discover, Audit, Capture and Align, Design, Deploy, Govern, Measure, Optimize.
AI Forward Deployed Engineering
When a company needs this
The usual sequence is that AI adoption outruns an organization’s ability to operationalize it. A prototype works. Everyone agrees it should be in production. Then it meets the parts of the company a prototype never had to touch: the systems that hold the data, the people who own the decisions, the process that has to keep working on the day the model is wrong.
That gap is not a modelling problem, and it is rarely solved by a better prompt. It is an engineering and organizational problem, and it is solved by someone working inside the company rather than delivering to it.
What actually changes
The gap is not a modelling problem, and it is rarely closed by a better prompt. It is an engineering and organizational problem, which is why it is closed from inside the company rather than delivered to it.
Before
- The prototype works, on an extract someone prepared
- The systems that hold the real data are not connected to it
- Nobody has said who is allowed to approve what
- Nothing is designed for the day the answer is wrong
The implementation gap
Everything a prototype was allowed to assume, and production has to prove.
After
- Shared business context, written down rather than held by one person
- Connected systems, with the workflow running against the real ones
- Declared decision rights — what AI may do, and what needs a person
- A record of what actually happened, not just where things ended up
- Measured against the outcome agreed at the start
The eight stages
One sequence, run in order. The later stages are the ones that decide whether the work survives contact with production.
- 01
Discover
Understand the business, the workflow, and what a good outcome actually looks like to the people doing the work.
- 02
Audit
Establish what systems, data and decisions exist today — as they are, not as documented.
- 03
Capture & Align
Capture the shared business context and align on decision rights: who decides what, and what AI may do without asking.
- 04
Design
Design the workflow around that, including what happens when the system is wrong.
- 05
Deploy
Put it into the real environment, connected to the real systems.
- 06
Govern
Rules, approvals and a record of every run, so the workflow can be trusted in production.
- 07
Measure
Measure against the outcome agreed in Discover, not against model metrics.
- 08
Optimize
Improve on evidence from real use, and hand over what the internal team will own.
What this work runs into
These are the shapes the problem takes. None of them is solved by a better model.
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The prototype works. Production does not.
The demo ran against an extract someone cleaned by hand. Production runs against the live system, where the same field means two things depending on which integration wrote it.
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Nobody agrees which system is the record.
Two systems hold a version of the same number and both are consulted. Deciding explicitly which one owns which question is most of the work.
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The smartest employee is a spreadsheet.
The number everyone plans against is assembled by hand, each month, by one person — and nothing else can reproduce it.
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The model answers, and nobody trusts it.
Without the basis for a recommendation and a record of what was done with it, an answer is an opinion the system happens to hold.
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Ownership of the decision is unclear.
Until someone states what AI may do without asking, the work cannot reach production however good the answers are.
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The work crosses systems and leaves no trail.
A task that touches four systems and ends in an email has no durable workflow — so it cannot be measured, improved, or handed over.
What the engagement produces
The output is an operating workflow, not a report and not a demo. That means it runs against your systems, a named person owns it, and there is a record of what it did.
What the AI is allowed to do is written down rather than left implicit: a person reviews and approves its suggestions wherever your rules require it, and every change that matters is recorded with how it looked before and after.
- A workflow running in the real environment
- Declared decision rights — what AI may do, and what needs a person
- A record of what actually happened, not just where things ended up
- Handover to the team that will own it
What this does not mean
It does not mean replacing your engineering team, and it does not mean a fixed deliverable handed over at a distance. The word "embedded" is doing real work in that sentence.
We have not published engagement lengths, team sizes, pricing or outcome figures on this page. Those depend on the engagement, and publishing a representative number would imply a typical case we have not evidenced.
Questions buyers ask
- What is an AI forward deployed engineer?
- An engineer who works inside the customer’s environment on the customer’s real systems, rather than delivering a specification from outside. There is a longer explanation of the role in our resources.
- Do we need to be an mPorts software customer?
- No. The engagement is about getting AI into your operating workflows. Whether mPorts software is part of the answer is a question the Discover and Audit stages exist to answer honestly.
- How is this different from a consulting project?
- The output is a workflow running in production with an owner and a record, not a recommendation document. Govern, Measure and Optimize are stages of the engagement rather than someone else’s follow-up.