Forward deployment

Our engineers, inside your team.

Senior engineers join your standups, learn the operation well enough to argue about it, and ship into production. Wrong assumptions die in days.

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Two people working side by side at a desk, seen from behind

Service overview

Forward deployment means the people building the system sit with the people who use it. Our engineers join your standups, get accounts in your systems, work in your repositories and ship on your release train, for the duration of the engagement.

It exists because the expensive unknowns in enterprise AI are organisational, not technical: the exception nobody documented, the approval that takes three days, the field that means two different things in two systems. Those surface in week one when someone is in the room, and in month four when they are not.

It is also the delivery model underneath our other services. Whether the output is an agent platform, an integration layer or an internal tool, the way it gets built is the same.

Talk to us

Want to meet the pod?

Tell us the problem and we will bring the two engineers who would actually be embedded, not an account manager.

Schedule call

What we build

Standing up a first AI system

From nothing to a workflow running on live work, with your engineers on the commits throughout.

Rescuing a stalled pilot

Diagnosing why it never reached production, then either fixing it or saying plainly that it should stop.

Enabling your team

Pairing, code review and internal workshops so your engineers can build the next one without us.

Platform foundations

The shared tooling, evals and deployment path that the next five workflows will reuse.

Performance and cost work

Latency, throughput and spend on systems already live but not yet economical.

Delivery under audit

Building where a regulator, auditor or security team needs evidence for every decision.

Migrations and replatforming

Moving a working system to new infrastructure or a new model without a big-bang cutover.

Incident and reliability work

Systems that run but wake people up, traced, stabilised and handed back with runbooks.

Interim engineering leadership

Standing in as the senior technical voice while you hire the person who will own it.

Proof of concept, honestly scoped

A two-week answer to a specific technical question, with a written recommendation either way.

Codebase assessment

Reading what you already have and telling you whether to extend it or replace it.

Process redesign with the build

Changing the process where that is the real fix, rather than automating a bad one faster.

Why Momentem for this

About the company →

1 wk

to a working slice against your real data

Because the engineers are in the room, the first version runs on genuine records inside week one. Ugly, but real, and arguable.

Senior only, named

Two to four engineers who have shipped this before. No pyramid, no rotating roster, no juniors billed as seniors.

Speed from proximity

Sitting with your operators removes the requirements round trip. Wrong assumptions die within days of being made.

Your team ships too

Your engineers work alongside ours in the same codebase, which is what makes handover real rather than a document.

Bad news early

The most valuable thing we sell is an honest week two. Sunk cost is not a delivery strategy.

A defined ending

Success is your team changing the system without us. Engagements are designed to end, and we say when they should.

Operators, not advisors

Everyone in a pod has run software they built in a business that depended on it, including our own.

Weekly demos, no surprises

Every week ends in something running against real data, so nothing waits until a reveal at the end.

How this compares

Momentem

Large consultancy

Off-the-shelf tool

Who is on the ground

2 to 4 named senior engineers

Mixed pyramid, seniors part-time

No team

Learning your business

Embedded from week one

Discovery phase, then remote build

Not applicable

Where the code lives

Your repositories

Sometimes theirs first

Nothing to own

Accountability

Outcome for one workflow

Hours and deliverables

Uptime SLA

Knowledge after exit

With your team, by design

Often leaves with the team

With the vendor

Contract shape

Fixed scope, defined end

Master agreement, change requests

Rolling licence

Based on publicly available information and our own experience of comparable engagements. Generalisations rather than claims about any specific provider. There are good exceptions in every column, and the point is where each model is structurally strong.

The stack we work in.

Languages

We work in yours rather than standing up a parallel stack alongside the one you already maintain.

Cloud and platform

Your accounts, your pipelines and your deployment path, so nothing has to be migrated later.

AI tooling

Chosen per task and benchmarked against your own eval set rather than a public leaderboard.

Ways of working

Your standups, your review standards and your tracker. We join how you already work.

How the build runs.

Week 0

Scope

Sessions with the people doing the work. We leave with a written target and a data map.

Week 1 to 2

Prove

A thin slice against your real records, scored on a test set drawn from your own history.

Week 3 to 12

Deploy

Integrations, approval gates, audit logging. Live on one team with a rollback switch.

Ongoing

Hand over

Runbooks, paired on-call and a decision log, until your team changes it without us.

Two people marking up a diagram on a whiteboard, one pointing

Meet the engineers who would be embedded.

Forty-five minutes with the people who would build it. No pitch, and a straight answer on whether it is worth building.

Schedule call

Questions

All questions

Hiring is the right answer eventually, and our engagements are designed to end in your team owning the system. Forward deployment gets you there faster, with people who have already made these mistakes elsewhere.

One engineer with production access and one operator who does the work today, roughly a fifth of their time. That is the real constraint, not budget.

Yes, and usually that is right. We follow your conventions, your review standards and your release process rather than standing up a parallel stack.

Both. On-site for the first weeks where it matters most, then in-timezone with regular on-site days. For air-gapped work, on-site throughout.

Runbooks, decision records, paired on-call, and a deliberate test: your engineer makes a change, and the eval set catches a regression they introduced on purpose.