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AI Deployment Sprint — 30 days to production

From problem
to production.

One focused month. One embedded engineer. One working system in your environment — not a proof of concept, not a demo, not a slide deck.

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The model

Not a project. An embedding.

Most AI consultancies work at a distance. They take a brief, disappear for several weeks, and return with a prototype that doesn't survive contact with your actual data, your actual infrastructure, or the people who have to use it every day.

We operate differently. Our Forward Deployed AI Engineers embed directly into your team — working alongside your engineers, your data people, and whoever actually owns the problem. We learn how things work before we touch a line of code. The result is a system built around operational reality, not an idealised version of it.

A Deployment Sprint runs for 30 days. It begins with deep listening and ends with something live. Everything in between is built collaboratively, tested against your real data, and scoped to what can genuinely be delivered and maintained.

“We identify your highest-value AI opportunity, build the system, and deploy it to production. Scoping to live in one month.”

Delivery

Four weeks, clearly defined.

Week 1

Discovery & scoping

We sit inside the problem. Interviews, workflow mapping, data audits, and honest conversations about what AI can and cannot do. Success criteria defined together before code is written.

Weeks 2–3

Build

Heads-down construction against real data, in your environment. Daily standups, weekly stakeholder reviews, no black boxes. You see the system evolve in real time.

Week 4

Production deployment

The system goes live on your infrastructure. We handle deployment, run final safety checks, and remain on-hand through initial go-live to absorb any edge cases.

Handover

Documentation & enablement

Full technical documentation, runbooks, and a team walkthrough. You own the system outright — maintained and extended without any ongoing dependency on us.

Who it’s for

You have the problem. We have the engineering.

A sprint works best when an organisation has identified a specific, high-friction operational problem — manual triage, document classification, repetitive decision-making, slow information flows — and needs an external team to build the solution quickly. You bring domain knowledge and data access. We bring cross-functional AI and engineering expertise.

It is particularly well-suited to teams that need to demonstrate ROI to leadership within a quarter, or to organisations where an internal AI initiative has stalled and needs a focused delivery partner to move it forward.

You have a clearly defined operational problem, not just a general interest in AI
You need to move faster than a full internal programme allows
You want to own the system outright at the end, with no vendor lock-in
Leadership needs a working system, not a proposal, as proof of viability
Compliance, governance, and safety are non-negotiable from day one

How we work

Safety-first. Communication-first.

The Forward Deployed model means our engineers are embedded, not remote. We use your tools, attend your standups, and develop genuine fluency in your operational context. This isn’t about physical presence — it’s about the shared understanding that produces systems which actually fit how your organisation works.

Every sprint includes explicit safety checkpoints: evaluation frameworks to test model behaviour, guardrails to constrain output, and governance documentation your compliance team can stand behind. We treat safety not as a constraint on pace, but as a precondition for production.

Strong communication protects both sides. You will always know what is being built, why a decision was made, and what risks have been identified. We escalate problems early, frame trade-offs clearly, and write documentation that a future engineer who wasn’t in the room can actually use.

Outcomes

A production system. Nothing less.

Live in your environment

Deployed on your infrastructure, integrated with your identity and data systems. Processing real inputs from day one of go-live.

Full documentation

Architecture records, API references, runbooks, and deployment guides written for the engineers who will maintain the system long-term.

Governance framework

Evaluation pipelines, model behaviour records, access controls, and a clear audit trail — built in from the start, not retrofitted.

Measurable ROI

Every sprint is scoped against clear success metrics agreed upfront. Quantified impact demonstrable to leadership within 30 days of kickoff.

Next step

Tell us the problem.

A 30-minute call is all it takes to scope whether a sprint is the right fit. We will map the highest-value opportunity and tell you exactly how we would approach it.

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