Our AI deployment methodology.
Method.AI
A practical framework for bringing AI into an established engineering team's software development lifecycle — more speed and better quality, without giving up security, reliability or engineering ownership.
The problem it solves
Most teams are stuck between two bad defaults.
Ban it, and you watch other teams move faster while your own engineers use it anyway — quietly, on personal accounts, with no guardrails at all. Open it up, and velocity climbs for a quarter before the bill arrives: code nobody can explain, review that has quietly become rubber-stamping, and no clear answer when a customer asks how any of it is governed. Method.AI is the path between the two.
What Method.AI is
Clear gates, named owners, and controls that scale with risk
Method.AI sets out when AI can be used across your delivery lifecycle, what a person has to check before relying on it, and who remains accountable for the result. It classifies work by risk, so a typo fix and a production migration do not carry the same review burden — and it makes material AI assistance traceable, so the answer to "how do you govern this?" is a document rather than a shrug.
It is built to sit on top of the engineering controls you already have, not to replace them. Where your existing policy is stricter, your policy wins.
The shape of it
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01
Six principles
Who owns outcomes, how much access a tool is given, what must be verified before it is trusted, and how the controls flex with the stakes.
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02
Four risk tiers
Every AI-assisted task is classified before work starts — from routine, through to work where AI simply is not used. This is what keeps the rest proportionate.
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03
Six lifecycle gates
From tool eligibility through plan, build, verification, release and operation. Each gate has a defined exit condition, so "done" is not a matter of opinion.
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04
Accountability and evidence
Named roles for author, reviewer, service owner and specialist approver, plus a lightweight record of AI assistance a reviewer or auditor can actually inspect.
Why the detail isn't on this page
The tier definitions, gate criteria and guardrails are the working substance of Method.AI — and they are worth very little in the abstract. They only start to mean something once they are mapped onto your architecture, your obligations and your threat model. That mapping is the engagement, and it is the part worth talking through on a call.
What it holds to
The principles are the part we are happy to put on a web page, because they are the part you should hold us to.
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People own outcomes
AI can draft, analyse and automate. Engineers remain responsible for the decisions and the changes. That line does not move.
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Least privilege, always
A tool gets the access, data and permissions the task needs, and nothing beyond it — reviewed before write or execution access is granted.
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Verify before you rely
Generated code, analysis and recommendations are untrusted until checked against evidence. Confident output is not the same as correct output.
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Controls scale with risk
Proportionality is the whole trick. Process heavy enough for a payments change will be ignored on a docs fix, and then ignored everywhere.
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Usage is traceable
Material AI assistance, the decisions around it and the validation done are recorded where the team can inspect them later.
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Improve from evidence
Usage expands or tightens based on incidents, defects, security findings and delivery data — not on enthusiasm, and not on nerves.
What your team actually gets
A framework that exists only as a document changes nothing. These are the things that have to come with it for any of it to stick.
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Something usable on Monday
Not a position paper. Tier examples written against your own architecture, a change record that takes a minute to complete, review checklists, and CI gates that enforce what the framework asks for.
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A baseline, and then numbers
Cycle time, review load, escaped defects, security findings, rework and developer experience — measured before we start. Adoption widens where the evidence supports it, and not otherwise.
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An answer for procurement
Enterprise buyers, auditors and insurers are already asking how AI is governed in your delivery process. A methodology you genuinely follow is a commercial asset, not just an engineering one.
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Engineers who work this way
Gates only help if people clear them rather than wave them through. Enablement is part of the rollout: framing work for an agent, judging how much context to give, and reviewing a diff nobody wrote by hand.
Built to stay current
Models and tooling shift every few months, and a framework written once and filed is worse than none — people quietly work around it and tell you it is fine. Method.AI carries a review cadence, so what your team adopts keeps pace with what the tools can actually do. If a build has already run ahead of its guardrails, that is a different conversation — see Spike It!.
Where it fits
Who it's for
- Established engineering teams
- Regulated or sensitive domains
- Teams already using AI informally
- Scale-ups adding process
It assumes you already have engineering controls worth building on. If you are earlier than that, start with Ask the CTO.
What we'd look at first
- Your delivery lifecycle
- Existing controls
- Data classes
- High-risk systems
- How AI is used today
Usually the honest answer to the last one is more interesting than anyone expects.
Talk it through with us
A free call, no obligation. We'll walk you through Method.AI properly and tell you plainly whether it fits where your team is right now.