AI Coding

AI Coding for Enterprises.
Done properly.

We help businesses turn AI coding into faster time to market.seamless legacy integration.production-ready capability.modern AI and data governance.

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How we help

The Anthropic framework, made real.

Anthropic have created an industry standard for how enterprises should adopt AI coding. We bring the strategic thinking, engineering discipline and delivery experience that turns the framework into a working practice.

Phase 01

Activation

More than a plan. Everything you need to start building.

Typical shape: 4 weeks. Fixed scope.

The delivery checklist:

Plan and business case.

  • An estate map, legacy systems included, opportunity identified workload by workload.
  • A prioritised roadmap. What to build first, what blend of capabilities each workload needs, what it costs.
  • A proof point. A demonstrable baseline or small shipped workload.

Tooling, environments and data.

  • Agentic tools selected and configured. Claude Code, Copilot, Cursor or equivalents, with enterprise controls in place.
  • Development, test and production environments separated. Branch protections and agent permissions set.
  • Data readiness. Classification, access rules, and the plan for what agents can see and touch.

Governance, quality and the team.

  • A governance harness tuned to your risk posture. Identity, data access, change control, audit, spend. Including shadow AI and shadow IT exposure.
  • Quality process. Code review protocols, test coverage, automated guardrails before production.
  • Operating model. Fusion team structure, request triage, business and IT collaboration.

Machine-readable foundations for the next phase.

  • Project-level instructions agentic tools consume directly. Architecture maps, coding standards and patterns in the formats Claude Code and Copilot read.
  • Data classification and access rules expressed as tooling instructions.
  • Test harnesses and review patterns ready to run from day one of Acceleration.

Phase 02

Acceleration

Stand it up, deliver the first workload, prove the whole approach.

Typical shape: 3 to 12 months. Modular, monthly commitment.

The delivery checklist:

Infrastructure stood up.

  • Agentic coding tools deployed with production-grade controls. Compliance APIs, spend caps, policy settings, tool permissions, managed credentials.
  • The Activation governance harness stood up around the tools. Identity, data access, environment controls, audit trails, review workflows, approval gates.
  • Harness sits on top of the tools, not inside them. You retain control regardless of which tools come and go.

First workload shipped.

  • The workload prioritised in Activation, delivered end-to-end to production.
  • Chosen to exercise the governance harness, train the fusion team, and produce a result you can point to internally.
  • Modern product and engineering practice throughout. Not agent autonomy unsupervised.

Team embedded, not handed off.

  • Amplifi engineers embedded with your team. We pair, we review, we transfer knowledge as we go.
  • Not offshored delivery with senior consultants on Zoom. Not a tool setup left for your team to figure out.

Fusion team operating.

  • Activation's operating model stood up in practice. Business and IT collaborating on agentic work.
  • Requests triaged into the right delivery track. New capability integrating with existing pro-code and LCNC work.

Knowledge transferred.

  • Every artefact, decision and tooling configuration documented.
  • Your team trained on the tools, the classification logic, the governance patterns, the context-building discipline.
  • The practice ready to extend without us in the room.

Phase 03

Expansion

Keep it working as the tools, the regulations and your estate all change.

Typical shape: Ongoing retainer or managed practice.

The delivery checklist:

Ongoing product and engineering discipline.

  • Continuous challenge on why workloads are being built and when to stop.
  • Active management of AI-generated code review, refactoring, testing and architectural coherence.
  • Prevention of the fragmentation that kills AI-inflected codebases within eighteen months.

Governance evolution.

  • Quarterly review of the harness against changing regulatory and risk requirements.
  • Refresh of policies, access controls and audit patterns as the environment changes.
  • Technical debt tracked against defined metrics your leadership can monitor.

Visibility and cost control.

  • Adoption and usage monitoring. How tools are used, by whom, for what.
  • Identification of emerging shadow use, expansion opportunities, and workloads not delivering value.
  • Ongoing spend discipline as usage scales.

Tooling and capability evolution.

  • Active management of the tooling stack as vendors release, deprecate and reprice.
  • Protection from lock-in, and from chasing every new tool.
  • Team upskilling as the landscape changes.

Next-frontier identification.

  • Ongoing work with you to identify the next workload, the next capability, the next frontier.
  • Advanced use cases and transformational workloads, surfaced by a partner paying attention over time.
The destination

What a well-run AI coding practice actually looks like.

Most enterprises adopting AI coding today do not know what good looks like. They have seen individual velocity, not organisational capability. Here is the picture we build toward, and the state you should expect to reach.

Top-line impact.

New revenue streams and products delivered in months rather than years. Time-to-market that lets you beat competitors to the punch. Data you already own turned into commercial capability your customers pay for.

Bottom-line impact.

Delivery costs materially reduced across the estate. Fewer senior engineers needed to run the roadmap. Legacy maintenance costs brought under control. Tool spend visible, capped, and tied to measurable business outcomes.

Engineering velocity measured in weeks, not months.

Software that used to take two quarters now shipping in six weeks. Legacy refactors that used to be impossible happening as normal maintenance. Roadmap items landing at the speed the business asks for them.

A governance harness that holds on day one and eighteen months later.

Identity, data access, environment control, audit, spend. All five layers mapped to the tools in scope. Security team sleeps. Auditors have what they need. Regulators are satisfied.

Code quality that doesn't degrade as the codebase accelerates.

AI-generated code indistinguishable from good hand-written code, because the guardrails force it to be. Architectural coherence maintained. Technical debt tracked, not quietly accumulating.

A fusion team that owns the practice.

Your engineers trained on the tools, the discipline, and the governance patterns. Able to extend the practice to the next workload without us.

An estate getting simpler over time, not more tangled.

AI coding reducing complexity, not adding to it. Legacy systems modernised, wrapped, automated around, or left alone, depending on which is the honest answer.

A board that has stopped asking "are we doing enough with AI?"

Because the answer is obviously yes, and the evidence is running in production.

Worth knowing

Five things most enterprises get wrong.

If none of these surprise you, you are our kind of buyer. If they do, there is more we should talk about.

Governance is the work, not the wrapper.

The harness around the tools determines whether the investment delivers or fails. More than the tools themselves.

Context is the real constraint.

Agentic tools are only as good as the context they operate in. A Claude Code instance in a repo with no architectural map, no coding standards and no test harness produces code you will rewrite. The difference is the preparation, not the tool.

The eighteen-month problem.

AI-generated code accumulates technical debt faster than human-written code. Gartner predicts 50% of enterprises will face rising maintenance costs from unmanaged AI-generated technical debt by 2030. Most programmes have no answer.

AI coding fits around what you already have.

It sits alongside your legacy estate, your LCNC platforms, and any agents you deploy for business processes. We have a view on how all of it fits together. We will walk you through it.

The cowboy problem is already inside your organisation.

Gartner 2025: 69% of organisations suspect their employees are already using prohibited generative AI tools. Your engineers are probably using Claude Code or Copilot outside your governance right now. Doing nothing is not a neutral choice.

Why Amplifi

Why us, specifically.

Claude Partner Network member. Palantir service partner.

Both earned through formal review. Together they cover the data and platform layer where most enterprise AI investment fails to land, and the AI coding layer where most software delivery is being reinvented.

We live it every day.

AI coding is how we build, not just what we sell. Our own engineering team uses Claude Code, Copilot and the wider agentic stack on every engagement. You get a partner that has run the practice on itself before it runs it with you.

Breadth across the stacks that matter.

Palantir Foundry and AIP. Claude Code, Copilot, Cursor. Traditional pro-code engineering. Data engineering and ontology design. Product and delivery practice. We work across the full blend because a real enterprise engagement needs all of it.

Mid-market fit the Big Four can't match on speed or price.

Big Four frameworks are sized for FTSE 100 programmes. Mismatched to mid-market scale and budget. Our Palantir delivery record, blended UK-Vietnam operating model and 65-70% cost advantage let us deliver the disciplined approach at mid-market speed and price.

Mastery of what goes around AI coding.

The tools will commoditise. The engineering and product discipline that makes them work as enterprise software over a multi-year horizon will not. Most enterprises treat AI coding as a tooling question. We treat it as an engineering practice question where AI tools are one input.

Where do you want to begin?

Start with a conversation. Pick an entry point, or just tell us what you're thinking about.

Let's just talk

Or start with a specific phase.

Activation Acceleration Expansion