AI DRIVEN DIGITALISATION
Digitalisation, Engineered with Claude, Copilot, and Cursor.
We rebuild legacy estates, ship bespoke applications, and run full digital transformations all powered by AI-native engineering teams who deliver outcomes, not output.
Book a Demo
Talk to an Architect

The Problem We Solve

Most enterprises sit on the same three problems: a legacy stack that slows every release, a backlog of bespoke features the business needs yesterday, and a digital transformation roadmap that stalls at the pilot stage. Generic IT vendors layer more people on the problem; we replace cost with code written, reviewed, and tested by AI-native teams.

Our AI Driven Digitalisation practice exists to do three things, in any order you need them: modernise what is old, build what is missing, and transform how the business operates around it.

Three Ways We Engage
Modernise. Build. Transform. In Any Order You Need Them.

Three practices. One delivery model. Picked and sequenced around the outcome you need first.

Legacy Modernization

Aging codebases drain budget, slow releases, and lock you out of the cloud. We use Claude to read, refactor, and rebuild legacy applications into cloud-native architectures with full test coverage generated alongside the code. Average time-to-modernise drops by 50% compared to traditional vendors.

  • Codebase analysis, dependency mapping, and risk scoring in days, not weeks.
  • Refactor monoliths to microservices or modular monoliths your choice, our recommendation.
  • Cloud-native targets: AWS, Azure, GCP. Containerised, observability built-in.
  • Test suites generated and validated by Claude alongside human review.
Bespoke Development

When the business asks for a workflow no SaaS will ship, we build it. Our bespoke development practice runs on AI-native pair programming every engineer is paired with Claude, Copilot, or Cursor depending on the task which is how we deliver fully-featured applications in weeks, not quarters.

  • Domain-driven design workshops in week one.
  • AI-pair-programmed sprints with weekly demos and demoable increments.
  • Outcome-based contracts available for scoped builds.
  • Production-ready: CI/CD, security, accessibility, and observability - not after-thoughts.
Digital Transformation

Transformation that does not produce a P&L impact is not transformation. We start with the business outcome cycle time, cost per transaction, customer lifetime value then re-engineer process, technology, and data around it. AI is a means, not a message.

  • Tech stack and integration audit before any build.
  • Change-management framework alongside the technical work.
  • KPI-anchored roadmap with quarterly milestones.
  • Co-delivery: SMI builds, your team operates, knowledge transfers as we go.
Why It Still Matters​

The goalposts have moved. Digital transformation is no longer about moving spreadsheets to the cloud it is about embedding intelligent tools like generative AI and predictive analytics directly into the way work gets done, decisions get made, and customers get served.
Three forces make this non-negotiable right now:

Competitive Displacement

Leaner competitors and well-funded startups are using operational data to move faster, price sharper, and serve better. Organisations that delay modernisation do not stand still they fall behind.

Shifting Customer Expectations

Consumers and enterprise buyers alike expect seamless, omnichannel experiences. Meeting that bar requires continuous backend modernisation not a one-time project.

Operational Resilience

Transformative architecture gives organisations the agility to navigate supply disruptions, regulatory shifts, and market volatility without rebuilding from scratch each time.

How We Engage

Whether you are assessing your current state or ccelerating an existing roadmap, we bring structure to every stage.

Step 1
Tech Stack and Integration Audit
We evaluate your existing architecture, surface integration gaps, and identify where AI-native tooling will produce the highest near-term leverage.
Step 2
Change Management Planning
We build adoption frameworks alongside the technical work, so your teams operate the transformation, not just survive it.
Step 3
KPI-Anchored Roadmap
Every initiative is mapped to financial and operational KPIs from day one. Quarterly milestones. No vanity metrics.
Step 4
Co-Delivery Model
SMI builds; your team operates; knowledge transfers as we go. Governance covering AI risk, data residency, and compliance is built in from the start.
How We Get the Most from AI Code Assistants

Deploying AI assistants without discipline creates noise, cost overruns, and inconsistent output. We have built a structured operating model around five engineering principles that determine how our teams use Claude, Copilot, and Cursor in practice.

Token Optimisation

We treat context windows as a constrained resource. Prompts are engineered to be precise and outcome-focused stripping noise, pre-compressing efference material, and structuring inputs so the model spends tokens reasoning, not re-reading.

Right-Sized Model Routing

Not every task needs the most powerful model. We route requests intelligently complex architecture and multi-step reasoning go to frontier-class models; repetitive generation, boilerplate, and test scaffolding route to faster, lower-cost models. The result is higher throughput at materially lower inference cost.

Spec-Driven Knowledge Retrieval

Our engineers work from structured specifications not open-ended prompts. Feature requirements, API contracts, and domain rules are codified before any generation begins. This grounds outputs in project reality and eliminates the drift that comes from vague, conversational prompting at scale.

Agent Skills with Cache Reuse

Repeatable tasks scaffolding, linting, migration patterns, test generation are codified as reusable agent skills with prompt caching enabled. Teams do not rebuild context on every run. Shared knowledge is retained, compounding efficiency across sprints and projects.

Governed AI Usage Controls

Every AI assistant interaction sits inside a controlled environment policy guardrails, output review gates, and audit trails included. We define what models can and cannot do within each project context, ensuring compliance, IP protection, and consistent quality without slowing engineers down.

AI Engineering Operating Model​
AI Driven Digitalization Across Regulated, High-Complexity Sectors.
Five sectors where AI-native delivery produces the largest P&L impact.
Manufacturing
Shop-floor systems, MES / ERP modernization, quality vision.
Supply Chain
control-tower visibility, demand sensing, autonomous planning.
Healthcare
EMR modernisation, claims, and clinical workflow AI.
Retail
omnichannel, store-ops, and personalisation engines.
BFSI
core-banking modernisation, risk, fraud, and customer service AI.
ENGAGEMENTMODELS
Four Ways to Engage. Match Your Risk Appetite.

Pick the model that fits your budget, risk profile, and roadmap maturity.
Same delivery team across all four.

Outcome-Driven

You define the KPI; we carry the
execution risk. Ideal for funded
transformation programmed.

Time & Material
Transparent, capacity-led
engagement for exploratory or
evolving work.
External IT Team

Build-Operate-Manage, Build operate Transfer, or productivity On- Demand pods. Specialist teams stood up in 4 weeks.

Staffing
Individual AI-native engineers, vetted and embedded in your sprints.

FAQ

Frequently Asked Questions

Will Claude actually write code that ships to production?
Yes — but not on hope. Every AI-generated change passes through four correctness layers: spec-driven inputs, test-first generation, deterministic verification gates (static analysis, regression suite, dependency scan), and an eval-driven CI gate. Human review sits on top, not underneath. Throughput gains come from compressing the floor-work, not skipping the review.
We use Claude, Copilot, and Cursor via enterprise-tier endpoints with zero-retention configuration. IP indemnification flows through from our model and tooling vendors (Anthropic, GitHub, Cursor) to our MSA with you. Every release ships with SBOM and SLSA-aligned attestation; AI-suggested lines carry the same provenance trail as human-written code. The full IP and data-handling summary is part of MSA — not a separate appendix.
Build is 40–60% of total. Real modernisation TCO covers build, dual-running, training, change management, decommission, and 24-month run-rate operations. Our 40%-lower TCO claim is calculated against full programme cost on equivalent scope — not against build cost alone. Per-line-item breakdown shared at proposal stage.
We capture behaviour before we refactor. Tests are generated against the current system first, run green, then become the contract the new code must satisfy. Behaviour parity is verifiable, not hoped for.
This is the most common starting point. We use Claude to reverse-engineer behaviour into tests and documentation, then validate those tests against the running system. The artefacts have value even if you delay the refactor.
Target architectures are usually AWS / Azure / GCP. We are explicit about where managed services (Aurora, Azure SQL) create platform coupling and where open foundations (Postgres, Kubernetes) preserve portability. We architect for your portability requirements, not vendor convenience, and tell you the trade-offs in writing at architecture stage.