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Seven practices that hold each other up.

We do not sell these as separate line items. A product needs a platform, a platform needs infrastructure, and an AI feature is worth nothing without the data pipeline underneath it. Most engagements draw on three or four at once.

Practice 01

Technology Solutions

Enterprises rarely have a technology problem — they have an integration problem wearing a technology costume. We design the target architecture, choose what to buy versus build, and make the estate coherent enough to move quickly again.

Solution architectureSystems integration API strategyEvent-driven design Build vs. buyTechnology due diligence
  • Target-state architecture

    A documented destination with a migration path you can fund in stages and stop at any point without stranding value.

  • Integration platforms

    API gateways, event backbones and contract testing so that two teams can change independently without a coordination meeting.

  • Vendor and platform selection

    Structured evaluation against your real constraints, with the total cost of ownership modelled over five years, not one.

  • Discovery that ends in a decision

    Two to four weeks of user research, technical spikes and prototyping, closing with a scope we are both willing to commit to.

  • Design and engineering in one team

    Interface design, accessibility and front-end engineering sit together. No handover documents, no drift from the intent.

  • Platform engineering

    Internal developer platforms, golden paths and paved-road tooling so your other teams inherit the speed rather than the sprawl.

Practice 02

Product Engineering

Full-lifecycle teams for software products — internal or customer-facing. We take responsibility for the outcome rather than the ticket count, which means telling you when a feature on the roadmap is not worth building.

Product discoveryUX & UI design Design systemsMVP to scale Developer platformsAccessibility
Practice 03

Software Development

The core craft. Web, mobile and backend engineering delivered by teams that write tests first, keep pull requests small, and leave a codebase your own engineers can pick up without an archaeology phase.

Web applicationsiOS & Android MicroservicesTest automation CI/CDPerformance engineering
quality-gates.yml
Practice 04

AI & Automation Solutions

The gap between an impressive demo and a system you can put in front of customers is roughly nine months of unglamorous work. We start from that end.

LLM applications

Assistants, copilots and agentic workflows grounded in your own systems — with tool access scoped, logged and reversible.

AgentsTool useGuardrails

Retrieval & knowledge systems

RAG pipelines with document chunking, hybrid search, permission-aware retrieval and citations users can actually verify.

RAGVector searchPermissions

Document intelligence

Extraction, classification and validation across contracts, claims and invoices, with confidence thresholds routing to humans.

OCRExtractionHuman-in-loop

Machine learning & forecasting

Demand, risk and churn models built on governed data, monitored for drift and retrained on a schedule you control.

MLOpsDrift monitoringFeature stores

Intelligent process automation

Workflow orchestration that replaces the spreadsheet-and-email layer between your systems, with full audit trails.

OrchestrationRPAAudit

Evaluation & AI governance

Golden datasets, regression scoring, red-teaming and model cards — the evidence your risk committee will ask for.

EvalsRed teamingCompliance
  • Landing zones and guardrails

    Multi-account structure, network topology, identity and policy baselines on AWS, Azure or GCP — codified, not clicked.

  • Container platforms

    Kubernetes with the boring parts solved: ingress, secrets, autoscaling, progressive delivery and a sane developer experience.

  • FinOps and reliability

    Cost attribution per team and per service, right-sizing, and SLOs with error budgets that genuinely change release behaviour.

Practice 05

Cloud & Infrastructure

Cloud migrations fail when they are treated as a hosting change. We rebuild the operating foundation — identity, networking, delivery pipelines and cost visibility — so that the platform gets cheaper and safer as it grows.

AWSAzureGCP KubernetesTerraformObservability Zero trustFinOps
Practice 06

Digital Transformation

Modernisation programmes fail on sequencing, not technology. We slice the estate so that value lands every quarter, the legacy system keeps running until the day it is switched off, and no one has to bet the business on a cutover weekend.

Legacy modernisationStrangler pattern Data platformsProcess redesign Operating modelEnablement
MIGRATION SEQUENCE
  1. Wrap — put an API in front of the legacy system
  2. Extract — move one bounded context at a time
  3. Run parallel — shadow traffic and reconcile
  4. Cut over — per context, reversible in minutes
  5. Decommission — and actually turn the old one off
SLA

Tiered response

P1 acknowledged in 15 minutes, 24×7, with a named incident commander on every major.

SRE

Error budgets

SLOs agreed with the business and enforced against the release schedule.

SEC

Patch cadence

Critical CVEs remediated within defined windows, evidenced monthly.

OPS

Continuous improvement

A fixed share of every sprint spent on toil reduction, not just tickets.

Practice 07

Enterprise Technology Services

Running software well is a discipline of its own. Our managed engagements combine application support, site reliability engineering and a standing improvement backlog, so the platform gets better rather than merely surviving.

Managed engineeringApplication support Site reliabilityIncident management Security operationsService desk
NEXT STEP

Bring us the problem, not the specification.

Send a paragraph about what is not working. An architect will come back within one working day with questions worth answering.