Everything we do, written down

• LLM application development

  A model capability end users can actually rely on.

  • Architecture and model-selection memo
  • Evaluation harness with reference dataset
  • Cost and latency budget

• RAG and enterprise knowledge systems

  Answers grounded in your own documents, with citations.

  • Ingestion and chunking pipeline
  • Permission-aware hybrid search index
  • Retrieval quality benchmark

• AI agents and workflow automation

  Software that completes multi-step work, not just answers.

  • Process map with human checkpoints
  • Scoped tool access, policy layer
  • Audit trail and staged rollout

• Conversational AI and assistants

  An assistant that resolves work instead of deflecting it.

  • Channel integrations — web, Slack, Teams
  • Handoff-to-human design
  • Safety and tone guardrails

• Document intelligence

  Data pulled from messy documents at an auditable accuracy.

  • Taxonomy and field schema
  • OCR, extraction and validation pipeline
  • Confidence scoring, human-review queue

• Computer vision

  Cameras and images turned into operational signal.

  • Collection and annotation plan
  • Detection or segmentation model
  • Edge or cloud inference deployment

• Predictive analytics and applied ML

  Forecast what costs money — churn, demand, fraud, failure.

  • Dataset and feature build
  • Baseline and champion models
  • API or batch scoring deployment

• Model fine-tuning and adaptation

  Domain adaptation — after prompting, retrieval and evaluation, never before.

  • A do-you-need-this assessment first
  • Training dataset curation
  • Eval comparison against baseline

• AI integration and model migration

  AI in software you already own, without a rewrite.

  • Gateway, routing, fallbacks, caching
  • Provider-agnostic abstraction
  • Migration test suite and cutover

We build on foundation models and adapt them where evidence supports it. We do not train foundation models.

• Multi-tenant SaaS product development

  Tenancy, billing and isolation designed in from release one.

  • Tenancy model and data isolation
  • Auth, RBAC, SSO and SCIM
  • Subscription, metering and billing

• MVP and product discovery

  In front of real end users inside a quarter.

  • Discovery workshop and prioritised scope
  • Clickable prototype
  • Shippable MVP with analytics

• Full-stack web application development

  Web applications your team can still own in three years.

  • Accessible frontend, WCAG 2.2 AA
  • Backend services and domain model
  • Automated test suite and CI

• API and backend platform engineering

  Versioned, documented APIs that others can build on.

  • OpenAPI or GraphQL contract
  • Versioning and rate-limiting policy
  • Developer portal and SDKs

• Systems integration and middleware

  SaaS, ERP, CRM and data tools behaving as one.

  • Integration map and canonical model
  • Idempotency, retry and reconciliation
  • Health monitoring and runbooks

• Product design and UX

  Complex software made to feel obvious.

  • UX research and journey maps
  • Design system and component library
  • Usability and accessibility testing

• Cloud architecture and migration — AWS, Azure, GCP

  A cloud footprint that is secure and not burning cash.

  • Landing-zone and IAM design
  • Terraform or Pulumi baseline
  • FinOps cost model

• DevOps, platform engineering and SRE

  Ship more often, break less, sleep at night.

  • CI/CD and environment strategy
  • Observability, SLOs, error budgets
  • On-call rota and runbooks

• Data engineering and analytics platform

  Trustworthy data — the precondition for every AI thing next.

  • Ingestion pipelines and orchestration
  • Lakehouse or warehouse design
  • Quality tests, contracts and lineage

• MLOps and LLMOps

  AI on rails: versioned, monitored, reproducible.

  • Model and prompt registry
  • Deployment and rollback pipelines
  • Drift, quality and cost monitoring

• AI evaluation, observability and guardrails

  Evidence that your AI still works on live traffic.

  • Labelled reference dataset
  • Offline and online eval suites
  • Runtime guardrails and regression gates

• Legacy modernisation

  Escape the monolith without stopping the business.

  • Risk-ranked codebase assessment
  • Strangler-fig migration sequence
  • Data migration and decommissioning

• Quality engineering and test automation

  Confidence to release weekly instead of quarterly.

  • Test strategy and pyramid
  • Automated regression suites
  • Performance and load testing

Handover is a real option, not a threat. Several arrangements we like best began as one.