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Take AI from pilot to production.

We identify high-value use cases, engineer generative AI and machine learning on your own data, and operate them with the evaluation, security and observability your risk teams expect. Every use case is tied to a measurable business outcome.

  • A value-ranked AI portfolio
  • Governed, production-grade AI
  • Outcomes measured against a baseline
Discuss your initiativeAll capabilities

Why AI initiatives stall before production

  1. What we hear

    “Our pilot impressed everyone, then went nowhere.”

    How the team answers

    Production engineering from day one: named ownership, evaluation harnesses, observability and cost controls for every model in service.

  2. What we hear

    “We can't trust the answers it gives.”

    How the team answers

    Retrieval-grounded AI with cited sources, continuous evaluation and human review for high-stakes decisions.

  3. What we hear

    “Legal and security keep saying no.”

    How the team answers

    Private deployment, data-residency controls and a complete audit trail, designed to the standards your risk and compliance teams set.

  4. What we hear

    “Nobody can tell us what AI is worth here.”

    How the team answers

    Use cases prioritized by value and feasibility, each with a baseline and a success measure agreed before work begins.

AI solutions engineered for production

Generative AI and machine learning engineered for production: grounded in your enterprise data, governed by your existing controls and measured against business outcomes.

  • 01

    AI strategy & use-case portfolio

    Use cases ranked by value, feasibility and risk, each with a baseline and a business case.

  • 02

    Copilots & knowledge assistants

    Retrieval-augmented assistants grounded in your policies and institutional knowledge, with every answer traceable to its source.

  • 03

    Document intelligence

    Invoices, claims, contracts and forms processed automatically, with people reviewing only the exceptions that need judgment.

  • 04

    Predictive & prescriptive models

    Forecasting, risk, anomaly and optimization models that let the business anticipate rather than react.

  • 05

    MLOps & LLMOps

    Evaluation harnesses, release management, monitoring and token-cost control that keep every model dependable after launch.

  • 06

    Responsible AI & model risk

    Guardrails, bias testing, adversarial testing and documentation aligned with frameworks such as the NIST AI RMF and the EU AI Act.

Platforms and tools we work with

Experience across leading cloud AI platforms and model providers, so each use case runs on the option that best fits its cost, risk and performance profile.

Models and platforms
  • Azure OpenAI
  • Amazon Bedrock
  • Google Vertex AI
  • Anthropic Claude
  • Open-weight models
  • Databricks Mosaic AI
Tools
  • Python
  • PyTorch
  • LangChain
  • LlamaIndex
  • MLflow
  • Vector databases
  • Hugging Face
Team behind the work

AI architects, ML and GenAI engineers, data scientists and MLOps engineers, working alongside your risk and domain teams.

From promising use case to governed production AI

AI earns its place when value is measured and risk is controlled. Each use case moves through the same evidence-based path.

  1. 01

    Identify & baseline

    We rank use cases by value, feasibility and risk, then agree a baseline and a success measure for each before any build begins.

    Value defined up front
  2. 02

    Prepare & prototype

    We ready the data the use case needs and build a working prototype, tested against an evaluation set drawn from your real work.

    Evidence before scale
  3. 03

    Engineer for production

    We add security, guardrails, human review and monitoring, and integrate the model into the systems and workflows where the work happens.

    Production-grade from release
  4. 04

    Measure & improve

    We track outcomes against the baseline, monitor quality, drift and cost, and refine the model as your data and needs change.

    Outcomes you can prove

Applied Intelligence, answered

Can't find what you're looking for? Ask us directly.

Ask us directly

Can you take an AI use case from idea to production?

Yes. Use-case discovery, data readiness, prototyping, evaluation, production engineering, MLOps and post-release monitoring, so a promising idea becomes a dependable, governed service.

Do we need perfect data before starting with AI?

No. A strong first use case needs data that is good enough for one job. We assess quality early, fix what that use case needs and build from there.

Will our data be used to train public models?

No. Models run in your cloud environment or through enterprise services with training on your data switched off, and access follows your existing controls.

Which models do you use?

Whichever best fits the task, cost and risk profile: commercial models through Azure OpenAI, Amazon Bedrock or Vertex AI, or open-weight models hosted privately. Evaluation results decide, not preference.

How do you measure whether AI is working?

Every use case gets a baseline, a target and a quality evaluation before work begins. We report both after release, so the decision to scale rests on evidence.

Other capabilities

All capabilities

Build what’s next.

Whether you're modernizing a core platform, operationalizing AI or rebuilding your data foundation, start here.

For enterprisesStart a ConversationTell us about your objectives. Our team will respond within 1 business day.For experienced professionalsExplore OpportunitiesEngineer production platforms alongside people who own the outcome.