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Data & AI Consulting

Your AI demo worked.We make it run.

We design, build and run the data platforms and AI systems enterprises depend on.

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See what we do

From prototype to production

Start with the business outcome.

We map the systems, data and constraints already in place, then agree a practical route to production.

We build on

AWSAzureGCPDatabricksSnowflakeTensorFlowPyTorchLangChainAWSAzureGCPDatabricksSnowflakeTensorFlowPyTorchLangChain
Data & Cloud MigrationML Model Migration & MonitoringAgentic AI OrchestrationLLM Services & RAGCode & Cost Optimisation

Most AI never leaves the demo.

Real systems have to hold up under real data, real access and real operating costs.

Real workloads

Design for changing data, demand and users.

Clear boundaries

Scope access and keep system actions auditable.

Predictable spend

Find waste in code and infrastructure before it compounds.

What we do

Five ways we get AI into production.

Data platforms, models and agents—built for real workloads.

  1. Move everything. Lose nothing.

    Move data, pipelines and workloads to modern platforms—with validation and a safe route back at cutover.

    Explore migration

  2. Models that stay good after launch.

    Move and version models, then monitor their behaviour as data and workloads change.

    Explore monitoring

  3. Agents that act, with a human in charge.

    Connect agents to your tools with clear permissions, human decisions and an audit trail.

    Explore orchestration

  4. Answers from your data, with sources.

    Ground LLM answers in your own documents, with sources people can check.

    Explore RAG

  5. Spend less. Run just as fast.

    Find and fix waste in code, queries and cloud setup, then keep spend in check.

    Explore optimisation

From first conversation to production

A clear path from your system to a release.

We start with the reality you have, then shape the engineering around what your teams need to operate.

  1. 01

    Understand the system

    Map the data, workloads, constraints and risks that shape the work.

    A clear route forward

  2. 02

    Build for real use

    Engineer the platform, pipelines or AI workflow around real operating needs.

    A working production path

  3. 03

    Validate the release

    Confirm behaviour and controls, then hand the system to the team that will operate it.

    A clear team handover