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AI ENGINEER UKBUILT FOR THE REAL WORLD
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Capabilities

What we actually build.

Each page below describes the work rather than the outcome, because by the time somebody is reading this they have usually had enough of the outcome version.

LLM integration

Putting a language model behind a real feature, with the evaluation, guardrails and cost controls that stop it embarrassing you in production.

Claude OpenAI Bedrock vLLM Instructor
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Retrieval and semantic search

Search over your own documents that returns the right passage and cites where it came from, rather than a confident paragraph you cannot verify.

pgvector PostgreSQL Elasticsearch Qdrant LangChain
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Agent and workflow pipelines

Multi step processes where a model calls real tools against real systems, built so a failure halfway through does not leave your data in pieces.

MCP Temporal Laravel Queues Celery Redis
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Model deployment and inference

Running models on your own infrastructure when data residency, unit cost or latency make a hosted API the wrong answer.

vLLM Ollama Docker Kubernetes Terraform
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Data engineering for AI

The unglamorous work that decides whether anything above will function. Pipelines, quality, lineage and the schema nobody wrote down.

PostgreSQL dbt Airflow Python Pandas
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Let's work together

Tell us what you are trying to build.

Technical detail welcome. The more concrete the problem, the more useful the first reply.

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  • UK based
  • Direct engineer contact
  • Documented handover