02 / AI

Artificial Intelligence Solutions

AI applied to real workflows, with a human in the loop.

Most AI value comes from narrow, well-chosen use cases wired directly into existing workflows. We help you identify those cases, prove them quickly, and then engineer the reliability, evaluation and cost controls needed to run them in production.

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4–6 weeks

Prototype to production

Traceable

Answers with source citations

Human review

On every critical decision path

What we do

Capabilities in this practice

LLM assistants and copilots

Domain assistants grounded in your own documents and data, with retrieval, citations and guardrails so answers can be traced back to a source.

Retrieval-augmented generation

Document ingestion, chunking, embeddings and vector search tuned for your content, including permissions so users only retrieve what they are allowed to see.

Document and content automation

Extraction, classification, summarisation and translation pipelines that turn contracts, invoices, tickets and reports into structured data.

Predictive machine learning

Forecasting, scoring, ranking and anomaly detection models built on your historical data, with a clear view of accuracy and its business meaning.

Evaluation and safety

Test sets, automated evaluation, human review queues, prompt versioning and fallback behaviour so quality is measured rather than assumed.

Cost and model strategy

Model selection, caching, routing between providers and token budgeting to keep unit economics predictable as usage grows.

How we work

A predictable delivery process

Use-case shortlist

Assess candidate workflows for value, data readiness and risk, and pick the ones worth building.

Prototype

A working proof of concept on real data within weeks, measured against an agreed quality bar.

Productionise

Integration, guardrails, evaluation harness, monitoring and cost controls.

Improve

Ongoing tuning driven by real usage data, user feedback and model updates.

Deliverables

What you receive

·Use-case assessment and feasibility report
·Working prototype on your data
·Production integration with guardrails and logging
·Evaluation suite and quality benchmarks
·Cost model and operating guidance

Toolkit

Typical technologies

Large language modelsRAG pipelinesVector databasesEmbeddingsEvaluation harnessesPython & TypeScript

FAQ

Common questions

Will our data be used to train external models?

No. We use enterprise API terms that exclude training on your data, and we can deploy in configurations where data stays inside your own cloud account.

How accurate will it be?

We define an accuracy target and a measurable evaluation set at the start. If a use case cannot meet the bar, we say so before you invest in productionising it.