AI and Agents · Production AI

Generative AI and Machine Learning

Turn organisational data and domain expertise into secure AI applications that solve a specific, named business problem.

What usually brings people here

A proof of concept that impresses in a demo often stalls before production, usually for the same reasons: no evaluation baseline, unclear data boundaries, unpredictable cost, and no owner for the thing once it is live.

How we approach it

We start by narrowing to a use case with a measurable outcome, then work backwards from what it takes to run it safely — data access, retrieval quality, evaluation, cost ceiling, and the operational model. Where retrieval solves the problem, we do not reach for fine-tuning.

Retrieval pipeline

  1. 1

    Source content

    Documents, wikis, ticket history

  2. 2

    Ingest and chunk

    Scheduled, with permissions captured

  3. 3

    Embed and index

    Vector plus keyword

  4. 4

    Retrieve

    Filtered to the asking user's access

  5. 5

    Ground and answer

    Model reads only those passages

  6. 6

    Cite and log

    Sources shown, quality measured

How a question becomes an answer grounded in your own content, with the permission filter applied before the model ever sees a passage.

AI strategy and readiness

Before building anything, establish which use cases are worth pursuing and what would have to be true for them to work.

  • Use-case identification and prioritisation against business value
  • Data availability, quality and access review
  • Security, privacy and governance constraints mapped early
  • A roadmap that sequences by dependency, not by enthusiasm

Retrieval-augmented generation

Grounding a model in your own content is usually the highest-value first step, and the one most often done poorly.

  • Ingestion, chunking and embedding strategies matched to the corpus
  • Hybrid semantic and keyword retrieval
  • Re-ranking and citation of source material
  • Permission-aware retrieval so users only see what they may see

AI applications and copilots

The model is a component. The value is in the application built around it and the workflow it sits inside.

  • Enterprise knowledge assistants and semantic search
  • Document intelligence: extraction, classification and validation
  • Domain copilots embedded in existing tools
  • AI-native features inside your own product

Evaluation and responsible AI

Model behaviour changes when providers update, when your data changes, and when prompts drift. Evaluation is what catches it.

  • Golden datasets and automated evaluation in CI
  • Prompt and context engineering with version control
  • Fine-tuning only where retrieval and prompting demonstrably fall short
  • Bias, safety and data-handling review before launch

What you receive

  • Prioritised use-case assessment with a recommended starting point
  • Reference architecture and data-flow documentation
  • Working application deployed to your AWS account
  • Evaluation harness and baseline scores
  • Cost model and monitoring for AI workloads

What should change

  • Secure access to knowledge that was previously hard to find
  • Manual document handling reduced or removed
  • A repeatable path from experiment to production
  • Predictable, monitored spend on AI workloads

AWS services we commonly use

  • Amazon Bedrock
  • Amazon SageMaker
  • Amazon OpenSearch Service
  • Amazon S3
  • AWS Glue
  • Amazon Athena
  • AWS Lambda
  • Amazon Q

Talk this through with an engineer

Thirty minutes is usually enough to establish whether this is the right service for your situation.

Book a Strategy Session

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