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Generative AI grounded in what your business knows.

We build generative AI applications that answer from your documents and data, show where the answer came from, and are tested and monitored like any other production system.

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Problems we address

  • Answers that cannot be trusted

    A general model does not know your policies, products or data, and may answer confidently anyway.

  • Knowledge trapped in documents

    Important information is buried in manuals, contracts, tickets, and reports—making it hard for teams to find the answers they need.

  • Sensitive data and access control

    Ensure people only get answers from information they’re authorized to access, with permissions respected throughout every interaction.

  • Quality nobody measures

    Without ongoing evaluation, an AI prototype can look impressive in a demo yet deliver inconsistent results in everyday use.

Capabilities

Retrieval-augmented generation

Ground answers in your content by retrieving relevant information when each question is asked.

Knowledge preparation

Ingest, clean, and structure documents and data so the right information can be found.

Citations and traceability

Show the sources behind each answer, making it easier to verify.

Access-aware answers

Respect existing permissions so people only receive information they’re authorized to see.

Evaluation

Measure accuracy, relevance, and safety with realistic questions—before and after each change.

Guardrails

Help keep responses safe, relevant, and within the application’s intended purpose.

Typical use cases

  • Internal knowledge assistants for policies, procedures, and technical documentation
  • Drafting and summarising reports, contracts, and correspondence
  • Search and question answering across engineering, operational, and customer records
  • Support assistants that suggest responses grounded in approved content
  • Customer service assistants for common questions, troubleshooting, and service updates
  • Document extraction and classification for forms, invoices, and case files
  • Meeting transcription, summaries, and action-item tracking
  • Workflow automation for routine requests, approvals, and handoffs
  • Incident and ticket triage, including routing and priority suggestions
  • Knowledge discovery that connects related information across teams and systems

How we deliver

A professional searching shelves of archived documents, with a laptop on a nearby table.

  1. Choose the content and the question

    Agree which knowledge matters, who uses it and what a good answer looks like.

  2. Prepare the knowledge

    Ingest and structure sources, and set up retrieval and permissions.

  3. Build and evaluate

    Create the application and test it on real questions, improving retrieval and prompts.

  4. Release with guardrails

    Deploy with safety controls, monitoring and clear ownership.

  5. Monitor and improve

    Track quality, cost and feedback, and refine as content and usage change.

Technical foundations on AWS

Built on AWS, with managed services where they fit.

  • Amazon Bedrock
  • Amazon Bedrock Knowledge Bases
  • Amazon Bedrock Guardrails
  • Amazon OpenSearch Service
  • Amazon S3
  • AWS Lambda
  • Amazon CloudWatch
  • Model selection follows the task, language, data sensitivity, latency and cost.
  • Retrieval quality usually matters more than model size, so we invest in content preparation and evaluation.

Security, governance and operations

  • Data protection

    Designed so that your data stays within your AWS environment and is not used to train third-party models.

  • Access control

    Answers respect the permissions on the underlying content.

  • Audit

    Questions, sources and outcomes can be logged for review.

  • Human review

    High-impact outputs go to a person before they are used.

Related services

Related insights

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