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Metabarc

AI & LLM Solutions

Practical AI that removes busywork — with the controls a real business needs.

We build LLM-powered assistants, knowledge search and automations that connect to your data and tools. Every solution is designed with evaluation, human approval where it matters, privacy controls and a fallback path.

Capabilities

What we deliver.

  • AI agents & assistants

    Task-focused agents that draft, look up, summarise and route work across your tools.

  • Knowledge search (RAG)

    Search and answer over your documents, policies and product data with cited sources.

  • Support assistants

    Chat assistants that answer common questions and hand off to humans with context.

  • Document intelligence

    Extract structured data from invoices, forms, contracts and emails.

  • Workflow automation

    Automate repetitive multi-step processes with approvals and audit trails.

  • Evaluation & monitoring

    Test sets, quality metrics and monitoring so performance is measured, not assumed.

AI solutions are validated with evaluation and human review before production use.

Deliverables

What you get.

  • Use-case assessment and success criteria
  • Data and privacy review
  • Working pilot connected to real data
  • Evaluation results against agreed test cases
  • Human-in-the-loop and fallback design
  • Production rollout plan and monitoring

Use cases

Where it fits.

  • Answering repetitive customer questions
  • Searching internal policies and documents
  • Processing invoices and forms
  • Drafting product descriptions at catalogue scale

Process

How the work runs.

  1. 01

    Assess

    Pick a use case with clear value and measurable success criteria.

  2. 02

    Pilot

    Build a scoped pilot on real data with evaluation from day one.

  3. 03

    Validate

    Review accuracy, cost and risk with your team before rollout.

  4. 04

    Scale

    Harden, monitor and extend to more workflows.

FAQ

Common questions.

Will the AI act on its own?

Only where you decide it should. We design approval steps for sensitive actions and default to human review until performance is validated.

Is our data used to train public models?

We configure providers and settings so your data is not used for model training, and we review data handling during the assessment.

How do you measure quality?

We build evaluation sets from real examples and report accuracy and failure cases before any production rollout.

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