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BlueOrbitAI
Generative AI & LLM Solutions

Generative AI that knows your business, and shows its sources.

From a support assistant grounded in your policy library to a private model that never leaves your servers, we turn language models into features your compliance team will actually approve.
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What’s included

  • Retrieval
  • Private models
  • Copilots
  • Evaluation

Cited

a source on every answer the assistant gives

On-prem

delivered, with zero bytes leaving the network

Capabilities

What we deliver

Knowledge assistants, AI copilots and LLM features built into your product, grounded in your own data with a citation on every answer.

  • Permission-aware retrieval

    Answers drawn from your documents with access enforced per document and per person, and a source on every passage.

  • Grounded assistants

    Assistants that understand your products, policies and tone, and decline rather than invent.

  • Copilots inside your product

    Summarisation, drafting, smart search and recommendations built into software your users already open daily.

  • Private and on-premise models

    Open-weight models served on your own GPUs or in your own cloud, for data that is not permitted to leave.

  • Data pipelines

    Ingestion, cleaning, chunking, versioning and vector search that keep answers fresh and attributable.

  • Evaluation and LLMOps

    Automated scoring, regression detection, monitoring and cost tracking for every model call in production.

Why it works

Accurate enough to put in front of a customer.

A generative feature is only useful if someone can check it. Everything below exists to make a wrong answer visible and diagnosable rather than quietly costly.

Technologies we use

  • OpenAI
  • Anthropic Claude
  • Llama
  • vLLM
  • Qdrant
  • pgvector
  • LangGraph
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  1. Grounded answers

    Retrieval with citations keeps responses tied to your real content, and makes a wrong answer diagnosable.

  2. Model-agnostic by design

    Swap between providers or to an open-weight model without a rebuild, because the model sits behind an interface.

  3. Security as architecture

    Role-based access, redaction and region-locked hosting, decided before a line of code rather than bolted on.

  4. Predictable cost

    Caching, streaming and hard ceilings per conversation keep both latency and the invoice inside a known band.

How a generative AI project runs

  1. 01

    Discover

    We read your documents and agree what a correct answer looks like.

  2. 02

    Ground

    We build the retrieval layer, with permissions enforced per document.

  3. 03

    Evaluate

    A test set of real questions with approved answers, scored before anyone sees it.

  4. 04

    Launch

    Into your product or your intranet, with citations visible on every answer.

  5. 05

    Improve

    Monthly scoring, cost review, and model migrations as providers change.

Related case studies

All case studies

Generative AI: common questions

Straight answers to what we are asked most often.

Retrieval lets a model answer from your own documents rather than only its training data. If the assistant has to be accurate about your products, policies or records, you need it.

Yes. Either enterprise agreements with no training on your data, or open-weight models deployed entirely inside your own infrastructure. We have delivered the second.

We build a test set of real questions with approved answers, then score every release against it before it reaches a user. A regression blocks the release.

Yes. Most of this work is integrated into products that already exist, through your current backend and design system.

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