Ovrin
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Services

AI integration and workflow automation

We build LLM features, retrieval and workflow automation into real products, with evaluation, fallbacks and cost controls from day one.

We mainly integrate existing models, chosen to suit each client's requirements. The aim is practical automation for workflows that suit it, judged by how useful it is rather than how new the technology is.

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Problems we solve

  • Repetitive work that takes time away from meaningful work

    Manual, recurring tasks create friction and leave less time for the work that matters. Some of them are good candidates for automation; some are not.

  • Knowledge that's hard to find

    Answers are buried in documents, tickets and folders. Retrieval lets a model answer from your own content and show where the answer came from.

  • AI that works in a demo, not in production

    Without evaluation there is no way to tell whether a change made answers better or worse, or how often they are wrong.

  • Costs that grow with every request

    Model usage is billed per request, so cost has to be designed for alongside quality and speed.

What we build

  • LLM features inside existing products: drafting, summarising, classifying and extracting
  • Retrieval over your own documents and data
  • Workflow automation that connects the tools you already use
  • Evaluation sets and monitoring for AI features

How we work

  1. Discover

    Choose a specific workflow and agree what a good result looks like, before choosing a model.

  2. Design

    Design how the feature fits the product, where people review its output, and what happens when the model is unsure or unavailable.

  3. Engineer

    Build the feature with an evaluation set from the start, so every change can be measured.

  4. Refine

    Measure quality, speed and cost against the evaluation, and improve whichever falls short.

  5. Ship

    Release with monitoring of quality and cost, plus documentation.

What you get

  • An AI feature or automation running in your product
  • An evaluation set and results
  • Fallbacks for when the model can't help
  • Monitoring of quality and cost

Technologies

  • Python
  • PyTorch
  • TypeScript

Tell us what you're building.

Share a little about the product, the problem or the timeline, and we'll reply by email.

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