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ALGOLOGIX

Work

What we have built, and how we knew it worked.

Each of these is written the same way we run a project: the constraint that shaped it, what we built, how it was evaluated, and what the numbers were. The evaluation section is the one worth reading — it is the difference between a system that demos and a system that ships.

Filter case studies by sector

Filter case studies by sector
  • Healthcare · 2026

    A knowledge assistant that shows its sources

    A retrieval assistant over clinical policy and procedure documents, answering staff questions with span-level citations and refusing to answer when the documents do not support one.

    Answers carrying a verifiable citation
    • rag knowledge systems
    • generative ai
    • data engineering analytics
    Read a knowledge assistant that shows its sources
  • Retail · 2026

    Turning supplier documents into records

    A document-extraction pipeline for a retail group, classifying and extracting structured records from supplier invoices and delivery notes arriving in every format a supplier felt like using.

    Documents extracted without human review
    • generative ai
    • data engineering analytics
    • backend apis
    Read turning supplier documents into records
  • Logistics · 2026

    A voice agent that handles the overnight queue

    An inbound voice agent for a freight operator, handling status enquiries and booking amendments outside staffed hours, with a clean handover into the morning queue.

    Overnight calls handled without a person
    • conversational voice ai
    • agentic ai
    • backend apis
    Read a voice agent that handles the overnight queue

Some of our work is under NDA. We are happy to walk through it on a call.

Tell us what you are trying to ship.

The useful first conversation is about the constraint, not the technology. Bring the deadline, the data you actually have and the thing that has to be true — we will tell you what it takes, including when the answer is that you do not need us yet.