Para-expert reasoning and self-serve data analysis for teams without specialist support — labelled by its actual evidence status.
Agxio⋅Intelligence answers questions directly from an organisation’s own knowledge base using real-time retrieval rather than retraining, and flags when that knowledge base can’t fully answer a question rather than guessing. Built for domain teams without engineering support.
Agxio⋅DataScience lets non-technical teams query an underlying database directly in plain English — no SQL, no engineering ticket — turning a multi-step reporting request into an immediate, reviewable result set.
For a leading wealth manager, Agxio redeveloped the underlying data and platform strategy required to support sustained economic growth — auditing the existing data estate, redesigning governance and access models, and setting a platform architecture capable of carrying predictive and generative AI at scale. The engagement paired deep technical architecture work with commercial strategy, aligning the roadmap to the growth case the business needed to make internally.
For a global exchange platform company, Agxio developed an AI approach that optimised how trading data was analysed for client firms — benchmarking their existing Bayesian methods against modern alternatives and identifying where those established techniques were sub-optimal. The result gave the client a more defensible, higher-performing analytical method, backed by a clear technical case for the change.
Agxio works directly with boards to design AI strategy from first principles — assessing technical feasibility, commercial return and regulatory exposure together, and translating that into a governed roadmap the board can mandate and fund with confidence.
Agxio provides board-level support for grant applications and commercial strategy across multiple domains, pairing deep technical credibility with commercial and consulting judgement to strengthen funding bids, sharpen go-to-market decisions and de-risk strategic investment cases.
We label every result by its actual status — historical demonstration, completed programme, pilot or evaluation, beta, product capability, or live deployment — and update labels as work progresses. We do not present a benchmark result as a production outcome, a beta as a finished product, or a pilot as a deployment.
We’re glad to walk through methodology, data provenance and evaluation design for any case here.
Discuss a high-value use case