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Research

We take on applied research mandates for organisations that need evidence before they commit: model evaluations, feasibility studies, benchmarks, and technical due diligence. Findings are reproducible and written to be acted on.

When you need this

The situations we are called into.

  • You must choose between models, vendors, or architectures — and public benchmarks do not answer your question.

  • An acquisition or investment requires technical due diligence on an AI product.

  • A regulator, standards body, or court needs the evidence behind an AI claim.

Approach

How the engagement runs.

  1. Question formulation

    The decision the research must inform, stated precisely — so the study answers it and nothing else.

  2. Method design

    Datasets, metrics, baselines, and controls specified and agreed before any experiment runs.

  3. Experimentation

    Runs executed on versioned code with logged configurations, so every number can be regenerated.

  4. Analysis

    Results with uncertainty stated, limitations named, and alternative explanations addressed.

  5. Reporting

    A technical report and an executive summary, presented and defended to your stakeholders.

Deliverables

What you hold at the end.

  • Study design document

  • Reproducible experiment code

  • Technical report

  • Executive summary

  • Findings presentation

Related research

Technical report · 2026

Evaluating large language models for regulated enterprise workflows

A practical evaluation framework for LLM systems in banking, insurance, and government contexts — metrics, test harnesses, and a taxonomy of failure modes.

In preparation

Benchmark · 2026

Private LLM inference on Apple-silicon clusters

Throughput, latency, and cost characteristics of quantised open-weight models served on commodity Apple-silicon hardware, measured against cloud baselines.

In preparation

White paper · 2026

Multi-agent systems in production: reliability patterns

Failure modes observed when orchestrating multiple LLM agents on business-critical tasks, and the containment patterns that keep them recoverable.

In preparation

Contact

Talk to our engineers.

Describe the situation you are in. The person who replies is the person who would do the work.