The lab does not chase benchmarks. Every programme starts as a question that surfaced in a live deployment and ends as something we can teach and ship.
Active programmes, each tied to a production system
Industries the questions come out of
Latency budget the voice work is held to
Routes a finding takes out of the lab
Why industry-led
Published results are measured on clean data, with unlimited latency, by people who will never operate the system. None of those conditions hold in production.
So the lab works the other way round. We take the unresolved questions out of systems that are already live, reproduce them on honest data, and publish what actually moved the number.
01 -Active programmes
Eight programmes, each running against a system that is already in production.
PersonaGraph – Persistent Personal Knowledge Reuse Across Large Language Models Remember Once. Use Forever. About This Series We introduce PersonaGraph, an architecture for persistent personal knowledge reuse across…
A unified layer across models, agents, data pipelines, policy, and observability ??? so AI adoption scales past the pilot team.
Autonomous agent orchestration, tool-use patterns, and multi-agent collaboration ??? with the governance, guardrails, and observability that production requires.
Natural spoken interaction between a student and a tutor that has to keep up. The hard parts are not speech recognition ??? they are contextual dialogue over a…
02 -Focus areas
Beyond the active programmes, these are the domains we read, publish, and hire into.
ASR, TTS, emotion and energy analysis, and the latency engineering underneath.
Detection, inspection, and scene understanding on hardware that fits the site.
IoT-fed yield and pest prediction, and the data-quality work that makes either possible.
Diagnostic support, voice intake, and the calibration standards regulation implies.
Voice tutors, adaptive pacing, and measuring comprehension rather than engagement.
Claims, risk scoring, and document intelligence where every answer must be defensible.
Multi-agent collaboration, tool use, and failure recovery as a first-class concern.
Grounding, knowledge graphs, citation integrity, and honest handling of the unknown.
03 ??? The loop
Every programme feeds the curriculum we teach and the systems we deliver.
A constraint from a live system becomes a programme with a baseline and a harness.
Validated findings become curriculum.
The method reaches clients as a production system.
What breaks in the field is the next programme.
Lab → delivery
04 ??? Outputs
A finding that never leaves the lab has not really been tested.
Applied results with the method, the baseline, and enough detail to reproduce the number.
Architecture and strategy analysis for teams deciding what to build and what to buy.
Production deployments with the metrics that mattered to whoever paid for them.
Harnesses and tooling we would want to find ourselves, contributed back.
Collaborate
Enterprises with a constraint nobody has cracked, academic groups looking for a production partner, and engineers who want to work on the failure cases.
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