Projects

MHIL

Concept & Early Prototyping (TRL-5)
Semi-Stealth • Non‑Profit
  • MHIL is a non-profit research institute pioneering comprehensive benchmarks for authentic machine intelligence that extend beyond narrow LLM capabilities.
  • It develops human-aligned test batteries designed for composite AI systems and autonomous agents, rigorously evaluating memory, planning accuracy, robustness, and problem-solving under uncertainty.
  • The lab crafts open evaluation matrices enabling objective, vendor-agnostic comparisons across models and architectures, addressing the emerging demand for pre-superintelligent system assessment.
  • Its frameworks focus on standardising reproducible scorecards, defining capability tiers, and establishing safety-critical thresholds vital for real-world, safety-sensitive AI deployment.

MazeByte

SOTA Proof of Concept (PoC)
Semi-Stealth
  • MazeByte is an advanced research initiative demonstrating a fully autonomous ETL AI agent capable of self-maintenance without human intervention.
  • It employs biologically inspired feedback mechanisms, including reward and pain signals, alongside resource-seeking behaviours to foster adaptive learning and system resilience.
  • The agent generates modular pseudo-code components that manage memory
  • search

SightBit

SaaS (TRL-9)
Public
  • SightBit is a production-grade computer vision SaaS platform delivering real-time detection for drowning prevention, human and vessel identification, and flood-risk forecasting.
  • Operating exclusively on standard CCTV video streams, it requires no specialised sensors or edge processing hardware and eschews site-specific calibration.
  • Its core panoptic segmentation model, enhanced by a proprietary data augmentation pipeline, achieves significant improvements in small-object recall, offering scalable, reliable visual analytics for safety-critical environments.

AdaptiveBridge

Production-ready (TRL-8)
Public • Open-Source
  • AdaptiveBridge is an open-source, production-ready Python library engineered to maintain machine learning model robustness amid missing features at inference time.
  • It predicts and imputes absent inputs with configurable thresholds on feature importance and correlation, supporting custom accuracy metrics and automated handling of variable distributions.
  • Comprehensive documentation, thorough unit testing, and continuous integration accompany its permissive open-source release.
  • Delivered as a drop-in estimator with a familiar fit-transform interface
  • it manages complex feature dependencies

Jhive

Production (TRL-7)
Public • AIforGood
  • Jhive is a Django-based, production-level web application utilising the OpenAI API for real-time antisemitic content detection.
  • It features a detailed reporting interface for monitoring classified content and provides RESTful API endpoints with rate limiting for secure integration.
  • The system incorporates rigorous input validation and sanitisation protocols to defend against injection attacks
  • prioritising operational security and responsible AI deployment in sensitive content moderation domains.