Products & Use Cases

A tool for simulating decisions and alignment behaviour in synthetic agents using surveys, experiments & social contexts.

An adaptive system to support personalised learning, well-being, and everyday AI interactions across life domains.

Leverage ALMs and semantic search to intelligently match experts and teams to large-scale, complex projects.

Plug AI into real-world sensing environments through edge intelligence and autonomous configuration tools.

Explore pilot tools, experimental prototypes, and domain-specific systems tailored to emerging Social AI use cases.

Aim/Focus: Analyze emergent alignment behavior in ALMs through survey experiments, ethical dilemmas, and values negotiation.
Key Features: Dynamic scenario testing, contextualized Q&A, ALM feedback integration to refine survey instruments.
Tech Stack: Python (Flask) backend, Streamlit frontend, hosted on AWS, optionally Dockerised.
Status: Live demo available; seeking institutional and enterprise trial partners.

Aim/Focus: Support life planning, wellbeing, and personal development with AI-guided routines and learning journeys.

Key Features: Longitudinal memory, contextual agent coaching, habit loops, embedded feedback channels.

Tech Stack: React frontend, Python (FastAPI) backend, containerized and hosted on Google Cloud.

Status: Beta in development; co-designing with partners in health, L&D, and higher education.

Aim/Focus: Rapidly identify, rank, and recommend ideal experts or teams for complex R&D projects or grant proposals.

Key Features: Semantic search, skill graphs, plugin-enabled APIs, prompt chaining, persona matching.

Tech Stack: Python middleware with Streamlit UI, PostgreSQL and Weaviate (or Pinecone) vector backends.

Status: Used in academic pilots; enterprise licensing and marketplace integrations in development.

Aim/Focus: Create AI-enhanced automation systems for IoT development, deployment, and adaptive control.

Key Features: PDF-to-JSON firmware extraction, autonomous device configuration, plug-and-play pipelines.

Tech Stack: Python (Docker), Coral TPU + Raspberry Pi stack, integrated with Streamlit dashboards.

Status: LLM solution deployed in partnership with EoH.io; progressing into adaptive learning and smart config generation.