Our work
Real AI. Real Products. Real Outcomes.
A selection of AI systems we have designed and engineered, from multi-agent platforms to retrieval assistants and conversational commerce.
News Aggregation & AI Automation
An automated pipeline that ingests high-volume sources, classifies and de-duplicates content, and produces editorial-ready summaries.
- Problem
- Editorial teams could not monitor the volume of incoming sources manually.
- Solution
- A scheduled ingestion pipeline with topic classification, near-duplicate detection, sentiment tagging and summarisation, feeding a review queue rather than publishing directly.
- Outcome
- Editors work from a filtered, categorised queue instead of raw feeds.
- Automation
- Classification
- NLP
AI Law Assistant
A research assistant that retrieves relevant clauses, precedent and guidance from a controlled legal corpus with citations.
- Problem
- Fee earners spent significant time locating relevant precedent across archived matters and reference material.
- Solution
- Document ingestion and chunking tuned for legal structure, hybrid retrieval, and answers that always cite the source passage so the underlying document can be checked.
- Outcome
- Research starts from cited source material instead of a blank page, with the professional retaining review.
- Document AI
- RAG
- Vector Search
AI Interview Platform
A structured interview platform that runs consistent first-stage screening conversations and summarises them for hiring teams.
- Problem
- First-stage screening consumed recruiter hours and varied between interviewers.
- Solution
- Structured AI-led interviews against role-specific criteria, automatic transcription and summaries, with scoring surfaced for human decision-making rather than automated rejection.
- Outcome
- Hiring teams review consistent, comparable summaries and keep the final decision with people.
- LLM
- Speech
- Workflow Automation
AI Shopping Assistant
An intelligent shopping assistant helping customers discover, understand and choose products through natural conversations.
- Problem
- Category filters and search failed shoppers who described what they wanted in their own words.
- Solution
- A conversational layer over the product catalogue that interprets intent, compares options against live inventory data and hands the basket back to the storefront.
- Outcome
- Shoppers reach a relevant product in fewer steps, and support handles fewer pre-purchase questions.
- Conversational AI
- E-commerce
- LLM
TruPaths AI
A domain-specific AI assistant designed to provide contextual answers using a controlled knowledge base.
- Problem
- Users needed reliable answers from a specialist body of content, without the assistant inventing information outside it.
- Solution
- A retrieval-augmented architecture with strict source grounding, citation of the underlying documents and evaluation harnesses to test answer quality as the content set grows.
- Outcome
- Answers stay tied to approved content, and new material can be added without retraining a model.
- Generative AI
- OpenAI
- Qdrant
- RAG
Insiders Health
A multi-agent AI platform using specialised AI agents and persistent knowledge to deliver personalised coaching experiences.
- Problem
- Personalised health coaching depended on practitioner availability, so guidance between sessions was inconsistent.
- Solution
- A coordinated set of specialised agents with a shared long-term memory layer, retrieving from an approved knowledge base and handing off to human coaches when a conversation moves outside defined boundaries.
- Outcome
- Members receive continuous, context-aware guidance, and coaches spend their time on the conversations that need human judgement.
- LLM
- Multi-Agent AI
- Qdrant
- RAG
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