01 · Capture
Capture. Documents, tickets, chats, and system records are read and broken into entities and facts.
Documents · Systems · Conversations
Applied AI
Shared brain.
An enterprise knowledge graph that people and AI agents share. It links your documents, systems, and decisions, answers with sources, and learns as you work.
Every organisation knows more than any one person can hold. The shared brain joins a knowledge graph, holding your data, relationships, rules, and constraints, with AI models that predict, optimise, and answer in plain language. People and agents work from the same context, every answer shows its reasoning, and what was learned stays when people move on.
Enterprise AI needs a shared foundation.
Most AI efforts stall for the same reasons, and none of them is the model.
01
Too much for people alone
Volatile markets and tightly linked operations change faster than spreadsheets and weekly meetings can follow.
02
Intelligence in silos
Point AI tools each see one function, so the forecast, the price, and the supply plan never meet.
03
Answers nobody trusts
A black-box answer with no reasoning behind it gets checked by hand, or quietly ignored.
The result: slow decisions, value lost between teams, and AI pilots that never scale.
Learns like a team, reasons like an expert.
The shared brain is neuro-symbolic: a knowledge graph that encodes how your business works, joined with AI models that learn from its data.
Symbolic
Knowledge graph
- Memory of data, documents, and decisions
- Relationships across every function
- Business rules, policies, and constraints
Neural
AI models & LLMs
- Learning from patterns in your data
- Prediction and optimisation
- Natural-language questions and answers
= Shared brain
Answers that learn from data, stay inside your rules, and show every step of their reasoning.
Explainable · Rule-aware · Always learning
From scattered knowledge to one shared brain.
Four steps turn documents, systems, and conversations into a graph your people and agents can ask.
02 · Connect
Connect. Entities are resolved and linked into one knowledge graph, so the same customer or asset is one node everywhere.
Entity resolution · Relations · Ontology
03 · Reason
Reason. Questions are answered by walking the graph and the source text together, with every step cited.
Graph retrieval · LLMs · Citations
04 · Learn
Learn. New documents, decisions, and corrections update the graph, so the next answer starts from what was learned.
Feedback · Updates · Memory
One brain, every kind of decision.
Predictive, prescriptive, and generative AI run on the same graph, so a forecast, a plan, and an answer never disagree about the facts.
Predictive AI
Forecasts of demand, capacity, cash, and risk, from next week to next year.
Prescriptive AI
Optimisation that weighs the trade-offs and proposes the best plan within your constraints.
Generative AI
Plain-language questions about the business, answered from governed knowledge.
The four questions agents answer
Past
What happened, and why?
Root causes traced through the graph, not guessed from one report.
Now
What is happening?
Risks and opportunities surfaced as new signals arrive.
Next
What is likely to happen?
Rolling forecasts and scenarios you can compare side by side.
Act
What should we do?
Recommended actions, with the reasoning and trade-offs shown.
Ask a question, see how it was answered.
Every answer shows the path it took through the graph and the sources behind it. The examples below are illustrative.
Question
Which supplier puts Line 3’s deliveries at risk?
Asset
Line 3
Part
Gearbox G-118
Supplier
Supplier B
Incident
Late shipment
Contract
Clause 7.2
Answer
Supplier B. Its last two gearbox shipments arrived late, and clause 7.2 of its contract allows a second source.
Sources
- [1]Maintenance log
- [2]Incident report
- [3]Supplier contract
Learning for the enterprise
A memory that gets better with use.
The graph is never finished. Each new document, decision, and correction is linked to what is already known, so answers improve with use instead of drifting out of date.
- Learns from documents, business systems, decisions, and corrections
- One memory shared by teams and agents, with permissions per source
- Every answer traceable to the documents and records behind it
- Knowledge stays with the organisation when people move on
Human + AI
You set the dial, decision by decision.
Agents take on the scale, speed, and routine. People keep the judgement, the strategy, and the calls that matter, and can oversee far more without losing sight of any of it.
Autonomy, per decision
01
Explainable
Every recommendation traces back to the data, rules, and sources behind it.
02
Governed
Role-based access, an audit trail, and policy controls on every agent.
03
Built to evolve
Works with multiple LLMs, and new retrieval or orchestration methods plug in without re-platforming.
Built for every team.
01
Operations knowledge
Procedures, incidents, and fixes linked to the assets they concern, ready for the next shift.
Procedures · Incidents · Assets
02
Research & development
Experiments, papers, and results connected, so teams build on prior work instead of repeating it.
Experiments · Literature · Results
03
Customer & support
Every account’s history, contracts, and tickets in one place for support teams and their agents.
Accounts · Tickets · Contracts
04
Onboarding & expertise
What senior people know, captured as they work, so new joiners get answers with context.
Onboarding · Expertise · Handover
Ready to give your teams one shared memory?
Tell us where your knowledge lives today. We’ll map the sources, the graph, and the first questions it should answer.