My thesis
Connected Intelligence
A world where fragmented data becomes shared reasoning.
The thesis
Most organisations don't lack data or models — they lack connection. Documents, systems, people and reports each hold part of the answer, in different formats and vocabularies. Intelligence emerges when they share context, can be reasoned over together, and the answer lands where the decision is made.
Why now
Language models made reasoning over messy information cheap — and made it just as easy to build confident systems on inconsistent foundations. The bottleneck has moved from “can a model answer?” to “can we trust the answer, and does it reach the person who needs it?”
A sample architecture
From sources to decisions, in five layers.
Seven principles
The nuances that make it work.
Connect, don't centralise
A semantic layer over systems where they live — not another copy in another lake.
Meaning before models
Most wrong answers are definitional. Governing the ontology drove our 40%+ accuracy gain.
Graphs + vectors
Vectors find what's similar; graphs know what's related and why. Real questions need both.
Trust is a feature
Show sources, respect who's asking, measure quality. Unverifiable answers don't get used.
Meet people where they work
Match Studio mattered once it lived inside PMT — at the moment of decision.
Experts curate, not answer
Put SME knowledge in the ontology once. That's how SME dependency fell 60%.
Every question teaches
Usage, corrections and unanswered questions feed back — so the system compounds.
Three traps
What connected intelligence is not.
A chatbot on a data lake
Fluent answers on undefined data.
One model to rule them all
A better model won't fix bad definitions.
Governance as a final gate
Bolted on late, it blocks launches.
The maturity path
Five steps from fragmented to connected.
Asking a question of the organisation should feel like asking a colleague who has read everything — and can show their working.