CommunityAI
Technology and data direction for an early-stage community-intelligence venture, turning public, area-level evidence into a privacy-safe and auditable product without representing model signals as client outcomes.
- Period
- 2026–present
- Status
- Current engagement
- Role
- Chief Technology Officer (Designate)
- Scale
- Organisation and platform
- I1Purpose + procurement
- I2Product + delivery
- I3Evidence + governance
- O1Decision criteria
- O2Target architecture
- O3Implementation roadmap
Chief Technology Officer (Designate)
- Appointment as CTO-designate following a technical audit and a staged platform roadmap.
- An open-data evidence architecture with traceable dataset, processing and model provenance.
- A validation framework that uses spatial holdouts, baseline comparisons and explicit claim boundaries rather than inflated random-split scores.
- Governed release controls separating research evidence, product-ready outputs and client-specific validation.
This case records my current technical mandate, not ownership of the venture or proof of a client outcome. Detailed architecture, counterparties, internal findings and model results are withheld pending written approval. The work is predictive and decision-supporting, not a causal claim.
The venture needed a dependable route from public neighbourhood evidence to a product that could survive technical and commercial scrutiny. The core problem was not simply model performance: every source, transformation and claim needed a traceable provenance, spatially honest validation and an explicit boundary between contextual evidence and a client's own outcome.
My contribution
As CTO-designate within the collaborative founding team, I set the technology and data direction and led the open-data rebuild. I designed the provenance, licence-governance, spatial-validation and reproducible-release controls around the evidence layer. I also built a gate that tests whether candidate signals add predictive value beyond agreed baselines; no signal is represented as client incrementality until it has been tested against the client's outcome. Detailed architecture and commercial findings remain confidential.
Decisions and constraints
Decisions I made
- Treat spatial validation as a release condition, not a retrospective methods note.
- Test candidate signals beyond agreed baselines, but make no client-incrementality claim without the client's outcome data.
- Keep the served product at aggregate area level and outside personal-data workflows.
- Withhold named counterparties, detailed architecture and commercial findings until written publication approval exists.
Operating constraint
A collaborative founding team, an early-stage product and a mutual confidentiality obligation. Public description therefore has to remain high-level while preserving an honest account of my mandate and the controls I introduced.
Claim boundary: This case records my current technical mandate, not ownership of the venture or proof of a client outcome. Detailed architecture, counterparties, internal findings and model results are withheld pending written approval. The work is predictive and decision-supporting, not a causal claim.
What can be checked.
- Appointment as CTO-designate following a technical audit and a staged platform roadmap.
- An open-data evidence architecture with traceable dataset, processing and model provenance.
- A validation framework that uses spatial holdouts, baseline comparisons and explicit claim boundaries rather than inflated random-split scores.
- Governed release controls separating research evidence, product-ready outputs and client-specific validation.