I help businesses become coherent to machines — so every surface reading your inventory, products, or content gets a consistent, governed signal. One canonical source. Everywhere.
Every layer reads from the one below. Most businesses don't have a canonical layer — they have a collection of stove-piped platforms each maintaining their own partial, inconsistent version of the same inventory.
Most engagements begin when something stops performing and no one can explain why. The ladder turns symptoms into a governed system — one rung at a time.
Things aren't working. Traffic dropped. Feeds rejected. AI surfaces get you wrong. Agencies couldn't explain why.
Multi-Surface Inventory Audit. Two weeks. Map every surface, find every contradiction. Most clients have never seen this picture before.
Design and build the canonical data model above your existing platform. The platform stays. The governed layer is new.
Wire every surface to read from the same source. Search, programmatic, distribution feeds, AI — coherent signal everywhere simultaneously.
Continuous loop. GSC signals, SERP changes, new AI surfaces, feed spec updates. The layer stays current without manual intervention.
A structured diagnostic of how your business is represented across every machine-readable surface. Most businesses have never had this done. It usually explains everything that hasn't been working.
Designed for specialty commerce, hospitality, and founder-led businesses — not just enterprise. AI-native implementation means the cost is significantly lower than comparable infrastructure work from a consultancy.
JSON-LD, OG tags, schema accuracy and completeness.
IAB, product categories, entity labels across surfaces.
Google Merchant, Roku, Amazon, ad stack — spec alignment.
How AI systems currently represent your business.
Entity coverage, intent alignment, citation readiness.
Where surfaces are reading different things about the same inventory.
From CTV streaming at 8M users to a specialty bike shop in Seattle — the same canonical-layer methodology, sized to the business.
Streaming · CTV · FAST · Vodlix
The most complex version of the problem at production scale. Per-surface SSAI validation, Roku / Iris / Google TV feed conformance, brand suitability signals, AI surface enrichment. Every surface reading from the same canonical model.
Specialty retail · cycling · Shopify + Lightspeed
Canonical data layer above Shopify and Lightspeed. Editorial voice encoding, product copy optimized for search and AI surfaces, closed-loop GSC optimization.
Cottage rental · WordPress · hospitality inventory
Canonical layer above WordPress for travel and hospitality inventory. Entity enrichment for AI search, structured data for discovery surfaces, continuous optimization loop.
The methodology isn't a pivot to AI — it's the same thesis, finally cheap enough to deploy outside the enterprise.
First practical systems for embedding metadata into digital media files. ISO MPEG-7 participant — in the room where machine-readable media description was formally defined as a standards problem.
Unlocked $20M in latent print sales by reconciling 10M archive prints to known metadata via image annotation and Linked Open Data.
Human computation platform for semantic media annotation at archive scale. Patent: task-agnostic integration of human and machine intelligence.
EU-funded research. Fragment-level entity annotation, cross-content hyperlinking for TED Talks. Required a consortium and five external APIs. Now runs in a single LLM prompt.
Video AI taken public on the London Stock Exchange. 10× improvement in Google Search funnel metrics via governed structured data and semantic conformity.
8M+ monthly users. 70+ local news stations. Full canonical data layer running in production. The most advanced proof of the methodology.
Start with a multi-surface inventory audit. Two weeks. A complete picture of where your machine-readable signals contradict each other — and a roadmap to fix them.