Make your data usable everywhere the work happens.
The expanding market for data consumers is also expanding where data has to be discoverable and accessible. Buyers now expect to find and use your data natively: inside OpenAI, Anthropic and Gemini, inside Snowflake, Databricks and BigQuery, inside AWS, Azure and GCP, inside every industry platform they already work in. Your data is more useful than ever before, but only if it can be found, purchased, and accessed where it will be used.
The near future
A buyer describes the problem. Your data enters the answer.
Discovery is moving upstream. A machine now assembles the sources that can solve a question, often before the buyer knows which provider to name. The products it can find, parse, compare and access win the deal.
What it takes to support that
- A problem-led product catalog machines can parse
- Coverage, schema, freshness and provenance kept current
- Documented APIs, MCP servers, samples and trial paths
- Metadata syndicated across AI, cloud and partner surfaces
- Measurement for recommendations, referrals and missed demand
Universal discovery layer
One catalog, every surface
Product discovery graph
Machine-readable catalog
The practice
Be where the question gets asked.
Build one machine-readable discovery layer. Syndicate it to every surface where buyers and agents look.
Reach
- One catalog, syndicated everywhere
- AI platforms and assistants
- Cloud marketplace listings
- Partner and reseller ecosystems
- Recommendations and referrals you can measure
Legibility
- A problem-led product catalog machines can parse
- Coverage, schema and freshness
- Provenance and usage examples
- Product fit for the problem being asked
- Evidence kept current, not point-in-time
Access
- Documented APIs and MCP servers
- Samples and self-serve trial paths
- Native paths into customer workflows
- Access the moment a product is found
- No sales call to evaluate
Success looks like
Access
Agents find, understand, compare and access every product. No sales call required.
Visibility
Your data shows up in answers before buyers ever search for your brand.
How we work
Benchmark it. Spec it. Build it.
We start with a short, scoped benchmark of this practice: where you stand, against whom, and what to build first. You own everything we produce, with us or without us.
Benchmark
- Ask the questions your products should win
- Audit what assistants and catalogs can find
- Map every surface where you should appear
Spec
- Define the problem-led catalog and metadata model
- Design syndication across every priority channel
- Set recommendation and referral measures
Build
- Publish catalogs, schemas and usage examples
- Connect API, MCP, sample and trial paths
- Syndicate every product to the right surfaces
Manage
- Monitor how systems describe and recommend you
- Keep coverage, freshness and access signals current
- Turn missed questions into products and placements
The reach benchmark
See whether AI puts your data in the answer.
We ask assistants the questions your products should win, trace what they can find, understand and access, and audit every priority catalog, marketplace and partner surface. You keep the scored readout and the ranked list of moves.
Request the benchmark