Primary
Inside the working business
Field research within live operations: observation of real workflows, operator interviews, and structured surveys we run ourselves.
DataDig AI
DataDig AI
Pet Industry R&D
Research & Development
The pet industry, finally measured.
DataDig AI builds the data foundation the pet industry has never had — primary research, operating benchmarks, and the applied systems that run on top of them.
Primary
Field
First-party
Operators
Zero-party
Intent
Secondary
Registry
01Sources
Most industry data is estimated. Ours is collected.
Four independent collection channels, cross-validated against each other. Nothing enters the record on a single source.
Zero-Party
Volunteered at intent
Assessments, configurators and intake tools inside products people already use — given deliberately, not inferred.
First-Party
Captured as work happens
Operating data from systems running inside live pet businesses: bookings, capacity, pricing, service history and outcomes.
Secondary
Validated against the field
Published industry, regulatory, registry and scientific sources — reconciled with what we observe directly.
02Output
What we build from it.
A reference layer, an operating layer, and the software that turns both into decisions.
Indexes
Breed, genetics, coat, health, training, breeding, whelping
Structured reference data across the animal itself — the layer that makes it possible to reason about an individual, not a category.
Benchmarks
Regional operating truth
Pricing, labor, capacity, equipment cost and utilization, by region and category.
Intelligence
Analysis for decision makers
Built for operators, manufacturers, brands, franchisors and investors.
Systems
The research doesn't get handed off. It gets built.
Applications, applied AI and augmented reality built directly on the data — each one becoming another instrument for collecting the next round.
03Method
The research and the engineering happen in the same room.
Research firms study an industry and hand findings to someone else. Software companies build first and learn afterwards.
We do both, which makes the loop short — and every product we ship becomes another instrument for collecting the next round of data.
01
Observe
Field, operator and registry collection
02
Structure
Normalized into indexes and benchmarks
03
Build
Systems deployed into live operations
04
Return
Deployment generates the next dataset
04Ventures
Where the research is deployed.
Operating companies built on the data layer, each returning first-party signal back into it.