Observed signals
What a source or public page actually shows, with source and snapshot context.
Data / methodology / intelligence
From dental signals to commercial intelligence.
DentalCensus combines public evidence with normalized profiles, historical change signals and cross-segment analysis. The result is more than a directory of open facts: it is a reusable intelligence layer for market, territory and customer-specific decisions.
Current public figures are dated snapshots. Collection is rolling and incremental; history stays visible.
Rolling + incremental collectionSnapshot context: July 2026 · historical versions retained
The Data Atlas
Eight modules. One commercial intelligence layer.
Each module answers a different commercial question. The value grows when signals are connected across practices, markets, networks, contacts and time.
01
Observed
Clinical services
Which practices fit the clinical thesis?
- Implants and all-on-X / full-arch
- Invisalign, pediatric, orthodontics, oral surgery and sedation
02
Observed
Technology & digital maturity
What technology context is visible?
- iTero and CEREC mentions on clinic websites
- Online booking widgets and website quality signals
03
Observed
Marketing & demand signals
Where is commercial activity already visible?
- Google Ads, Meta Ads and call tracking
- Membership plans, booking and demand proxies
04
Derived
Network & ownership structure
How is the market organized?
- DSO affiliation and network footprints
- Independent versus affiliated comparisons and change
05
Snapshots
Practice growth & momentum
What is moving between snapshots?
- Review-growth velocity and visible change
- Historical movement by clinic, market or segment
06
Derived
Market potential & territory
Where should a commercial team focus?
- Saturation and market-potential scores
- Demographics, whitespace and metro-to-region context
07
Observed + linked
Contact & decision-maker intelligence
Who can a team engage next?
- Public professional contact and role references
- Clinic, network, decision-maker and freshness relationships
08
Derived
Cross-signal intelligence
What only appears across the intersections?
- Services × technology × marketing × momentum
- Network × territory × contact structure and whitespace
What we build from the data
Observed signals are the foundation. Intelligence is the product.
Our own value comes from resolving entities, preserving change and combining dimensions into a base market model. From there, AI agents and analysts can help configure customer-specific models around a decision.
Derived intelligence
Normalized entities, historical change and intersections that no single source contains.
Customer models
A decision-specific view configured around a territory, workflow, threshold or monitoring need.
How the model is made
A traceable path from source to decision.
The methodology is designed so a customer can understand where a signal came from, when it was observed and what was computed on top of it.
How hidden ownership networks are detected ↗Hidden dental networks: the measured layer ↗
Methodology in six moves
Every layer has a job.
01
Collect
Gather public directory, website, advertising and Census evidence.
02
Resolve
Connect practices, networks, regions and people into reusable entities.
03
Normalize
Keep fields, definitions, missingness and provenance explicit.
04
Compare
Retain dated snapshots so change can be measured instead of guessed.
05
Model
Combine signals into market, territory and customer-specific intelligence.
06
Decide
Deliver a shortlist or view a commercial team can act on and validate.
Signal anatomy
A number without context is not intelligence.
Every useful output carries enough context to be challenged, refreshed and used in a commercial conversation.
See how the intelligence model works ↗Example output contract
Practice signal
SourceClinic website / public directory
SnapshotDated observation retained
StatusObserved · derived · unknown
DefinitionDenominator and field meaning
Missing evidence stays unknown. A derived grouping is labeled as derived. A dated snapshot is not silently rewritten into a current fact.
Trust layer
How we keep the numbers honest.
- Unknown is not "no". Every detected attribute is tri-state; percentages use an honest denominator.
- Website-derived flags mean "the clinic says so on its site", not verified ownership.
- Every published report includes source, snapshot date, N and metric definitions.
- Collection is rolling and incremental; dated history is not silently rewritten.
Use the intelligence layer
Start with a market. Or build the model your team needs.
Questions buyers ask
Definitions before decisions.
Where does DentalCensus data come from?
From our own crawl and enrichment pipeline: 101,000 clinic websites analyzed plus public sources, resolved to 103,674 US dental clinics. Website-derived flags mean 'the clinic says so on its site' — and are labeled that way.
How fresh is the data?
Collection is rolling and incremental, with dated snapshots shipped as new versions. History is never silently rewritten, so you can compare snapshots over time.
What does 'unknown' mean in the data?
Every detected attribute is tri-state: yes, no, or unknown. Unknown is never counted as 'no', and every percentage states its denominator. This is the core honesty rule behind all published numbers.
Can I check coverage in my territory before paying?
Yes — create a free account and search your market. No demo call required; paid plans add exports, deeper signals and API access.
Questions about coverage in your territory? Talk to us.