For investors

An order-of-magnitude gain in healthcare operations, already running

Dental groups hold years of charts, notes, and schedules that nobody has time to read. Clarific turns that record into briefings, population views, and plans, and it runs weekly in production.

The thesis: AI that truly provides an order-of-magnitude increase in healthcare operations, while reducing liability and increasing patient care standards. Operations improve because the record is read for every patient. Fewer missed details is designed to mean less exposure, and a care team that starts each visit better informed.

Market

Market sources pending owner sign-off. Third-party estimates vary by scope and method.

Traction, from the live platform

Offices
4
Patient records
16,000+
Active patients
9,000+
AI briefings live
900+
Production AI surfaces
7
Data refresh cadence
weekly

Figures reflect a working dental group running Clarific across its offices.

Unit economics

The cost of a chart splits in two. What it costs to run is the token line: the AI usage behind each briefing. What Clarific is worth reflects the IP built on top of it: the data plumbing, the guardrails, and the clinical standards.

Token optimization is already done

Model tiering

The heavy reasoning model runs only on the main pass; cheaper models handle the sub-tasks.

Context caching

Material that repeats is not paid for twice.

Scoped retrieval

Each call gets the relevant slice of the record, not the whole record.

Cost per chart

≈ $0.37 per chart in AI tokens at list API prices

  • $0.232 Main reasoning passthe heavy model, once per chart
  • $0.135 Three lighter support passesvisit digest $0.053, risk narrative $0.045, differential reminders $0.037

From roughly 40,000 tokens per chart, measured across 2,500+ production dossiers (n = 2,562 metered generations).

This is a list-price equivalent of measured token usage, not billed dollars. Generation currently runs on a flat subscription, so there is no invoice to quote.

Time saved per chart, by patient age

Computed over 2,232 real patient charts. Older patients carry longer records, so the saving grows with age.

  • Under 18~9 min
  • 18–34~11 min
  • 35–49~15 min
  • 50–64~20 min
  • 65+~26 min

Overall average ~19 min (median 17), marked by the dashed line, minutes saved per chart.

Record counts are measured from 2,232 real charts; minutes are modeled from those counts at conservative reading rates.

Defensibility

Built inside a working group

The product was shaped by use across 4 offices, not by a demo environment.

Receipts doctrine

Every AI-presented figure carries its source. Models narrate, tools compute, and money figures are never AI-final.

Documented evidence trails

Each finding links to the records behind it, so reviewers can check the work quickly.

Compounding data model

One canonical patient record feeds every surface, so each new surface starts with the whole chart.

A regulatory posture as a moat

Clarific is designed as non-device clinical decision support from the start. That discipline shapes every surface and is hard to retrofit.

Owner-editable guardrails

The rules, wording, and playbooks that govern the AI live in plain documents the practice owners edit and approve, not in code.

Designed to reduce liability exposure and raise standards

Missed history is where risk hides. A medication in an old note, a condition mentioned once, a recall that slipped. Clarific reads the whole record for every patient and presents what it finds with evidence attached.

It is designed to give each finding a documented evidence trail, and to start every visit with a better-informed care team. Clarific is non-device clinical decision support: no clinical determinations are made by the software, and clinicians decide.

Built by a dental group, for itself first

Clarific is a West Annapolis Group venture. It began inside a working dental group that needed this for its own patients, and it was built, used, and refined there. Now it is being prepared for other groups.

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