Pilot & MVP development customer
One practice, chosen because it is the hard case.
AiRadics is developed inside a working general practice rather than against a specification. Every module ships against real channel data, a real payer mix and a real patient book before it is offered to anyone else. A pilot is only worth something if what is learned there transfers — and most single-clinic pilots do not, because most clinics are narrow: one payer, one demographic, one service line. This one is deliberately the opposite.
4payer classes live
Medicaid, CHIP, Medicare Advantage and commercial PPO, in one book. Each behaves differently on acquisition cost, recall and lifetime value, so the model has to separate them rather than average them.
Mixedcatchment demographics
Owner-occupier and renter households, a wide income band and multiple language groups inside one catchment — the segment variance a single-demographic clinic never produces.
Fullservice line
Preventive through restorative, plus third-party patient financing. The full set of procedure categories a general dentist bills, which is what the ontology has to cover.
Since 2022operating history
Three years of channel spend, collections and procedure mix already in the book, which is the history a model needs before it can say anything about attribution.
The practice
A general practice in North Houston, operating since December 2022.
The point is coverage. A practice serving one payer and one demographic teaches the ontology one path. This practice exercises nearly all of them at once, which is why the axioms it produces — what worked for which segment under which payer, and what did not — generalise to the next clinic instead of having to be relearned.
The pilot practice is under common ownership with Quadfence. It is therefore a related-party engagement, stated plainly rather than left to be found. It is a development engagement and not a customer base, and is held to the same standard a paying stranger would hold the product to — the criteria below are published before they have been passed, not described afterwards.
What success looks like here
Four things have to be true before a second practice is sold to.
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Not yet passed
The engine moves the numbers
New patients per month, cost per acquired patient split by payer rather than blended, and lifetime value against acquisition cost. Measured against the practice’s own three-year history, not against a projection.
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Not yet passed
The governance gate holds at volume
Every generated asset clears FTC, ADA, state dental board and platform health-content rules before publication, with a pass rate that can be quoted and a provenance record for each decision. A gate that only works on a handful of assets is not a gate.
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Not yet passed
The ontology produces reusable axioms
Not conclusions about this clinic, but structured results — which channel produced which patient type at what cost, under which payer, in which catchment condition, and what failed. Enough of them, across enough segments, to be worth something to a practice never seen before.
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Not yet passed
A second practice stands up in a day
Its own cloud project, channels connected, catchment modelled, site generated — provisioned from the blueprint rather than built. This is the number that decides whether an agent-led sale works at all, and it is the one the round exists to prove.
None of these are passed yet, and that is not going to be claimed otherwise while the work is in front of us. They are the criteria, published in advance, so that the answer when asked is a number rather than a story.
Why one clinic is enough to start
What the pilot produces is not a case study. It is the ontology.
The engine does not carry conclusions from clinic to clinic. It carries structure: the categories a dental market decomposes into, and an axiom set recording what moved which segment, under which payer, in which catchment condition — and what did not.
| Transfers — built once, reused at every practice after | Does not — a second clinic is not a copy of the first | |
|---|---|---|
| Vocabulary | The dental ontology itself. Payer classes, procedure categories, catchment variables, channel taxonomy, patient journey states. The vocabulary the engine reasons in. | The catchment. Every practice sits in its own market and needs its own model. What transfers is the method for building it, not the answer. |
| Results | Segment axioms. Which channel produced which patient type at what cost, split by payer rather than blended. A Medicaid recall patient and a PPO restorative patient are not the same acquisition and should never share an average. | Absolute numbers. A cost per patient at one clinic is not a forecast for another. The transferable object is the relationship, not the figure. |
| Failures | Negative results. What was tried and did not work, held with the same weight as what did. Most marketing knowledge is lost because nobody records the failures. | The payer mix. The pilot’s is unusually broad, which is the point — a narrower practice uses a subset. A practice with a mix not yet seen adds to the ontology rather than reading from it. |
| Rules | The governance rule set. FTC, ADA and state dental board advertising rules compiled once into a gate that runs before generation. | |
| Plumbing | The pipelines and the console. Channel ingestion, the client warehouse, the hub aggregation, the generation and publishing path, deployment and monitoring. |
This is also the honest answer to why there are not twenty pilots. Twenty shallow clinics would produce twenty thin datasets. One clinic exercising every payer class, a mixed catchment and a full service line produces an ontology the twenty-first can actually use.