Our LLM. Not OpenAI. Not Anthropic. Yours.
A language model trained specifically for dentistry, running on your clinic's local GPU. Zero cloud calls. Zero per-token cost. Zero third-party dependency.
Why we don't use GPT-4 or Claude
Cloud LLMs are brilliant for generic tasks. For a dental clinic with sensitive health data and high AI query volume, they are the wrong choice.
Unpredictable per-token cost
Every query to OpenAI or Anthropic is billed by tokens. In a clinic with 50-100 AI queries a day, the cost grows significantly and, above all, varies month to month. Local hardware pays back in 12-18 months with zero marginal cost.
Vendor lock-in
If OpenAI changes prices, deprecates a model, or goes down, your clinic stops working. Your own LLM on your server doesn't depend on the health of a third party.
Health data outside the EU
Queries sent to OpenAI travel to US infrastructure. That forces SCC contractual clauses and creates GDPR friction. On-premise, data never leaves the clinic's server.
Network latency
A cloud call requires a round trip over the internet. Local is tens of milliseconds instead of seconds, and doesn't depend on your connection quality nor the provider's service status.
Trained on real dental data
Our LLM is not a generalist model with a dental prompt. It's trained on a profession-specific corpus.
How we evaluate it
We don't publish marketing benchmarks. We measure AI-Doctor concordance in real production and publish the figures.
- AI-Doctor concordance measured continuously with integrated outcome tracking. Every validation or correction by the dentist feeds the improvement cycle.
- Every LLM answer includes the bibliographic sources it used. The dentist can inspect and verify them.
- MUTEX between Vision AI and Text AI: two models never run in parallel on the same GPU. This guarantees precision over throughput, a deliberate decision.