Healthcare Data & AI Systems Development
We build the data and AI systems healthcare organizations actually run on — pipelines for claims, remittances, eligibility, and clinical records; de-identified datasets; analytics; models with evaluation harnesses; and the training environments where the next generation of healthcare AI learns the job. Built with AI acceleration, reviewed by humans, inside a HIPAA perimeter.
What We Build
Data Pipelines
837 claims, 835 remittances, 270/271 eligibility, FHIR R4 resources, and practice-management exports landed in a warehouse you control, with lineage from source to report.
De-Identification and Synthetic Data
Safe Harbor and Expert Determination de-identification, and synthetic encounter generation for development, testing, and training without a single real patient.
Analytics and Reporting
Denial reasons by payer, days in A/R, underpayments against expected allowed amounts, and clinician dashboards: the reporting discipline from our billing metrics guide, built into your data.
Model Development and Evaluation
Retrieval over your clinical and billing knowledge, task-specific models, and evaluation harnesses with ground truth, so a model’s accuracy is measured before it is trusted and logged after.
Training Environments for Healthcare AI
Simulated practice systems where models learn and are graded on real healthcare work. Early access for teams developing healthcare models.
AI-Accelerated Delivery
The lab builds with AI acceleration and ships working software weekly, with every AI-generated change reviewed and tested by an engineer.
Training Environments for the Next Generation of Healthcare AI
Most healthcare AI is trained on whatever text exists and then asked to generalize to work it has never done. The useful signal is in the work itself: the claim that denied and why, the authorization that lapsed on day eight, the note that did not support the code. Every one of those has a verifiable right answer.
A training environment makes that signal usable. It is Cannect™ loaded with synthetic patients, real payer rules, and real workflows, in which a model must document an encounter, code it, check eligibility, submit the claim, read the denial, write the appeal, and reconcile the remittance. Each task is scored programmatically where a right answer exists and graded by billers and clinicians where judgment is required. The model learns the job; patient data stays where it belongs.
This is the data-management approach behind the lab’s own Wolfware models, and we are building the first environments for outside teams now. If you develop or evaluate healthcare models and are arguing about where the training signal should come from, talk to us.
Managed Data Use
A BAA Before Any PHI
When protected health information is involved, a business associate agreement comes first, and the data stays under your control in your warehouse.
Processing Inside the Perimeter
Models run behind per-practice credentials. Provider keys never ship to customer systems, and PHI is never sent to a third party for processing.
No Identifiable Data in Training
Synthetic by default. De-identified only under Expert Determination with written authorization. Never sold, in any form.
Decision Records
Every model recommendation in a system we build is logged with the model that produced it, so behavior can be audited by your compliance team.
How an Engagement Runs
- Discovery. The questions you need answered, the sources that hold the answers, and who owns what. Output: a written plan and a BAA if PHI is involved.
- Architecture. Pipelines, the warehouse, de-identification steps, and where models touch the workflow. Output: a diagram your compliance officer can approve.
- AI-accelerated build. Working pipelines and reports weekly; every change reviewed and tested by an engineer.
- Evaluation. Models measured against ground truth before anyone relies on them, with the results written down.
- Operation. Monitoring, decision-record review, and the monthly numbers you chose in discovery.
Frequently Asked Questions
What data sources can you work with?
Claims and remittances (837 and 835), eligibility (270/271), FHIR R4 clinical resources, practice-management and EHR exports, scheduling and communications logs, and payer policy documents. Pipelines land the data in a warehouse you control, with lineage from source to report.
Do you train models on patient data?
Not on identifiable data, and never without written authorization. Model development uses synthetic encounters by default. Where real-world data adds something synthetic data cannot, it is de-identified under HIPAA’s Expert Determination standard with the practice’s authorization under its business associate agreement. We do not sell patient data in any form.
What is a training environment for healthcare AI?
A simulated practice system, Cannect™ loaded with synthetic patients, real payer rules, and real workflows, in which a model must do healthcare work: document an encounter, code it, check eligibility, submit the claim, read the denial, write the appeal, reconcile the remittance. Each task has a verifiable right answer, and people who do the work for a living grade the result. It is how models learn the job before they touch a real chart, and it is the data-management approach behind the lab’s own Wolfware models.
How does AI-accelerated development stay reliable?
The acceleration is in drafting and iteration, not in judgment. Engineers specify, review, and test every change; AI-generated code never ships unreviewed; and the systems we build log their own model recommendations to decision records so behavior can be audited after the fact. You get working software weekly and a paper trail for all of it.
Can you build this for an organization that is not a billing client?
Yes. Practices, specialty groups, health-technology companies, and teams developing healthcare models all work with the lab directly. A business associate agreement comes first when protected health information is involved; the data and the warehouse stay under your control.
Start With the Question You Need Answered
Which payer denies you most and why; what your underpayments add up to; whether a model is accurate enough to trust; where the next generation of healthcare AI should learn the job. Tell us the question and we will tell you what it takes to answer it.
See Canis AI on Your Claims
Watch the models work inside the Cannect™ product tour, read what makes an EHR truly AI-native, or have our team run a free baseline review showing where your current billing loses accuracy.