01
FHIRant
Policy-aware healthcare workflows
Connect payer criteria, clinical evidence, documents, codes, and FHIR resources into structured tasks and explainable next actions.
FHIRant
p2mesh
Private AI infrastructure for healthcare
Healthcare intelligence, closer to the data.
FHIRant brings policy-aware, FHIR-native workflow intelligence. p2mesh is building lean inference and predictive compression for capable AI beyond a cloud-only model. Together, the cooperation explores a more private path for regulated healthcare operations.
FHIR / LOCAL / REVIEWABLE
FHIRANT × P2MESH / BUILDING A PRIVATE PATH© 2026
01 / Shared intent
The goal is not to move every healthcare workload into one more remote service. It is to put the right intelligence at the right boundary, with traceable workflow logic and deliberate human control.
A private-AI path from policy to action.
FHIRant structures the operational problem: policies, evidence, FHIR resources, documentation gaps, and routeable outcomes. p2mesh explores how compact model assets and lean inference can bring capable AI closer to the systems that already hold sensitive data.
02 / Cooperation architecture
Different strengths.
One governed workflow.
The cooperation concept separates workflow intelligence, inference infrastructure, and operational control so each pilot can define its own data boundary, review process, and integration path.
02
p2mesh
Lean inference closer to data
Explore compact, quantized model delivery through a lean runtime and predictive compression layer designed for local and distributed environments.
03
Your control plane
Reviewable operational boundaries
Keep deployment, access, logging, model selection, human review, and downstream routing explicit within the pilot architecture.
03 / The journey
From fragmented inputs to a reviewable outcome.
A focused vertical journey keeps the infrastructure in service of the healthcare workflow—not the other way around.
01
Capture the request
Bring together FHIR resources, orders, clinical notes, policies, PDFs, and other authorized inputs.
02
Identify the applicable policy
Resolve payer, plan, line of business, service, and effective-date context before evaluating criteria.
03
Build the evidence checklist
Turn source-linked requirements into a structured, auditable set of documentation and clinical evidence needs.
04
Run bounded inference
Apply selected model capabilities closer to the authorized data boundary through the p2mesh runtime concept.
05
Make readiness explainable
Show matched evidence, missing evidence, uncertainty, and source context for human review.
06
Route the outcome
Send work to the payer path, back to the provider, or into manual review according to configured workflow rules.
04 / Pilot principles
Private by architecture.
Explainable by workflow.
The strongest first pilot is narrow enough to validate the full path: one workflow, one data boundary, one review team, and measurable acceptance criteria.
01
Local where it matters
Evaluate where inference should run based on data sensitivity, latency, infrastructure, and operational constraints—not a cloud-only assumption.
02
Compact by design
Test selected quantized models, measured memory behavior, and explicit quality gates for the target workflow and hardware.
03
Human review stays visible
Keep uncertainty, provenance, approval steps, and escalation paths clear before any operational outcome is acted on.
Private beta / focused pilots
Bring one healthcare workflow worth proving.
Start with a contained prior authorization, payer-policy, specialty pharmacy, or clinical-documentation workflow. We’ll frame the data boundary, target environment, review controls, and success measures together.
Discuss a FHIRant × p2mesh pilot