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Basys.ai

Also known as: basys.ai

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Payer side agentic AI for prior authorization and utilization management: encodes payer policies with generative AI, auto approves up to 90 percent of requests, and routes complex cases to clinicians. Mayo and Eli Lilly backed.

basys.ai is a payer side agentic AI platform for prior authorization, utilization management, clinical auditing, and fraud, waste, and abuse review.

Its engine, trained on over 10 million patient records and claims through its Mayo Clinic data partnership, automates encoding of payer policies with generative AI and can auto approve up to 90 percent of prior authorization requests while directing complex cases to clinicians, with a generative AI chat feature answering member and provider transparency queries.

Founded in 2022 by Harvard health data science graduates Amber Nigam and Jie Sun, backed by Eli Lilly, Mayo Clinic, and Nina Capital, and distributed through a Smart Data Solutions partnership reaching health plans and TPAs. Processes requests over FHIR with X12 data exchange, positioned as integration light for health plan deployment.

Vendor details

Canonical URL

https://basys.ai

Category

Healthcare agent

Funding status

$2.4M seed (2023) after oversubscribed pre-seed led by Nina Capital with Eli Lilly, Mayo Clinic, Two Lanterns, AMV, and Chaac participating

Company status

independent

Use cases & customers

Primary use cases

prior authorization auto-approval for health plansutilization managementclinical auditingfraud waste and abuse review

Target customers

health plansTPAshealth systems

Deployment options

SaaS

Integrations

FHIR standard for request processing and real time data connections; X12 standard for HIPAA compliant data exchange; positioned as plug and play, integration light; Smart Data Solutions partnership embeds it in payer clearinghouse workflows.

Agentic Index coverage score

6.0 / 14 capabilities · 43%

Integrations & Tool Calling Full

SourceFHIR standard request processing with real time data connections and X12 standard HIPAA compliant data exchange, positioned as plug and play, basys.ai payer pageread 2026-07-22

Workflow Orchestration Partial

SourceAutomates the prior authorization decisioning pipeline end to end with auto approval and routing, though scope is a single decision workflow rather than cross system orchestration, basys.ai materialsread 2026-07-22

Knowledge Grounding & RAG Full

SourceAutomated generative AI encoding of payer policies is the core technology, trained on over 10 million patient records with specialized disease area expertise in MSK, surgery, and cardiology, basys.ai materialsread 2026-07-22

Human Oversight & Guardrails Full

SourceDocumented escalation split: appropriate cases auto approved while complex cases are directed to clinicians, with interpretability and a transparency chat for members and providers, basys.ai and partner materialsread 2026-07-22

Security, Identity & Governance Partial

SourceHIPAA compliant exchange via the X12 standard and secure positioning stated first party, but no named attestations in retrieved materials, basys.ai materialsread 2026-07-22

Observability & Auditability Partial

SourceInterpretable and explainable decisioning is a stated design goal with a transparency chat surface, though no audit console is documented, basys.ai materialsread 2026-07-22

Memory & State Persistence Unable to verify

SourceNo documented persistent memory capability, basys.ai materialsread 2026-07-22

Deployment & Data Residency Partial

SourceSaaS delivery; no self host or residency options documented, basys.ai materialsread 2026-07-22

Prebuilt Agents, Templates & Packs Unable to verify

SourceSingle decisioning engine across UM modules without an agent or template library, basys.ai materialsread 2026-07-22

Triggers & Channel Coverage Partial

SourceReal time processing driven by incoming authorization requests over FHIR; no broader trigger or channel machinery documented, basys.ai materialsread 2026-07-22

Model Flexibility & Routing Partial

SourceHybrid generative AI plus deep learning engine documented, without customer model choice, basys.ai materialsread 2026-07-22

APIs, SDKs & MCP Extensibility Unable to verify

SourceNo customer facing API, SDK, or extensibility surface documented beyond standards based intake, basys.ai materialsread 2026-07-22

Testing, Debugging & Optimization Unable to verify

SourceNo customer facing testing or evaluation tooling documented, basys.ai materialsread 2026-07-22

Browser & Computer Use Unable to verify

SourceOperates over FHIR and X12 data exchange, not browser or computer interface control, basys.ai materialsread 2026-07-22

The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded

Pricing

Contact sales

What is public

No pricing is published. Positioning emphasizes achieving target savings for health plan partners, with distribution direct and via Smart Data Solutions.

Variable cost rationale

Payer UM platforms typically price per member or per authorization volume; basys.ai does not publish its basis.

Sales call required

Yes, required for paid access

Free / trial

None public

Agentic Index verified 2026-07-22

Alternatives to Basys.ai

The closest documented capability profiles to Basys.ai among healthcare agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.

  • Cedar6.5 / 14Adds documented Prebuilt Agents, Templates & Packs
  • Develop Health6.5 / 14Fuller documented coverage on Workflow Orchestration and Triggers & Channel CoverageBasys.ai vs Develop Health →
  • OpenEvidence6.0 / 14Adds documented Prebuilt Agents, Templates & Packs
  • XpertDox6.5 / 14Adds documented Browser & Computer Use
  • AKASA6.0 / 14Adds documented Prebuilt Agents, Templates & Packs
  • Almanac Health6.0 / 14Adds documented Prebuilt Agents, Templates & Packs and Testing, Debugging & Optimization, among others

Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded

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