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Healthcare Payor · Prior Authorization

Prior authorization, automated.

Reference solution for a Fortune-500 healthcare payor looking to automate utilization-management decisioning end-to-end. Production-shaped architecture; deployable into the payor’s environment in a 12-week pilot.

Reference
Solution architecture in repo · pilots open
DPDP-aligned
Audit, citations, escalation by design
12 weeks
Pilot SOW to shadow run

The problem

Prior authorisation is the most expensive form of paperwork in healthcare. Large payors run utilisation-management operations of substantial scale — typical Indian health insurers and TPAs review hundreds of thousands of cases per month against thousands of policy clauses and clinical guidelines. Every decision needs an audit trail dense enough for a regulator. The work is rules-heavy, document-grounded, and high-stakes — exactly the shape LLM-driven decisioning is good at, if you can show your work.

Honest framing: Vihaya is pre-revenue. The architecture below is shipped in the repo with full unit-test coverage; the first paid pilots are the immediate roadmap. Industry-impact figures (e.g. “$200M+ at F500 scale”) widely cited by US peers like Distyl describe theirdeployed customer outcomes, not Vihaya’s.

The solution

A single agentic decisioning service. One POST, one immutable decision, one audit chain, and a queue of low-confidence cases routed to a human reviewer.

What it does, end-to-end

Behaviour, not architecture. The underlying engineering substrate is proprietary; what follows is what the customer’s reviewers, compliance team, and regulators experience.

  1. 1
    Receive

    PA request arrives via the payor's existing intake channel; every action from this point is recorded in an append-only audit log mapped to SOC 2 control families.

  2. 2
    Ground

    The agent retrieves the relevant policy clauses, clinical guidelines, and any prior decisions for the same subject — so the eventual answer is anchored in the customer's own source-of-truth, not the model's general knowledge.

  3. 3
    Reason

    The agent applies the policy criteria to the case, produces a structured decision (approve / partial / deny / escalate), and attaches a model-reported confidence score and citations to the exact source passages it used.

  4. 4
    Decide

    Above the configured confidence floor the decision is recorded immutably and returned. Below the floor the case is force-routed to the customer's existing human reviewer with the agent's recommendation, rationale, and citations attached.

  5. 5
    Audit

    Every step of the chain — receipt, retrieval, model call, outcome, escalation, reviewer disposition — is linkable back to the underlying source. The audit chain is reconstructable from cold storage years later for IRDAI, RBI, or regulatory inspection.

Why this shape

  • One service, one shape. POST /api/pa/decide. The payor’s intake doesn’t change — only what happens after the request lands.
  • Confidence floor is a safety primitive, not a UI flag. Below threshold → escalation, regardless of model verdict. Tunable per drug class.
  • Citations are not optional. The decision row stores citation chunk IDs. Reviewers see the exact policy text the agent grounded on.
  • The audit trail is the product. Every step is one row in an append-only compliance log mapped to controls. Decisions are reconstructable from cold storage.

What ships in 12 weeks

  • ›Service deployed to your dev/staging environment with your UM corpus indexed
  • ›Inbound adapter for your intake (X12 278 / FHIR / portal API / file drop)
  • ›Outbound adapter back into your case-management / PA portal
  • ›Golden eval set of ≥50 scenarios per drug class, signed off by your medical director
  • ›Confidence threshold locked to your medical director's risk tolerance
  • ›OpenTelemetry traces exported to your observability stack
  • ›Runbook, threat model, retraining playbook

Honest caveats

  • Vihaya is pre-revenue and pre-SOC 2 Type II. The architecture is production-shaped; the audit firm is on the roadmap.
  • The bundled reference policies in the open-source repo are illustrative, not clinical. Real engagements replace them with the payor’s UM corpus.
  • “$200M+ forecasted impact” bakes in PA volume, per-case ops cost, automation rate, and reviewer-hour savings. The same math applied to your specific book is part of the diligence step.

Want to be the first paid pilot?

12-week design-partner pilot. Your corpus, your environment, your eval set, your medical director’s threshold. Materially better economics in exchange for being the lighthouse.

Talk to us about a design-partner pilot