SolutionsHealthcare
Turn your healthcare processes into AI agents
Trail parses your payer policies, consent forms, scheduling protocols, EHR records and tribal knowledge into one governed context graph — inside your own boundary, where the PHI already sits. Then AI agents run prior authorization, eligibility, referrals and consent on top of it.
Prior authorization
This week- 842total requests
- 61awaiting docs
- 24exceptions
- 88%auto-processed
- AUTH-40182Cedar Ridge Plan45378Approved—
- AUTH-40266Vantage Care Plan29881Checking—
- AUTH-40318Northgate Health72148HeldK. Ferro
- AUTH-40391Blue Harbor Plan27447ReviewD. Osei
- AUTH-40404Cedar Ridge Plan70553Approved—
Trusted by teams at leading enterprises
Top patient access teams
put Trail to work on:
Turn a payer policy into a working agent — in one upload.
Import the coverage policy, consent standard or scheduling protocol your access team maintains. Trail reads it inside your boundary, builds the agent, and shows which clause produced each rule.
- 01
Import process docs
Drop in the payer policy, consent standard or scheduling protocol your team already maintains — PDF, DOCX, scans.
- 02
Agent auto-built
Trail parses them into a context graph. It outlines the agent's role, splits the process into phases, and adds the tools each step needs.
- 03
Review, approve, run
Every learned rule is explained and traced to the exact text it came from. Agents take decisions strictly based on these rules.
“…an authorization to release information expires ninety days from the date of signature; disclosure to a third party may be made only under a current authorization naming that recipient…”
If the authorization on file is over 90 days old or doesn't name the recipient, request a new signed authorization before disclosing.
One graph underneath all your AI agents.
Everything Trail parses collapses into a single governed graph, and everything your patient access teams automate draws from it, whether the work runs in autonomous agents or copilot chats.
Agents - run processes autonomously
- ObserveAUTH-40318 | CPT 72148 · Coverage policy §6.3
- Reason4 of 6 weeks of therapy documented
- ActionHeld. Notes requested from the clinic
- ObserveENC-77390 | Plan 8812 · Benefit grid §2.1
- ReasonPlan switched Jan 1. Deductible unmet
- ActionEstimate reissued, coordinator copied
- ObserveREF-2208 | out-of-network · Scheduling protocol §9
- ReasonReferral logged by phone, none signed
- ActionSlot held. Signed referral requested
- ObservePacket ENC-77412 · Intake SOP §5
- ReasonAdvance directive missing before pre-op
- ActionPacket returned, missing form flagged
Copilot Chats - ask questions and run tasks
Help your agents solve the most complex problems with context graphs.
A request for an out-of-network specialist visit comes in for a plan that requires a referral, three days before the appointment, with a referral noted only in a phone log — can it proceed?
One question. Five kinds of context, each in a different system — the call log doesn't know the notice period, and the scheduling system doesn't know the plan's referral rule.
How the Context Graph works →Coverage & consent policy
Policy context · What does the plan require?
Signed referral · 5 business days' notice
Plan & provider records
System-of-record context · In network or out?
Out-of-network · referral required
Dates & notice period
Computed context · Is notice satisfied?
3 days · two short of policy
Call log & correspondence
Conversation context · What's documented?
Verbal referral logged, unsigned
Scheduling protocol
SOP context · What's the path?
Request signed referral, or reschedule
Connects to the agent platform you already use.
Point the agents your health system already runs, SAP Joule, Agentforce, Copilot, Claude or your own stack, at one Trail brain, through a native Model Context Protocol server, native retrievers, or a plain REST/GraphQL API. Add or switch platforms without re-teaching a thing.
35% of Fortune 500 and 10,000+ companies use Trail
Customer story · SafeRide Health
“Nanonets has significantly automated our driver and vehicle credentialing processes, reducing manual workload by 80%.”
- Per document
- 5 min → 1 min
- Annual cost
- $61,000 → $29,280
- Manual workload
- 80% reduction
- Team productivity
- 500% increase
Teams using Trail see
- 95%Straight-through processing
- 80%Reduction in associated costs
- 15×Quicker turnaround times
Built for how modern
enterprises work.
Trail meets the highest industry standards for your security & compliance. We include all default controls that you'd expect, including SAML SSO, audit logs, IP allow-listing, data lifecycle management, and much more.
- SSO & SCIM
- SAML, OIDC, and SCIM user provisioning with Okta, Azure AD, Google Workspace.
- Role-based access
- Full RBAC and fine-grained access control across orgs, workspaces, agents.
- Audit logs
- Every agent run, approval, and data access is recorded, and streams to your SIEM.
- Private deployment
- VPC, single-tenant cloud, or on-prem. Choose your infrastructure and network policies.
- Data residency
- Pin data processing to US, EU, APAC regions. Customer data never leaves your boundary.
- Human-in-the-loop
- Configure approval gates to route edge cases to Slack, Teams, or email for review.
- Encryption at rest & in transit
- AES-256 at rest, TLS 1.3 in transit. BYOK supported for all customer-managed keys.
- Usage quotas & rate limits
- Per-team budgets and API quotas with real-time cost dashboards. No surprises.
Security built in, not bolted on.
Your data and your company brain never leave your boundary. A HIPAA BAA is available, and PHI can stay entirely inside your own VPC with storage pinned to a region. Independently audited every year, with compliance controls enforced by the platform — not promised on a page.
Held and current — not roadmap targets.
SOC 2 Type IIOngoing security controls
GDPREU data protection
ISO 27001Global ISMS standard
HIPAABAA on enterprise plansTurn your healthcare processes
into a competitive advantage.
Bring one payer policy and one real-life authorization. We'll show you how Trail proposes rules and builds an agent, which then takes the correct decision and acts on it.
Prefer to read first? Read the FAQ →