Overview
Penguin Ai - Utilization Management for Healthcare Payers
Penguin Ai enables healthcare payers to cut administrative costs, accelerate authorization turnaround, and deliver superior decision consistency by combining intelligent AI automation with human-in-the-loop oversight at every step.
The platform ingests structured and unstructured clinical data to automate intake, validate requests, and support adjudication - aligning decisions with CMS guidelines, payer policies, and clinical criteria. The result is full lifecycle automation that goes beyond CMS-0057-F compliance.
Designed for enterprise deployment on AWS, Penguin Ai integrates with EDI (278), provider portals, and Utilization Management (UM) systems to deliver scalable, secure, and compliant prior authorization operations.
Core Capabilities
Authorization Rules and Decision Support
- Evaluate requests against payer rules, benefit design, CMS NCD/LCD, and clinical criteria
- Automate triage, apply approval logic, and classify requests into approve, deny, or review
- Generate medical necessity recommendations with transparent rationale
Workflow Orchestration and Integration
- Integrate with UM systems via APIs and EDI (278) transactions
- Route cases dynamically based on rules, SLAs, and documentation gaps
- Track request status across lifecycle stages with configurable notifications for approvals, denials, and follow-ups
Compliance and SLA Management
- Enforce payer policies, NCQA and URAC regulations, and clinical guidelines with built-in validation
- Track SLAs and trigger escalations for aging or non-compliant requests
- Maintain audit-ready documentation aligned to CMS guidelines
Key Benefits for Healthcare Payers
- Reduce administrative costs - Automate manual intake, triage, and adjudication workflows to lower operational overhead
- Accelerate authorization turnaround - AI-driven decision support enables faster processing with high-confidence auto-approvals
- Improve decision accuracy and consistency - Healthcare-native NLP combined with payer-specific policy logic ensures reliable outcomes
- Strengthen compliance and audit readiness - Explainable rationale tied to CMS guidelines, payer policies, and clinical criteria
What Sets Penguin Ai Apart
- Beyond CMS-0057-F: Automates the full prior authorization lifecycle, not just interoperability requirements
- Healthcare-Native AI + Policy Logic: Combines clinical NLP with payer-specific rules for accurate, consistent decisions
- Explainable and Audit-Ready: Transparent rationale aligned to CMS guidelines and payer policies
- Automation with Control: High-confidence auto-approvals with human review for complex cases
AWS Deployment
Penguin Ai is designed for secure, scalable enterprise deployment on AWS with HIPAA-aligned processing. The platform integrates with existing payer infrastructure including EDI (278) workflows, provider portals, and Utilization Management systems to enable rapid adoption without disrupting current operations.
Who Should Use Penguin Ai
Penguin Ai is built for healthcare payers seeking to modernize prior authorization operations - including health plans, managed care organizations, and third-party administrators looking to reduce costs, improve turnaround times, and maintain regulatory compliance across their authorization workflows.
Highlights
- Automated Intake and Adjudication: Penguin Ai ingests structured and unstructured clinical data across EDI (278), API, and document workflows to automate the full prior authorization lifecycle. The platform evaluates requests against payer rules, benefit design, CMS NCD/LCD, and clinical criteria to classify requests into approve, deny, or review - delivering high-confidence auto-approvals while routing complex cases for human review.
- AI-Driven Decision Support with Compliance Built In: Healthcare-native NLP combined with payer-specific policy logic ensures accurate, consistent authorization decisions aligned to CMS guidelines, NCQA and URAC regulations. Explainable rationale provides audit-ready documentation, while built-in SLA tracking and escalation triggers keep operations compliant and on schedule.
- Scalable AWS Deployment with Enterprise Integration: Designed for secure, HIPAA-aligned enterprise deployment on AWS, Penguin Ai integrates with existing Utilization Management systems, EDI (278) transactions, and provider portals. Dynamic case routing, configurable notifications, and lifecycle tracking enable payers to modernize prior authorization operations without disrupting current infrastructure.
Details
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Features and programs
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Pricing
Dimension | Description | Cost/12 months |
|---|---|---|
Monthly subscription fee | Monthly subscription fee | $50,000.00 |
Transaction fee | Transaction Fee | $5.00 |
Vendor refund policy
All fees are non-refundable. Either party may terminate for an uncured material breach (30 days' written notice, including non-payment) or if a party ceases operations. Termination ends platform access and future fees but does not refund pre-paid or unused fees. The sole exception: if Penguin Ai terminates in response to a third-party intellectual property claim, it will refund pre-paid, unused fees for the terminated portion of the term.
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Delivery details
Utilization Management
- Amazon EKS
- Amazon ECS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
- Product overview
A HIPAA-aware backend service that automates Prior Authorization (PA) and Utilization Management (UM) review. It ingests clinical documentation, performs OCR and PHI-aware extraction, evaluates cases against medical-necessity guidelines using large language models on Amazon Bedrock, and produces reviewer-ready determinations, letters, and analytics.
Key capabilities: PA intake and processing, longitudinal patient summarization, inpatient and concurrent review, automated determination letters, dashboards and turnaround-time analytics, and built-in content guardrails with PHI/PII redaction.
The service exposes a REST API (OpenAPI/Swagger) and runs from a single container image in two roles: an API server and one or more background workers.
- Product at a glance
Runtime: FastAPI + Uvicorn (Python 3.11), non-root container Listening port: 7000/tcp (HTTP, behind a TLS-terminating load balancer) Health endpoint: GET /health (unauthenticated, no PHI) API docs: GET /docs, GET /redoc State: stateless - all persistent data lives in external, buyer-owned AWS services
- Prerequisites
Provision the following in your own account and supply their coordinates via environment variables:
- Amazon DocumentDB (MongoDB-compatible, TLS) - primary datastore: cases, users, guidelines, audit logs
- Redis (Amazon ElastiCache) - task broker, result backend, and cache
- Amazon S3 - source documents, OCR output, generated letters
- Amazon Bedrock (Claude models enabled in-Region) - LLM inference
- OCR service (e.g., Amazon Textract) - text extraction from clinical PDFs/images
- Enterprise identity provider (OAuth 2.0 / SAML SSO) - sign-in and role mapping
- Amazon ECS or EKS + Application Load Balancer - runtime and HTTPS termination
Enable Bedrock model access in the deployment Region, and keep DocumentDB, Redis, and S3 in the same VPC/Region as the compute.
- Deployment (high level)
- Subscribe to the product and pull the container image from Amazon ECR (URI and tag shown on the product's Launch page).
- Provision the prerequisites in Section 3.
- Store secrets (database/Redis passwords, identity-provider client secret, OCR key, JWT signing keys) in AWS Secrets Manager or SSM Parameter Store, and inject them as environment variables. Never bake secrets into the image.
- Deploy on ECS or EKS:
- Run one API service (container port 7000) behind an ALB with health check path /health.
- Run one or more worker services (same image, worker command) to process background tasks.
- Attach an IAM task role / IRSA with least-privilege access to Bedrock, S3, Secrets Manager, and KMS (Section 9).
- Verify: confirm the service is healthy, then: curl https:///health # -> {"status":"ok","service":"...-api","version":"1.0.0"} Open https:///docs for the API reference, and complete an SSO sign-in to confirm end-to-end authentication.
- Scale by running additional API and worker containers behind the load balancer.
- Configuration (high level)
Configuration is entirely environment-variable driven. The main groups are:
- Datastore: MONGO_URL (TLS + retryWrites=false), or host/user/password/db.
- Cache and broker: REDIS_HOST, REDIS_PORT, REDIS_PASSWORD, REDIS_SSL.
- Storage: AWS_REGION, AWS_BUCKET_NAME.
- AI / Bedrock: Claude model/inference-profile ARNs and model names.
- Authentication: identity-provider client ID/secret/tenant and JWT signing keys (JWT_SECRET_KEY, SECRET_KEY) with configurable token lifetimes.
- OCR: OCR service endpoint/key.
- Security and ops: CORS allow-list, security headers/CSP, PHI_TMPDIR, log level, and CloudWatch metrics export.
Optional feature flags gate modules such as inpatient review, letter generation, and intelligent allocation; leave them at defaults unless enabling a specific capability.
- Data, sensitive information and encryption
Where data is stored - the container holds no durable data:
- PHI, users, guidelines, audit logs -> Amazon DocumentDB (buyer-owned).
- Documents, OCR output, letters -> Amazon S3 (buyer-owned).
- Task/cache data -> Redis (ephemeral).
- Transient PHI temp files -> an owner-only container directory (PHI_TMPDIR, mode 0700/0600), deleted after use - never world-readable /tmp.
- Secrets -> AWS Secrets Manager / SSM (buyer-owned).
Encryption:
- In transit: TLS everywhere - ALB terminates HTTPS to clients; DocumentDB connections enforce TLS; enable in-transit encryption for Redis (REDIS_SSL); all AWS SDK calls use HTTPS.
- At rest: enable AWS KMS encryption on DocumentDB, S3, ElastiCache, and Secrets Manager when you provision them.
- Decryption: the app uses no proprietary encryption. Managed services transparently decrypt for the container's IAM role; JWTs issued by the API are signed (not encrypted) with JWT_SECRET_KEY and verified per request.
- Credentials and rotation
Store all credentials in Secrets Manager / SSM and rotate on a regular cadence (align to your security policy, typically 90 days or less):
- JWT signing key / app secret (JWT_SECRET_KEY, SECRET_KEY) - rotating invalidates outstanding tokens; roll API and workers together.
- DocumentDB / Redis passwords - use managed rotation and redeploy so all services pick up the new value.
- Identity-provider client secret and OCR key - rotate before expiry; overlap old/new during cutover.
- AWS access: use IAM task roles / IRSA - avoid long-lived access keys.
Procedure: update the secret -> rolling redeploy of API + all workers -> confirm health -> revoke the old value.
- Backup, recovery and health
Backup / recovery (standard, non-proprietary stores):
- DocumentDB (system of record): enable automated snapshots + point-in-time recovery; restore to a new cluster and repoint the connection string. Required indexes are re-created automatically at startup.
- S3: enable versioning (and optional cross-Region replication for DR).
- Redis: treated as ephemeral; in-flight work is re-driven from DocumentDB state.
Because the container is stateless, DR is: restore DocumentDB + S3, redeploy the image, repoint environment variables.
Health monitoring:
- In the ECS/EKS console, confirm running count = desired count and tasks/pods are healthy (the container has a built-in health check and the ALB checks /health).
- Call GET /health for a fast liveness signal (it does not probe downstreams, so downstream slowness never falsely marks the API down).
- Enable CloudWatch metrics export (token usage, cost, latency, decisions, guardrails) and send container logs to CloudWatch Logs (logs are PHI-safe).
- Recommended alarms: unhealthy target hosts, service running-count below desired, DocumentDB/Redis CPU and connections, and Bedrock throttling/error rate.
- Service quotas, IAM and pricing
Service quotas - request increases proactively via Service Quotas:
- Amazon Bedrock per-model requests-per-minute and tokens-per-minute is the primary scaling constraint.
- Also monitor ECS/Fargate tasks, DocumentDB instances/connections, ElastiCache nodes, and OCR TPS.
IAM (least privilege, task role / IRSA): bedrock:InvokeModel* on the configured models; s3:GetObject/PutObject/ListBucket/DeleteObject on the configured bucket(s); secretsmanager:GetSecretValue / ssm:GetParameter* and kms:Decrypt on the relevant resources; CloudWatch PutMetricData / log permissions when enabled. The execution role additionally needs ECR pull and secret-read permissions.
Pricing - software is billed through AWS Marketplace at the listed rate. You separately pay standard AWS rates for the infrastructure you run it on: ECS/EKS compute, DocumentDB, ElastiCache, S3, Amazon Bedrock per-token inference (usually the largest variable cost), OCR, load balancer, Secrets Manager, and CloudWatch. Estimate with the AWS Pricing Calculator; Bedrock token spend and compute/DocumentDB sizing dominate total cost.
- Security and compliance (PHI)
This product processes Protected Health Information. To operate it in a HIPAA-eligible manner: execute an AWS Business Associate Addendum and use HIPAA-eligible services; enable KMS encryption at rest and TLS in transit; run all containers and data stores in private subnets exposing only the ALB; restrict the container security group so port 7000 is reachable only from the load balancer; keep the container non-root; scope IAM to least privilege; retain the DocumentDB audit trail per policy; and keep input/output guardrails and PII redaction enabled in production.
- Upgrades and release notes
Upgrades are a rolling image replacement with no data migration (indexes are ensured idempotently at startup):
- Review release notes for the target version and any new required environment variables.
- Snapshot DocumentDB and confirm S3 versioning before upgrading production.
- Pull the new image tag and roll out the API and all worker services to the same version.
- Verify health and run a smoke test. Roll back to the prior image tag if needed - rollback is immediate since no destructive migration runs.
Release notes (v1.0.0 - initial release): PA/UM API with OCR, Bedrock-powered extraction and guideline evaluation, patient summarization, inpatient/concurrent review, determination letters, analytics, and governance guardrails; hardened non-root container with PHI-safe temp handling and TLS-enforced database connectivity. Future releases are labeled Critical (security), Important, or Optional.
- Support
This product is provided by Penguin AI. For product support, questions, or issues, contact support@penguinai.co . AWS infrastructure issues are handled through AWS Support. A running instance serves its interactive API reference at /docs and /redoc.
Additional details
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Contact support@penguinai.co
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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