Penguin Ai Raises $29.7M To Cut Administrative Waste In Healthcare
Penguin Ai secured $29.7 million to reduce administrative overhead across payers and providers. The Series A was led by Greycroft with $25 million, joined by UPMC Enterprises, SemperVirens, Snowflake Ventures, Watershed Ventures, and Horizon Mutual Holdings. Early backers ManchesterStory and Overwater Ventures participated, along with the California Health Care Foundation and industry veterans.
The focus is clear: automate prior authorization, claims processing, coding, and appeals at scale. The goal is fewer manual handoffs, faster cycle times, and measurable savings on back-office operations.
Why This Matters For Healthcare Operators
Hospitals, health plans, and clinics spend hundreds of billions each year on administrative tasks. Independent analyses such as the CAQH Index highlight the cost and time tied up in manual workflows and partial automation.
- Prior auth delays care and ties up staff time.
- Claims adjudication errors drive rework and denials.
- Coding variability creates audit risk and revenue leakage.
See the CAQH Index for benchmarks on administrative spend and electronic adoption.
What Penguin Ai Is Building
Penguin Ai uses generative AI to automate routine, high-volume work through digital workers and purpose-built language models. Customers can use pre-built models for tasks like medical coding and claims adjudication or configure custom agents for their own workflows.
The platform emphasizes governance, auditability, and bias correction aligned with healthcare standards. Security and compliance are positioned as foundational, not add-ons.
Early Traction And Integrations
Penguin Ai is piloting with leading health plans and provider groups to validate speed, accuracy, and cost outcomes. The company is also integrating with broader data ecosystems so teams can deploy workflows without stitching together multiple vendors.
One example brings Penguin Ai's applications into the Snowflake AI Data Cloud, helping healthcare organizations work within existing data operations. Learn more about the Snowflake AI Data Cloud.
Leadership With Deep Payer-Provider Experience
The company is led by Fawad Butt, former chief data officer at UnitedHealthcare, Kaiser Permanente, and Optum. The leadership team brings a century of combined experience across payer, provider, and high-tech roles, which informs product choices grounded in real operational constraints.
Who's Backing The Company
Investors include Greycroft (lead), UPMC Enterprises, SemperVirens, Snowflake Ventures, Watershed Ventures, Horizon Mutual Holdings, ManchesterStory, Overwater Ventures, the California Health Care Foundation, and several industry veterans.
What Comes Next
Funding will expand product development, accelerate the roadmap, and scale deployments across payer and provider networks. As new models roll out and agents mature, Penguin Ai expects shorter claims cycles, fewer prior auth errors, and material reductions in administrative budgets.
Key Perspectives From Backers
- CEO Fawad Butt emphasized the chance to lower costs at scale, improve accuracy, and return staff time to patient care.
- Greycroft's Mark Terbeek pointed to strong platform vision and early customer traction.
- UPMC Enterprises' Nicholas Shapiro highlighted the importance of security, fairness, governance, and bias correction in healthcare AI.
- SemperVirens' Robby Peters noted a full-service platform built by former payer and provider executives, reducing the need for multiple point tools.
- Snowflake Ventures' Harsha Kapre underscored bringing specialized applications into the Snowflake AI Data Cloud so organizations can adopt AI with confidence.
Action Steps For Healthcare Leaders
- Prioritize 2-3 workflows with high volume and clear rules (e.g., prior auth categories, recurring denial codes, specific coding tasks).
- Define success metrics: turnaround time, first-pass yield, denial rate, and staff hours saved per case.
- Assess data readiness and integration points (EHR, claims platforms, document repositories, data cloud).
- Run a 60-90 day pilot with human-in-the-loop review, compliance checkpoints, and audit logs.
- Plan workforce impact early: role redesign, QA protocols, and training for exception handling.
- Build a governance framework covering model updates, drift monitoring, bias mitigation, and PHI safeguards.
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