Data engineering hours
Spent maintaining egress pipelines instead of advancing your data strategy.
Snowflake Native Apps. Healthcare Ai Workers, where your data already lives.Where your data already lives.
Each app installs from the Snowflake Marketplace, runs inside your account, and produces recommendations your clinical staff can verify, stand behind, and present to a regulator. Nothing leaves.
The problem
The playbook you've been handed.
Healthcare organizations spent the last decade consolidating clinical, claims, and member data into Snowflake. One account. One access model. One audit trail. Then the AI vendors arrived and asked you to copy it out.
By the time you have stood up the egress pipelines, the duplicate copies, and the new BAAs, you have a second data warehouse you did not budget for and a compliance posture harder to defend than the one you started with.
Here is where it breaks: the model assumes the AI has to run somewhere else. It does not. Every chart you move out of Snowflake to score is you paying for the vendor's architecture with your governance.
01
Consolidate the data.
Years of work to land clinical, claims, and member data in one governed environment.
02
Buy an AI tool.
Vendor needs the data outside Snowflake to run.
03
Build egress pipelines.
New copies, new keys, new BAAs, new audit logs to reconcile.
04
Repeat per vendor.
Every use case adds another pipeline.
It is not the integration spend. It is the governance erosion.
When clinical data has to leave Snowflake to be useful, you lose the thing you built Snowflake for. One copy becomes three. One audit trail becomes four. One access model becomes a reconciliation problem your CISO and CDO own jointly.
The lightbulb
The AI should run where the data lives. Not the other way around.
The answer is that healthcare AI vendors built their stacks before Snowflake became the standard. They are catching up. Penguin Ai's solutions are purpose-built as Snowflake Native Apps. They install inside your account. They query your data directly. Nothing leaves.
The closed loop
- Read from Snowflake, reason over Snowflake, write back to Snowflake.
- Every recommendation carries data lineage your team can trace to the row.
- Your governance stays intact. Your audit trail stays unified. Your data never moves.
Three Snowflake Native Apps. One installation pattern.
Each app installs from the Snowflake Marketplace, runs inside your account, and produces recommendations your clinical staff can verify, stand behind, and present to a regulator.
App 01
Snowflake Medical Coding.
For provider organizations
CPT, ICD-10, and HCPCS codes surfaced from clinical documentation with supporting evidence attached. NCCI edits, payer rules, and medical necessity criteria applied before a coder touches the case. Documentation gaps flagged with draft queries ready to route.
01
Documentation queried.
Notes, operative reports, and labs read directly from Snowflake.
02
Codes surfaced.
Recommendations generated with chart evidence and rationale attached.
03
Validation applied.
NCCI edits, payer rules, and modifier requirements checked automatically.
04
Coder confirms.
Reviews the recommendation and validation in one view. Confirms. Submits.
App 02
Snowflake HCC Coding.
For health plans and risk-bearing provider organizations
Encounters scanned for supportable HCCs. Diagnoses extracted, mapped to CMS-HCC or HHS-HCC, and surfaced with MEAT evidence in context. Coder confirms every code. Audit-ready provenance attached by default.
01
Encounters scanned.
Historical and current encounter data read directly from Snowflake.
02
HCCs surfaced.
Supportable conditions identified with chart evidence and ICD-10 codes.
03
Gap detection.
Conditions supported but not coded surfaced. Conditions coded but not documented flagged for CDI.
04
Coder confirms.
Every code reviewed with evidence in context. RAF impact calculated.
App 03
Snowflake Prior Authorization.
For health plans · Clinical evidence review
Clinical documentation pulled from Snowflake. Your plan-specific criteria applied at intake across every line of business. Standard cases reach the reviewer's queue with recommendation, rationale, and evidence already organized.
01
Documentation pulled.
Clinical evidence queried the moment a PA request lands.
02
Criteria matched.
Plan-specific criteria applied across every code and condition.
03
Evidence mapped.
Documentation organized against criteria with gaps visible.
04
Reviewer decides.
Medical director receives a decision-ready brief. The art of medicine stays with the clinician.
One installation pattern. Your data never moves.
Native execution, not integration.
Installed from the Snowflake Marketplace. Runs inside your account. No egress pipelines, no duplicate copies.
Your governance stays your governance.
Snowflake roles, RBAC, masking policies, and audit logs apply unchanged. Penguin Ai inherits your security posture.
Your clinical staff makes the call.
Ai Workers handle extraction, criteria application, and evidence assembly. Your coders and medical directors own every recommendation.
The closed loop is in your account.
Every recommendation, override, and audit outcome stays in Snowflake. The Ai Workers learn from your data without that data ever leaving.
The Glassbox. Inside your account.
The Glassbox
Every recommendation arrives with the clinical evidence, criteria applied, rule path, and confidence scoring visible. No black box.
Full audit trail
Every code, recommendation, and reviewer action documented inside Snowflake with clinical reasoning, data lineage, and evidence references attached.
Data never leaves Snowflake
Clinical, claims, and member data stays in your governed account.
Configurable rules and criteria
NCCI edits, payer policies, plan-specific criteria, and proprietary guidelines applied consistently with no workarounds.
Human in the loop
Ai Workers prepare the work. Your coders, reviewers, and medical directors apply their expertise and make the call.
Continuous learning, inside your account
Every confirmation, override, and audit outcome strengthens future recommendations. The learning stays in your environment.
The next time a vendor tells you their AI needs your data, ask why their architecture is your problem.
Healthcare AI should run where your data already lives. It should inherit the governance you already enforce. It should give your clinical staff their time back without asking your data team to rebuild a thing.