Three accelerators. Four practices.

What it does

Healthcare organizations generate extraordinary volumes of information every day. Most of it stays locked in unstructured clinical notes — physician documentation, patient summaries, outcome narratives. The problem is not a lack of data; it's a lack of accessible intelligence.

Clinical Healthcare Intelligence transforms that documentation into searchable, cited, governed insight inside Snowflake. Clinicians and researchers ask questions in plain language. The platform answers in seconds, without data ever leaving the secure environment.

• Cortex Search across millions of clinical notesSemantic indexing of unstructured documentation with medical context awareness.
• Cortex AISQL for in-database extractionSummarization, classification, and validation directly inside Snowflake — no data movement.
• Cortex Analyst for natural-language queriesPlain-English access to structured patient and outcomes data.
• Healthcare Marketplace Native AppDistributed via Snowflake Marketplace with role-based access, masking, and audit trails preserved.

Use cases: Oncology treatment discontinuation · Cardiology readmissions · Behavioral health side effects · Surgery post-op detection · ED frequent-utilizer analysis · Pharmacy off-label use · GLP-1 therapy switching · Clinical trial eligibility screening · Care transition gaps.

Built for Healthcare

▶ Live demo Watch Clinical Intelligence in action. 90 seconds · Cortex Agents querying real clinical data, inside Snowflake.

Read the full overview

What it does

Healthcare revenue leaks every day through manual, disconnected EDI processes and inconsistent payer data. Denials rise unnoticed, underpayments go unchallenged, and claims sit in reconciliation backlogs for weeks.

Avalanche EDI Intelligence transforms raw EDI files into clean, governed revenue insight inside Snowflake. Finance and revenue cycle teams ask payment, denial, and contract questions in natural language and get cited answers — without engineering bottlenecks.

• Denial root cause analysisIdentify recurring denial patterns by payer, facility, or claim type to reduce revenue loss.
• Underpayment detectionHighlight variances between expected and received payments. Recover missed reimbursements quickly.
• Contract performance trackingMonitor payer adherence to contracted rates and identify compliance issues before they escalate.
• Operational benchmarkingCompare financial performance across facilities and service lines to guide improvement efforts.

Typical impact: Claims reconciliation weeks → hours · 5-10 day A/R reduction · 15-30% fewer denials · 2-4% Net Collection Rate lift · $100K-$300K recovered monthly.

Built for Healthcare

What it does

Schema drift in upstream data sources disrupts ingestion workflows, causing delays, maintenance overhead, and data quality issues. Traditional ETL pipelines struggle to handle evolving schemas dynamically, often requiring manual intervention that slows analytics and increases operational inefficiencies.

SnowDrift leverages Snowflake's FLATTEN() and Stream functionality to create a dynamic JSON ingestion framework. A standardized staging table captures row-level data and metadata stored as JSON, ensuring schema integrity at the row level. An Append-Only Stream on the stage table preserves historical changes for better disaster recovery.

• Dynamic JSON schema managementAutomated schema drift handling using Snowflake-native FLATTEN() and Stream functionality.
• Append-only architectureHistorical retention without truncation. Better disaster recovery, full data integrity.
• Metadata-hash validationPer-row metadata integrity checks. Records routed to success, warning, or failure states automatically.
• Snowflake-native, no external toolsRuns entirely on Snowflake compute. No third-party orchestration required.

Typical impact: Eliminates manual ETL maintenance · Reduces ingestion failures · Improves disaster recovery posture · Scales without re-engineering.

What we design

We begin with the shape of the data that matters most to the business, not the shape of the warehouse. From there we work backward into warehouses, roles, zones, and consumption policies that keep cost predictable as volume grows.

Every platform we build is sized to the workload, isolated by environment, governed by RBAC from day one, and instrumented so finance, security, and engineering can each see what they need to see.

• Warehouse and role architectureRBAC, masking policies, zero-copy cloning, and environment strategy that supports both velocity and compliance.
• Medallion and semantic layerRaw, conformed, and serving zones with clear contracts at each step. dbt-powered, lineage-aware.
• Consumption and cost observabilityPer-team, per-model credit attribution. Alerts before surprise bills. Guardrails that don't block product velocity.
• Security and compliance postureHIPAA, SOC 2, and internal audit readiness built in, not bolted on. Access reviews that take minutes, not weeks.

What we ship

Most "data projects" fail the day the original builders rotate off. We design for the second engineer, not the first. That means contracts between upstream and downstream models, SLAs that are visible to stakeholders, and runbooks that assume on-call has never seen this pipeline before.

We work natively in dbt, Snowflake, and the orchestrator of your choice. We're opinionated about structure and neutral about tooling.

• Ingestion across 20+ source systemsSaaS APIs, legacy databases, EDI flows, file drops, and streaming sources. Incremental by default.
• dbt models with enforced contractsColumn-level tests, freshness checks, and schema versioning. Breaking changes fail loudly upstream, not silently downstream.
• Observability and SLAsPipeline health as a first-class product metric. Dashboards and alerts the business actually reads.
• On-call runbooks and handoffEvery pipeline ships with a one-page failure playbook. Your team can own it from day one.

What we build

AI in the enterprise fails for the same reasons data projects fail: the foundation isn't ready for it. We stand up AI systems on top of a governed Snowflake platform so you get enterprise-grade safety, audit, and cost attribution alongside the model output.

The pattern we favor: retrieval over structured and unstructured data, natural-language access via Cortex Analyst, and agent workflows that respect role-based access from the first query.

• Retrieval-augmented generationCortex Search indexes your unstructured content with semantic awareness of your domain vocabulary.
• Natural-language analyticsCortex Analyst lets stakeholders ask questions against structured data in plain language, with cited answers.
• Cortex Agents and conversational UXAgents that combine retrieval, structured queries, and domain logic into a single conversational surface.
• Governance, masking, and auditRole-based access, masking policies, and audit trails that make AI auditable for security and compliance.

What we measure

A good platform that nobody trusts is worse than no platform at all. We close that gap with KPI models that trace cleanly from source system to boardroom, self-serve tooling for non-technical teams, and measurement strategies that outlast re-orgs and tool changes.

Every engagement ends with a clear answer to one question: what changed, and how do we know.

• KPI models with lineageSource-to-boardroom traceability. Any metric can be traced back to the raw event that produced it.
• Self-serve analyticsTableau, Power BI, and Looker environments built for non-technical teams. Less waiting on the data team.
• Measurement strategyMetric definitions that survive the next re-org, the next tool migration, and the next leadership change.
• Executive reportingA single source of truth for the numbers leadership argues over. No more "whose dashboard is right."