Finance you can defend — built AI-native for multi-entity healthcare.

Why finance teams at multi-entity healthcare groups choose Odin.

Watch the 60-second demo

Drop a messy export, resolve the uncertain mappings, consolidate across entities, and click a number to trace it to its source. Illustrative data.

Odin for multi-entity healthcare — a 60-second walkthrough.

AI does the heavy lifting, but trust is built in: provenance on every number, confidence gates on every mapping, and a human in the loop before anything computes. What that looks like in practice:

1

AI-native architecture

Odin is built around the AI doing the work that breaks finance teams — reading a messy export, recognizing each entity, and mapping columns to the right financials. It is not a chat panel bolted onto a dashboard; the AI is the ingestion and analysis layer itself.

Today: Drop a CSV or XLSX from any system and watch the mapper resolve it on the live demo.

2

Built for healthcare finance

The data model speaks healthcare: payer mix, per-case economics, and the structures of ASCs, PCs, and specialty groups. It was built by a practicing healthcare CFO, not retrofitted from a generic SaaS template.

Today: The healthcare / ASC surface and specialty KPIs are live — built by a CFO at a New York practice.

3

Multi-entity by design

Most groups run a parent plus several PCs, LLCs, ASCs, or locations and consolidate by hand in spreadsheets. Odin treats multi-entity as the foundation: switch to any entity, or roll them up into one consolidated view.

Today: The entity switcher and cross-entity consolidation are live in the demo.

4

Provenance on every number

Every figure can be traced back to the exact source transactions behind it, grouped by entity. When the board asks where a number came from, the answer is a click, not a week.

Today: Click the consolidated net on the live trace demo and watch it break down entity by entity, reconciled to the penny.

5

Confidence-gated mapping, with human review

The AI scores its own column mappings. High-confidence columns map automatically; anything ambiguous stops and asks a person to confirm before a single number is computed. Human approval is the feature, not an afterthought.

Today: The cold-file confirm flow is live — uncertain columns stop for review before metrics compute.

6

From messy export to trusted number, fast

Connect or upload data, review what the AI is unsure about, consolidate across entities, and trace any result to its source. The path from a raw export to a defensible number is measured in hours, not a quarter-long implementation.

Today: No services contract or implementation team — bring a real file to a Trace Engagement and we will do it on your data.

Bring a number you need to defend.

Built by a practicing healthcare CFO. AI-native from the architecture, multi-entity by design, and traceable to the source. Start with a Trace Engagement on your own data — we ingest your real export and trace a number you care about back to where it came from, live.