What exactly is Ellie?

A collaborative, AI-powered data modeling platform. Business experts and data architects share one canvas to define conceptual, logical, and physical models, instead of a diagram in one tool and a glossary in another.

Is this just another data catalog or governance tool?

No. A catalog tells you what data you already have. Ellie helps you design the model your data should follow, including the one your catalog or governance platform (Collibra, Purview) then enforces.

Why do we need this if we already have Snowflake, dbt, or a modern stack?

A modern stack can't fix inconsistent logic. Without a shared model, different teams keep defining 'customer' or 'exposure' differently. Ellie keeps that definition consistent from the conceptual layer through to production SQL.

Does Ellie support insurance-specific frameworks like Solvency II or IFRS 17?

Yes. Ellie helps codify risk taxonomies and exposure definitions and keeps them consistent across underwriting, claims, actuarial, and finance, the way Solvency II and IFRS 17 reporting require.

Do you work with healthcare or other regulated sectors?

Ellie's approach to consistent definitions applies anywhere regulation depends on shared meaning, including HIPAA-governed healthcare data. Our proven regulated-industry deployments today are in financial services and insurance; other regulated sectors are next.

How does this integrate with our governance and access tools?

Ellie connects to Collibra and Microsoft Purview for governance, and Okta or Azure Active Directory for access control, alongside 170+ source systems and an open API.

Ellie is ISO 27001:2022 certified. AI features are opt-in, and Ellie never trains on or retains your customer data.

For Financial Services & Insurance

Compliance-Ready by Design

Ellie is the backbone for your universal semantic layer, AI-native, not AI-tacked-on. Give every regulatory definition one source of truth, from GDPR and BCBS239 to Solvency II.

Book a Demo

A 45-minute walkthrough of how Ellie fits your compliance and reporting stack.

Data model diagram showing relationships between risk management entities such as Risk, Control, BusinessUnit, and DataOwner

Used by Regulated Financial Institutions

Compliance Is a Consistency Problem

Audits Don't Fail Because of Missing Data

One term, four definitions

Underwriting, claims, finance, and risk each define 'active policy' or 'customer exposure' a little differently. That's fine, until two reports need to reconcile and don't.

Documentation that can't keep up with regulation

Static spreadsheets and glossaries drift the moment GDPR, AML thresholds, or a risk calculation changes, and someone ends up manually chasing why two numbers don't match.

AI inherits the ambiguity, not the fix

An AI system trained on inconsistent definitions doesn't resolve the inconsistency. It accelerates it, and now an unexplainable answer carries regulatory risk too.

One Model, Every Team Agrees On

Built for Teams That Get Audited

One Shared Business Model

Model shared concepts, customer, policy, exposure, claim, once, in a canvas risk, compliance, and data teams all work in together, so numbers stay consistent across regulatory, risk, and management reporting.

Traceability You Can Prove

Trace every reported figure back to the definition and source system that produced it, so a regulatory change or audit request never triggers a scramble, and change-impact analysis moves fast.

AI That Reads Your Model

Every model carries AI-specific metadata your agents can query via API, MCP, or YAML: the same governed definitions, not a fresh guess, for explainable AI on a trusted data foundation.

From Requirement to Governed Model

Three Steps to an Audit-Ready Model

Model the domain that drives compliance

Start with one high-impact area, AML, GDPR consent, Solvency II reporting, to show value fast instead of modeling the whole enterprise at once.

Standardize definitions with the people who own them

Bring risk, compliance, and data teams into the same canvas to agree on what 'customer,' 'exposure,' or 'coverage' means, once, not once per department.

Prove traceability, and keep proving it

Trace every reported number back to the definition and source system that produced it, so the next regulatory change is a model update, not a fire drill.

Built Around Your Regulations

Which Frameworks Does This Cover?

Full-stack data modeling is a regulatory capability, not just an architecture exercise. Here's what it looks like industry by industry.

Key Regulatory Frameworks	What Ellie Gives You
Financial Services: GDPR, AML/KYC, BCBS 239	Shared customer and exposure definitions, traced from source system to regulatory report.
Insurance: Solvency II, IFRS 17, NAIC model laws	Consistent risk, policy, and claim taxonomies across underwriting, claims, actuarial, and finance.
Healthcare & other regulated sectors: HIPAA and similar patient-data regulations	The same consistency approach, extended to patient, treatment, and billing data. Financial services and insurance are Ellie's proven regulated-industry deployments today; healthcare and other regulated sectors are next.