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Enterprise AI starts with data you can trust.

Solutions

How we govern your AI estate end to end

Three integrated layers — foundation, interface, engine. Each one shown the way we actually pitch it: the problem, what we do about it, and how you check that it worked.

Data Governance01 — Foundation

Every asset catalogued, every lineage drawn.

Data catalog, business glossary, stewardship workflows and automated lineage across 170+ connectors.

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Problem

Disconnected Data Ecosystem

Multiple systems operate without unified governance.

Assets with an ownerNone
Solution
OvalEdge

Unified Data Governance

Discover, catalog and govern enterprise metadata.

Connectors170+
Proof

Trusted Enterprise Data

Reliable data ready for analytics.

LineageFull
AI OrchestrationHAVAA02 — Interface

One agent, every channel, held to policy.

Omnichannel AI across Telegram, WhatsApp, web, mobile and email — five channels, three languages, one policy.

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Problem

Every Channel at Once

Messages arrive faster than a team can answer them.

Channels coveredNone
Solution
HAVAA

One Governed Agent

Drafts a reply, checks it against policy, escalates to a human.

Channels live5
Proof

Zero-Backlog Support Desk

Every reply logged with its policy and approver.

Languages3
Data Lakehouse03 — Engine

Answers from the lake, not from a copy.

Federated analytics on Trino and Apache Iceberg — querying 50+ sources where they already live.

Explore the data lakehouse
Problem

A Chain of Copies

Every question means another extract, load and rebuild.

Queries on live dataNone
Solution
Starburst

Query It Where It Lives

Federate every source with no copy and no pipeline.

Sources federated50+
Proof

Answered From the Lake

One query, live data, nothing copied.

Copies made0

AI governance framework

A layered system — each pillar builds on the one before it. Start with AI Inventory and build outward from there.

01

AI Inventory

Know every AI system running in your organization.

A live register of every model, agent and tool — with shadow-AI detection, risk scoring and a named owner for each system.
02

Data Lineage

Trace where data comes from and where it flows.

Source-to-dashboard tracing: origin, every transformation, the pipeline map, and impact analysis in both directions.
03

Data Quality

Ensure the data feeding your models can be trusted.

Validation rules, duplicate detection, freshness and schema checks that catch bad data before a model ever sees it.
04

Data Security

Protect data at rest, in transit, and in use.

Encryption, anonymization, threat detection and immutable secure storage across data at rest, in transit and in use.

How mature is your data estate?

Twelve minutes, scored live against the framework we are audited on. No sales call required to see your number.

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Certification

We are the first ISO/IEC 42001-certified AI governance company in the region.

The audited scope covers AI governance and the data platforms underneath it — the same framework the assessment above scores you against.

ISO/IEC 42001 certified AIMS — Yukon Labs
ISO/IEC 42001 — Certified AIMSRead the AI Policy

From the Journal

On AI governance, lakehouse architecture and what an audit actually asks for.

Company

Yukon Labs joins Microsoft for Startups

Yukon Labs has been accepted into Microsoft for Startups, unlocking $100,000 in Azure credits, Azure AI infrastructure and enterprise tooling to accelerate…

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Data governance

What Is a Data Catalog? Metadata, Lineage and Business Glossary…

A data catalog indexes what data exists, what it means, where it came from and who owns it. How metadata, column-level lineage…

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Data governance

OvalEdge vs Collibra vs Alation: An Implementer's Comparison

An honest comparison of three data catalogs from the team that deploys them: cost, on-premise viability, lineage quality, analyst experience and time…

Read the post
Partners

The platforms we deliver on

We build on a small number of platforms and know each one to the depth an audit requires. Hover a logo to see what it covers.

Contact

Talk to the team that builds it

Send a message and someone from engineering — not a call centre — will reply.