Your data finally understood

One platform where teams and AI build a shared, trusted understanding of what enterprise data means — not just where it lives.

Faster onboarding
1
Source of truth
0
Tribal knowledge
The problem

Your data is everywhere.
Understanding is nowhere.

Teams look at the same data and interpret it differently. Definitions vary. Logic is buried in code. Nobody knows which numbers to trust.

🔍

Discovery is broken

People don't know where to start. Data lives in dozens of systems with no entry point.

🔀

Definitions drift

Same metric means different things. "Active user" has six definitions and none are written down.

🔒

Context is locked away

Business logic lives in SQL, notebooks, and someone's head. New hires take months.

Trust is invisible

No ownership, no status, no review history. Can't tell if validated yesterday or abandoned a year ago.

💬

Questions vanish

Critical conversations happen in Slack and die there. Same question re-asked every quarter.

🤖

AI can't help yet

LLMs lack enterprise context. Without a knowledge layer, AI answers are guesses dressed as facts.

How it works

Five steps to trusted data

From scattered definitions to a living, reviewed, AI-enhanced knowledge base.

01

Capture any data concept

Create knowledge units for metrics, terms, tables, APIs, policies. Business and technical explanations side by side.

02

Connect to source systems

Link to databases, dashboards, documents, APIs. Track provenance and dependencies.

03

Challenge and improve together

Raise structured issues. Discuss definitions. Propose edits. Everything tracked and assigned.

04

Review and certify

Move from draft → approved → certified. Everyone sees how much to trust each definition.

05

Let AI accelerate everything

AI drafts, suggests relationships, detects conflicts, answers questions grounded in reviewed knowledge.

Monthly Recurring Revenue
Metric · Finance
✓ Certified
Business explanation

The total predictable revenue generated from all active subscriptions in a given calendar month, excluding one-time fees, usage overages, and credits.

Technical logic
SUM(subscription.amount)
WHERE subscription.status = 'active'
AND subscription.type != 'one_time'
GROUP BY DATE_TRUNC('month', date)
4
Source links
3
Relationships
v7
Version
Owner: Sarah Chen, FinanceReviewed 3 days ago
Features

Everything you need to build
shared understanding

A purpose-built system for documenting, reviewing, and trusting enterprise data.

Search-first discovery

Find any data concept instantly. Keyword search, semantic search, AI-powered questions, and filters across every type of knowledge unit in your organization.

MetricTableTermAPIPolicy
How is churn calculated?
Customer Churn RateIn review
Revenue ChurnCertified
churn_events tableApproved
💬

Structured collaboration

Challenges, threads, evidence, and resolution tracking. Like code review, but for data definitions. Every question and answer is preserved.

Review and certification

Approval workflows with clear statuses. Everyone sees whether a definition is a rough draft or a certified source of truth.

🔗

Source system links

Connect knowledge units to databases, APIs, dashboards, and documents. Track provenance and keep references current.

🗺️

Relationship graph

Visualize dependencies, conflicts, upstream and downstream connections. Navigate your data landscape with full context.

Trust model

Trust is not assumed.
It's earned and visible.

Every knowledge unit carries a clear status so your team always knows how much to rely on each definition.

D
Draft
S
Suggested
R
In review
A
Approved
C
Certified
AI

AI that works with your knowledge,
not around it

Connect any data source, build knowledge around it, and chat with the actual data. The AI reads your schemas, your documents, and your team's reviewed definitions — then answers from all of it.

💡

Explain in plain language

Ask questions and get answers grounded in your company's own definitions, not generic web knowledge.

✍️

Draft descriptions

AI generates business and technical explanations from metadata and schemas. Humans review and refine.

🔗

Suggest relationships

Auto-detect connections between knowledge units. Surface duplicates, conflicts, and dependencies.

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Semantic search

Search by meaning, not just keywords. Find related concepts even when the terminology differs across teams.

📋

Summarize discussions

Condense challenge threads and review conversations into clear summaries with decisions highlighted.

🛡️

Built-in guardrails

Every AI answer includes citations, confidence scores, and requires human approval before becoming trusted knowledge.