Verified Knowledge

Allows workspace moderators (human-expert-in-the-loop) to verify AI-generated answers and their underlying metric entities, so they can be reused reliably across the data workspace.

What is it?

Connecty AI introduces an Expert-in-the-Loop Verification System that allows data workspace moderators to verify AI-generated responses and their underlying semantic logic. This includes not just SQL snippets, but also the full context: metric definitions, filters, dimensions, relationships, and more.

Every verification becomes part of a living semantic knowledge base - stored in the Metricverse - used to improve accuracy, consistency, and reasoning across future queries and sessions.

Unlike traditional tools that store static SQL, Connecty AI models and understands semantic dependencies between questions, metrics, and business logic - so verified knowledge is precise, reusable, and impact-aware.

Why verify?

Modern data teams struggle with metric inconsistencies, unclear logic, and dashboards that users don't trust. Different teams define the same KPI in conflicting ways, and even AI-generated answers can surface unapproved or outdated logic.

Connecty’s verification system solves this by letting you:

✅ Establish trust in AI-generated responses through human-approved logic 🔁 Ensure consistency across metrics, dashboards, and questions 📚 Build a curated library of verified metric definitions everyone can rely on ⚠️ Prevent conflicting logic from being reused or silently applied 🧠 Guide the AI to reuse only trusted, context-appropriate definitions

This system supports both question-level verification (e.g. “Is this answer correct?”) and component-level verification (e.g. “Is this metric definition correct?”).

How it works

1. Verifying a Chat Response

When a data workspace moderator is satisfied with a response to a user question in chat, they can choose to "Verify this response."

• Connecty will automatically extract all underlying metric entities used in the SQL response.

• A popup will show the list of dependencies: metrics, dimensions, filters, joins—any entity that contributed to the result.

• The moderator accepts and the response is marked as verified, and all associated metric entities are transitioned to a Verified state.

2. Managing Verifications in the Metricverse

The Metricverse tracks the verification status of every metric entity:

• Verified – Reviewed and approved by a moderator

• Unverified – Used in responses but not yet approved

• No Action – Not yet reviewed or surfaced in a response

Moderators can also directly verify or revoke the verification of any metric entity from the Metricverse interface—independent of chat responses.

What Can Be Verified?

You can verify and store the following types of information in Knowledge:

🔹 Metric Entities

Semantic components extracted from your questions or SQL, including:

• Metrics (e.g., Gross Margin, Net Revenue)

• Subjects (e.g., Orders, Customers)

• Measures (e.g., Total Spend)

• Relationships (e.g., Customers JOIN Orders)

• Dimensions (e.g., Time, Channel)

• Filters (e.g., status = active, date > last quarter)

🔹 Chat Answers

You can verify full answers to user questions, including:

• The natural language interpretation of the question

• The generated SQL

• The full set of semantic entities involved (metrics, dimensions, filters, joins)

Verifying an answer confirms that the AI correctly understood and resolved the question using trusted logic.

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How to Utilize Verified Knowledge

1. In Chat, while asking a new question, enable the Verified button to restrict the knowledge to those entities that have been marked verified by the Workspace moderator.

1. In Metricverse left side panel, easily filter by Verified or Unverified

3. In Context Engine, easily filter by Verified or Unverified

1. Verified questions (and their queries) are also stored in Context Engine and can be easily filtered out, with full access to its metadata including versions and runs history.

Which Workspace is the Verified Knowledge Applied

• Scope: Verification is applied only within the current data workspace. Other workspaces are isolated.

• Usage: Verified components are reused during:

  • Semantic parsing of future questions

  • SQL generation

  • Conflict resolution and clarification

• Impact: Downstream responses will prefer verified definitions over inferred logic when contexts match.

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AI Intelligence: How Connecty Applies Knowledge

Connecty AI’s autonomous semantic graph is a dynamic representation of your data, logic, and verified knowledge.

Key Behaviors:

• Semantic Graph Construction: On each question or query, Connecty builds a real-time graph of all involved entities, metrics, relationships, and filters.

• Dependency Resolution: When you verify a metric or question, Connecty tracks all related components. It understands that Net Revenue depends on Revenue, Refunds, and Taxes, and maps this dependency.

• Scoped Trust Propagation: Verified definitions are reused only when contextually compatible. For example, Customer Lifetime Value verified for DTC customers will not be reused for B2B metrics unless dependency conditions match.

• Version & Conflict Detection: If multiple definitions exist (e.g., for Active Users), Connecty detects the divergence and prompts for clarification before use.

• Auto-Adaptive Updates: If your schema changes (e.g., renaming plan_tier to tier_code), Connecty updates affected graph nodes and recalculates dependent logic accordingly.

This system enables safe reuse, conflict resolution, and precision reasoning at semantic scale.

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Things to Consider

• Auto-Verification Cascade: Verifying a question may select multiple dependent components. You'll be shown a warning and can deselect any items manually.

  “You are verifying the response generated for this question. Verifying this will also auto-verify all its underlying components that are selected below. Unselect any components you don’t want to include. The verification will be applied for the currently selected data workspace.”

• Conflict Alerts: If verification introduces a conflict with existing Knowledge, you’ll receive a versioning or compatibility warning.

• Manual Review: Periodically audit your workspace’s Knowledge base to remove outdated logic or revise evolving definitions.

• Partial Verification is Supported: You can verify only parts of a query—such as specific metrics or filters—without storing the entire question or SQL.

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Example Workflow

Question: “What is our gross margin by region for the last quarter?”

Connecty extracts the following:

• Metric: Gross Margin = Revenue - COGS

• Dimension: Region

• Filter: last quarter

Once verified:

• These definitions are stored with their dependency graph.

• If another user later asks “Compare gross margin across regions”, the system will automatically reuse the trusted metric logic—ensuring consistency and avoiding errors.