Revolutionizing Data Discovery, Lineage, and Governance through intelligent agent orchestration.
A Data Catalog System is a modern platform that gives organizations complete visibility into their interconnected data ecosystem. It enables businesses to integrate data from multiple sources (MySQL, MSSQL, Snowflake, Redshift) and provides a visual interface to explore, track, and ensure data accuracy through lineage, alerts, and notifications.
Global asset searching across multi-cloud environments.
Deep-hop dependency tracking and impact analysis.
Real-time data quality scores and freshness alerts.
Understanding the core structure of our data ecosystem.
The individual "objects" in your catalog.
Examples:
• Tables & Columns
• Databases
• Pipelines & Jobs
• Dashboards & Reports
The "Family Tree" of your data. It maps the flow from the Source to the Destination, showing exactly how datasets are connected and transformed.
The "Identity Card" for every asset. It stores who owns the data, its current quality score, business tags, and technical schema details.
A comprehensive toolkit for autonomous data intelligence.
Coordinated specialist network.
Deep intent & semantic parsing.
Resolving ambiguity via dialogue.
Autonomous ReAct Loop.
Multithreaded task fulfillment.
Long-term conversation history.
Deep metadata & schema insight.
Vector + Semantic + Structured.
Autonomous path selection.
Breaking down complex goals.
Transparent usage monitoring.
Direct metadata modifications.
Role-based governance.
Human-in-the-loop validation.
Why most companies get stuck with basic tools that don't work for real business.
Easy to build, but they often "make up" wrong answers because they don't really know your company's data.
Good for basic questions, but it fails when you ask something slightly complex or when your data changes.
Too Rigid: These tools break easily.
No Thinking: They can't fix their own mistakes or ask you for help.
The shift from "Static Retrieval" to "Dynamic Reasoning".
An AI that can "think" through a problem, decide which tools to use, and verify results.
1. Plan: Decompose Query
2. Act: Use Expert Tools
3. Refine: Self-Correct
Maintains memory throughout the loop to ensure no detail is missed during discovery.
Moving beyond traditional RAG limitations.
Decomposes "impossible" questions into logical steps.
Asks for missing details instead of hallucinating.
Agents cross-check results before returning to user.
A linear, one-shot retrieval flow.
Vector Search
Generic Filter
Direct Prompt
Static Answer
Vector search finds chunks in workspace JSON.
Cross-encoder filters for high similarity.
Hardcoded routing to Mongo or Python paths.
LLM summarizes the final context once.
If retrieval fails at step 1, the whole answer fails.
Cannot handle complex dependencies across sections.
Guesses intent rather than clarifying with the user.
Dynamic, stateful, and autonomous execution.
Brain of the System
Clarification
Query SME
Analytics SME
Lineage SME
Health SME
The routing brain.
The gap-filler.
The domain experts.
The active executor.
Enriches query with history and database metadata.
Classifies if the task is Read, Write, or Ambiguous.
Parallelizes work across multi-threading specialists.
Guaranteed 100% precision through dialogue.
If confidence < 0.5, it halts execution.
Asks for specific time-ranges, asset names, or columns.
Factual lookup & Mongo query generation.
Complex aggregations & success rate math.
Traverses upstream/downstream flows.
Calculates freshness & quality scores.
Updating descriptions and tags via AI.
Permission checks and Human-in-the-Loop confirms.
Who, What, When tracking for all write actions.
The agents' inner monologue.
Iterates up to 5 times to refine and verify facts.
| Feature | Old RAG | Agentic Assistant |
|---|---|---|
| Logic Pattern | Static / Linear | Dynamic / Graph-based |
| Multiple Tasks | Fail / Sequential | Parallel Specialist Dispatch |
| Gap Resolution | Hallucinates | Clarification Agent Enquiry |
| Consistency | Hit-or-Miss | High (via ReAct verification) |
Direct comparison across different query complexities.
| Query Type | Old RAG Approach | Agentic AI Approach |
|---|---|---|
| Basic Discovery "Find sales table" |
✘ Often Guesses Wrong | ✔ Precise (via Clarification) |
| Multi-step Logic "Find 'Users' in Prod vs Dev" |
✘ Context Overflow | ✔ Parallel SME Dispatch |
| Data Analytics "Success rate of Job X?" |
✘ Information Unavailable | ✔ Real-time Calculation |
| Impact Analysis "Downstream of Column Y" |
✘ 50% Failure Rate | ✔ Deep-hop Lineage Trace |
Why industrial data catalogs need "Agency," not just conversation.
A standard chatbot only predicts text. Our Agentic AI **decomposes complex goals** into logical steps to solve multi-hop data problems autonomously.
Instead of hallucinating facts, agents actively **execute specialized tools** (SQL, Python, Mongo) to fetch real-time, verified metadata.
A simple bot is a generalist. We use **SME Agents** (Lineage, Analytics, Health) that act as individual domain experts for 100% technical accuracy.
Chatbots hit a dead-end if a query is ambiguous. Our system **self-corrects and pivots** to find alternative discovery paths or asks the user for missing info.
The **ReAct Loop** forces the AI to check its own work. If the observation doesn't match the plan, the agent refuses to guess and restarts the logic trail.
Seamlessly integrates with **Workspace Metadata** and RBAC, ensuring that AI responses are contextually grounded in your specific industrial environment.