Building the Mind for Enterprise Data
"Every enterprise will have a data mind—SchemaMind"
See the 7 Min demo
Enterprises struggle with four critical breakdowns — each one breaks the path to reliable AI.
CRMs, data warehouses, spreadsheets, and legacy systems never talk to each other.
Same entities live across multiple sources with conflicting values — no single source of truth.
Missing values, duplicates, and outdated records make every analysis unreliable.
Missing column lineage, role‑back access control, and approval workflows.
From unstructured text to structured intelligence — a new era for data.
Understands the unstructured web .
Maturity: High
Understands databases, PDFs, Excel — with lineage, quality, and governance.
Maturity: Emerging • Complete
The evolution of data engineering — three eras, one leap.
1970s – 2000s
Batch-only, manual schemas, no unstructured data.
2000s – 2020s
Predefined business logic, no drift handling.
Now – Future
Self-healing, natural language, governance, DQI, 50+ connectors.
Powered by intelligent data movement, cleaning, and fusion — turning raw, siloed sources into queryable datasets with over 25 connectors.
Natural Language Query
Orchestrates movement, cleaning & fusion
25+ connectors unify silos
Queryable, trusted datasets
Indian Patent Application 2510001IN-CS
Intelligence Layer
Natural Language Querying
Query heterogeneous databases using plain English, no SQL required.
AI Query Disambiguation
LLM generates clarifying questions to resolve ambiguity before execution.
Semantic Column Matching
Aligns "client_id", "cust_id", & "customer_id" across disparate sources.
Automation Layer
Auto Source Discovery
Automatically discovers SQL, CSV, Excel, and PDF data sources via 25+ connectors.
Zero-Schema Extraction
Extracts schemas & types without user-provided definitions.
Schema Evolution Detection
Monitors and adapts automatically to live schema changes.
Reliability Layer
Confidence-Scored Plans
Generates execution plans with 0–10 reliability scoring.
Self-Healing Generation
Automatically detects, corrects, and re-generates incorrect plans.
Dependency Resolution
Uses temporary tables to resolve complex cross-source dependencies.
Universal Extraction
PDF/DOCX Table Extraction
Converts embedded document tables into structured datasets.
Domain Verb Mapping
Maps business verbs (e.g., "Churned") to data columns user-defined.
Column Lineage Tracking
Every unified column traces back to its original source field.
| Capabilities | Traditional ETL (e.g., Fivetran) |
Semantic Layer (e.g., dbt) |
Document AI (e.g., Azure) |
GDI SchemaMind |
|---|---|---|---|---|
Natural Language Querying | ||||
Auto Source Discovery | ||||
Cross-Source Fusion | Manual Mapping | Full Semantic Merge | ||
PDF / Doc Integration | Single Source Only | |||
Self-Healing Execution | ||||
Zero Setup (No Code) | Heavy Config | Heavy SQL |
Finance Team
Several Weeks manually typing data from PDF invoices into Excel.
30 seconds. SchemaMind extracts & standardizes automatically.
Sales Analytics
Waiting days for data engineers to build new SQL reports.
Ask "Show Q4 revenue" in English and get results instantly.
Healthcare
Disparate EHR systems, lab results, and PDF documents.
Unified patient view merging EHR data with PDF clinical notes.
SchemaMind Overview
The world's first Generative Data Intelligence platform — instantly fusing databases, spreadsheets, and PDFs using plain English.
Patent Application Filed
2510001IN-CS
Patent-pending AI reasoning engine that "thinks" across all data sources to generate perfect datasets automatically, replacing rigid ETL pipelines.
$3B+ Unlocking the dark data problem—tapping 80-90% of enterprise data trapped in PDFs, spreadsheets, and silos—for AI-ready intelligence with first-mover advantage.
GPT proved generative AI for text.
SchemaMind brings that same transformative power to enterprise structured data, solving the "last mile" of data unification.
Building the Mind for Enterprise Data
See the 7 Min demo
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