Transform static databases into interactive dialogue partners through neural SQL orchestration.
DataConversation is a state-of-the-art conversational BI platform designed for the 2026 data ecosystem, where static dashboards are being replaced by autonomous analytical agents. The platform's core architecture utilizes a proprietary 'Schema-Aware Transformer' (SAT) which maps complex relational databases, NoSQL clusters, and unstructured data lakes into a unified semantic layer. Unlike traditional BI tools that require manual query building, DataConversation allows users to perform cross-source joins and longitudinal analysis using natural language. For 2026, the tool has integrated 'Hypothetical Scenario Modeling' (HSM), allowing executives to ask 'What if' questions that simulate market volatility against internal supply chain data. The platform emphasizes enterprise-grade security with a zero-trust execution environment where raw data never leaves the client's VPC; only the metadata and the natural language results are processed by the LLM orchestration layer. Positioned as a direct competitor to ThoughtSpot and Power BI's Copilot, DataConversation differentiates itself through its 'Explainable Logic' module, which provides a step-by-step mathematical breakdown of how every result was calculated, ensuring auditability in regulated industries like finance and healthcare.
Uses vector embeddings to identify related entities across disparate databases (e.g., Salesforce vs. Snowflake) without pre-defined foreign keys.
The open-source BI platform that turns your dbt project into a governed, version-controlled analytics engine.
Transform raw data into real-time metrics with a powerful semantic layer and automated BI dashboards.
The AI-powered data scientist that automates complex analysis, visualization, and predictive modeling through sandboxed code execution.
The world's most adaptable EPM platform for autonomous financial and operational planning.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A Monte Carlo simulation layer built into the dialogue interface to forecast outcomes based on variable shifts.
LLM-driven identification and correction of outliers, null values, and formatting inconsistencies during the query process.
Real-time regex and NLP-based masking of personally identifiable information before it hits the LLM context.
Dialogue-based chart editing (e.g., 'Change this to a stacked bar chart and color-code by region').
Decompiles LLM-generated SQL into a step-by-step logical sequence for technical verification.
Allows companies to upload internal documentation to refine the AI's understanding of proprietary metrics.
Marketing and Sales data live in different silos, making it impossible to see which ad spend led to specific closed-won deals.
Registry Updated:2/7/2026
Refine with: 'Exclude any campaigns with a CAC higher than $50.'
Export the resulting visual directly into a slide deck.
Inventory managers struggling to predict stockouts during seasonal fluctuations.
Traditional rule-based systems missing complex, evolving fraud patterns.