Kyligence
Augmented OLAP and Unified Metric Stores for High-Performance Cloud Analytics.
The semantic bridge between natural language and complex data ecosystems.
DataLingual represents the 2026 standard in semantic data mediation, leveraging advanced Large Language Models (LLMs) to bridge the gap between non-technical stakeholders and structured data environments. Its architecture is built upon a proprietary semantic mapping layer that translates high-level business inquiries into optimized SQL or NoSQL queries across heterogeneous data sources including Snowflake, BigQuery, and PostgreSQL. By 2026, DataLingual has positioned itself as a critical infrastructure component for enterprises looking to eliminate the bottleneck of manual report generation. It doesn't just translate text to code; it understands the underlying business logic and schema relationships, providing contextual visualizations and predictive insights. The platform's 'Autonomous Analyst' mode allows for recurring data monitoring and anomaly detection, pushing alerts to stakeholders in natural language. With robust security protocols and SOC2 compliance, it addresses the data privacy concerns inherent in AI-driven analytics by utilizing localized vector embeddings for schema mapping without compromising the underlying raw data.
A proprietary NLP layer that maps ambiguous business terms (e.g., 'Gross Churn') to specific mathematical definitions and database columns.
Augmented OLAP and Unified Metric Stores for High-Performance Cloud Analytics.
AI-powered adaptive math learning that identifies and bridges learning gaps through granular skill modeling.
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.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Capable of joining data across disparate platforms like Salesforce and Snowflake in a single natural language request.
Background agents monitor data streams for statistical anomalies and generate summarized explanations.
DataLingual processes metadata and executes queries on the source database without storing the raw data.
Visualizations automatically change type (e.g., bar to line) based on the statistical properties of the result set.
Optimizes existing slow-running SQL queries using AI to suggest better indexing and join strategies.
Every AI-generated query shows the logical step-by-step reasoning and the specific SQL generated.
CFOs need instant answers on burn rates without waiting for the data team.
Registry Updated:2/7/2026
Difficulty in merging Facebook Ads data with Shopify sales data.
Identifying at-risk customers before they cancel.