TextQL is an AI-powered enterprise data analysis platform that translates natural language queries into optimized SQL across all your databases, enabling business users to access insights without SQL skills.

How does TextQL AI work?

TextQL uses intelligent AI agents (named Ana) that understand complex data relationships, map semantic connections across tables, and generate efficient queries to surface actionable business insights continuously.

How do I interact with TextQL?

Users can ask questions in plain English through interfaces integrated with Slack, Microsoft Teams, or a web app. In Slack, you open support tickets or interact with bots such as Pylon to get assistance or submit queries.

What data sources does TextQL support?

It connects to on-premises and cloud databases, modern data stacks including Snowflake, Salesforce, BigQuery, and can integrate with over 100x more data sources than many competitors, bridging complex data environments seamlessly.

How does TextQL handle data security?

TextQL offers enterprise-grade security including SOC 2 Type II certification, encrypted data connections, user authentication, and options to deploy in private clouds for complete data sovereignty and compliance.

What support options are available?

TextQL provides customer support via Slack with a bot-guided ticket system, email support, official documentation, community forums, and priority enterprise support during business hours (Mon-Fri, 10 AM to 5 PM ET).

What are best practices for effective support?

Clear issue descriptions, providing screenshots or error logs, timely responses to support team queries, and indicating urgency for after-hours tickets help ensure efficient issue resolution.

TextQL is designed for both technical data professionals and non-technical users to facilitate self-service data exploration and accelerate decision-making without needing SQL expertise.

How does TextQL improve data workflows?

It automates SQL/Python code generation, data visualization, and creates digital twins of businesses to streamline and enhance data workflows, including metric diagnosis, anomaly detection, and automated business insight generation.

TextQL

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TextQL is an AI tool that lets anyone ask questions about their business data using simple English. It automatically finds answers and insights from many different data sources, helping people make smarter decisions without needing to know complex coding or database languages.

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Overview Features Use Cases FAQ Integrations Pricing

About TextQL

Who It's For

This tool is for everyone in a business, whether you are technical or not. It helps leaders, managers, and teams get quick answers from their company's data. You don't need to be a data expert or know complex coding like SQL to use it.

What You Get

You get an easy way to understand your data. TextQL provides smart AI agents that dig into your information to find important insights. It can connect to all your databases, no matter where they are, giving you a full picture of your business. This helps you make better and faster decisions.

How It Works

You simply ask your data questions in plain English, just like you would talk to a person. The AI, called Ana, understands your question and automatically looks through your company's many data sources. It then quickly gives you the insights and answers you need, without you having to write any technical code.

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Features & Capabilities

🤖 Core AI Agents

Integration Agent

Builds custom data pipelines on-the-fly for complex source systems and mappings.

Analytics Agent

Uncovers actionable insights from complex datasets with agent-native pattern recognition.

Reconciliation Agent

Harmonizes fragmented records and continuously maintains accuracy through system changes.

🧠 Semantic Data Layer

Comprehensive Data Ontology

A sophisticated semantic layer that maps relationships between tables for deterministic joins at scale.

Semantic Authoring

Allows users to build and refine custom ontologies specific to their organization that agents understand instantly.

Intelligent Agent Architecture

Enables agents to understand complex data relationships and automatically determine optimal join paths across hundreds of tables.

🛠️ Developer & Ops Tools

SQL Editor

Provides direct and optimized access to enterprise data systems like Epic, Cerner, and claims systems.

Polyglot Notebooks

Offers pre-configured Jupyter environments with vetted libraries built from real workflows.

Agent Scheduling

Enables flexible triggers for automated workflows, allowing agents to monitor conditions and execute tasks.

🔗 Enterprise Data Connectivity

Native Databricks Integration

Provides a native and seamless connection to Databricks environments.

Native Snowflake Integration

Connects directly to Snowflake data warehouses for robust data access.

Enhanced Tableau Visualization

Supports enhanced data visualization and analytics capabilities when integrated with Tableau.

🛡️ Enterprise Security & Deployment

Enterprise Resilience

Ensures resilience, scalability, and security tailored for enterprise-grade operations.

Data Sovereignty

Allows deployment in your own cloud infrastructure with complete control over your data.

Cloud Agnostic Deployment

Offers flexible deployment options across major cloud providers like AWS, Azure, or GCP.

SOC 2 Type II Certified

Guarantees enterprise-grade security and end-to-end encryption with SOC 2 Type II certification.

Use Cases

Empowering Non-Technical Users with Self-Service Data Insights

Business users often face bottlenecks getting data insights due to reliance on data teams and the need for SQL expertise. TextQL's AI agents allow users to ask complex data questions in plain English across all integrated data sources, instantly receiving actionable insights without needing technical skills.

EnterpriseFor: Business Analysts, Marketing Managers, Sales Leaders

Automating Complex Data Integration and Reconciliation

Large enterprises struggle with fragmented data spread across numerous disparate systems, leading to inconsistent reporting and integration challenges. TextQL's Integration and Reconciliation agents automatically build custom data pipelines and harmonize records across 100x more data sources, ensuring continuous accuracy and a unified view for critical decision-making.

Enterprise, Financial Services, HealthcareFor: Data Engineers, IT Directors, Data Architects

Accelerating Industry-Specific Operational Intelligence

Industries like banking, manufacturing, and insurance face specific challenges such as fraud detection, predictive maintenance, or claims processing, which require deep, real-time data analysis. TextQL's Analytics agent and specialized solutions provide AI-powered pattern recognition and insights to optimize these critical operational workflows, leading to improved performance and cost savings.

Banking & Finance, Manufacturing, InsuranceFor: Operations Leaders, Business Unit Heads, Risk Management Teams

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TextQL