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About TextQL
TextQL serves as a personal, virtual data analyst designed for enterprises. This AI-driven platform allows users to seek business insights through natural English queries.
TextQL's technology, embodied by the AI named Ana, constructs comprehensive analyses, creates visual representations of data, and generates robust models.
The unique feature of TextQL is its integration within a team's preexisting data platforms, which enables Ana to function where the team is already active.
This includes collaborating through platforms like Slack and Teams.TextQL finds its usability across business intelligence systems, serving as a primary point of contact to locate any metric or dashboard.
Moreover, it prevents redundancy in dashboard creation by retrieving any existing dashboard. Ana can also manage an enterprise's entire data catalog. It can index various locations where messy metadata might be stored, surface definitions from any stored location with verified links, and recognize different definitional uses across teams.TextQL employs a language learning model fluent in SQL and Python and can be configured to adhere to any compliance standard.
This allows for secure and compliant deployments. Workflows are designed to suit an organization's needs, and industry-leading guardrails enable data anonymization, ensuring privacy.
This makes TextQL a powerful tool with expansive data integration, analysis, and management capabilities that cater to various industries.
TextQL key features
- **Unified Platform**: TextQL combines multiple tools into a single system, eliminating the need for separate integrations.
- **Automated Workflows**: The application automates processes from data intake to approval, enhancing efficiency in critical domains like healthcare and finance.
- **Conversational Analytics**: Users can ask questions in natural language, receiving answers along with the underlying queries for transparency.
- **Self-Improving Ontology**: The ontology learns from each task, improving accuracy and reducing costs over time by inheriting knowledge from previous queries.
- **Data Apps**: TextQL allows users to create shareable and embeddable applications from their governed ontology, facilitating deployment across various platforms.
TextQL use cases
- Automates data processing from intake to approval in healthcare, ensuring timely patient treatment.
- Handles complex data workloads across various systems like SAP, Oracle, and Teradata, providing answers to challenging queries.
- Enables the creation of data apps and dashboards tailored to specific questions, improving decision-making accuracy.
- Utilizes agents that autonomously manage tasks, reducing the need for additional headcount and enhancing efficiency.
- Offers conversational analytics, allowing users to easily query data while providing transparency into the underlying queries.
Useful for
- Automates data processing from intake to approval, enhancing efficiency in critical domains like healthcare and financial services.
- Provides a unified platform that integrates various data sources, eliminating the need for multiple tools.
- Enables conversational analytics, allowing users to ask questions and receive answers with the underlying queries included.
- Utilizes an evolving ontology that improves accuracy and reduces costs over time by learning from previous tasks.
- Offers customizable data apps that transform governed ontologies into shareable applications for broader deployment.
Price
- One-time purchase for the entire TextQL platform, eliminating the need for multiple integrated tools.
- Offers a semantic layer and agents that automate data handling and decision-making processes.
- Designed to handle complex data workloads from various systems like SAP, Oracle, and Teradata.
- Provides conversational analytics, allowing users to ask questions and receive answers with underlying queries.
- Features an ontology that improves over time, enhancing accuracy and reducing costs per task.
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