Qluent is a digital platform that utilizes natural language processing to enable users to query their business data without technical expertise, facilitating data-driven decision-making. By integrating with tools like Slack and Teams, it allows for real-time insights and visualizations while maintaining data privacy by only requiring access to database structures.
Funding
$125K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many business users lack the technical skills to directly query databases, creating a bottleneck for data-driven decision-making and requiring reliance on data analysts or IT departments. Extracting insights from business data often involves complex SQL queries and data manipulation, which can be time-consuming and prone to errors for non-technical users.
Solution
Qluent is a natural language interface that allows users to ask questions about their business data in plain language, without needing to write SQL or understand database schemas. By integrating with platforms like Slack and Teams, Qluent provides real-time access to data insights and visualizations within existing workflows. The platform translates natural language queries into structured queries, retrieves the relevant data, and presents it in an easily understandable format. Qluent focuses on providing users with the resources to make data-driven decisions, rather than replacing their work.
Target Audience
Qluent is designed for business users, analysts, and decision-makers who need quick and easy access to data insights without requiring technical expertise in SQL or database management.
Features
- Natural language processing (NLP) engine that understands and interprets user queries in plain language
- Integration with Slack and Teams for seamless access to data insights within existing communication channels
- Automatic generation of visualizations to help users identify patterns and anomalies in their data
- Suggestion of follow-up questions to facilitate deeper investigation and discovery
- Thread-based organization for easy sharing of insights and collaboration with team members
- Access to underlying code and detailed explanations for technical users to verify internal processes
- Privacy-focused design that only requires access to database structure, not sensitive data