Conversational Business Intelligence: Turning complex data queries into simple chat conversations.
Looqbox is a next-generation conversational Business Intelligence platform designed to bridge the gap between complex data architectures and non-technical decision-makers. By leveraging advanced Natural Language Processing (NLP) and Large Language Model (LLM) orchestration, Looqbox allows users to query structured databases using plain language via Slack, Microsoft Teams, WhatsApp, or a dedicated web interface. Unlike traditional BI tools that require manual dashboard construction, Looqbox utilizes a semantic metadata layer to interpret user intent, translate it into optimized SQL or NoSQL queries, and return real-time visualizations or tabular data. For 2026, its technical architecture has evolved to include predictive 'what-if' modeling and autonomous anomaly detection, making it an essential layer for enterprises seeking to decentralize data access without compromising security or governance. The platform excels in environments with fragmented data sources, offering a unified interface for ERPs, CRMs, and custom data warehouses, effectively reducing the 'time-to-insight' from hours of manual analyst work to seconds of automated processing.
A proprietary abstraction layer that translates business semantics into multi-join SQL queries without requiring manual SQL coding.
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.
Synchronous query processing across Slack, WhatsApp, and Microsoft Teams using a single logic core.
Uses machine learning to detect statistical outliers in data streams and pushes alerts automatically.
Automatically selects the most appropriate visualization type (Bar, Line, Sankey, etc.) based on the data result set structure.
Acts as a middleman between the user and the DB, enforcing IAM policies and data masking in real-time.
Allows users to ask follow-up questions (e.g., 'Why?') which the AI interprets within the context of the previous result.
AI-driven identification of business terms and their technical counterparts by analyzing database metadata and user query history.
Sales managers needing instant access to regional performance without waiting for weekly reports.
Registry Updated:2/7/2026
Preventing stockouts in retail environments.
C-suite executives needing consolidated P&L figures on the go.