What is Lang.ai?

Lang.ai is an advanced AI platform designed to integrate seamlessly with Snowflake data warehouses. It specializes in transforming complex, unstructured data from various sources like customer interactions, product usage, and sales figures into clear, actionable insights. By automating the often time-consuming process of data engineering, Lang.ai helps businesses understand the correlation between user needs, purchase behaviour, and key business metrics.

The platform utilizes AI agents tailored to specific business goals such as customer retention, conversion optimization, contact reason analysis, Net Promoter Score (NPS) improvement, and cross-selling opportunities. These agents analyze enterprise-scale data volumes within the security perimeter of Snowflake, contextualizing insights based on relevance and user feedback. Lang.ai aims to provide a complete picture of customer touchpoints, identify emerging trends, and deliver prioritized insights directly to teams, facilitating data-driven decision-making for growth and retention.

Features

Snowflake Integration: Syncs with Snowflake data warehouses quickly.
AI Agent Customization: Enables creation of tailored AI agents for specific business goals (e.g., Retention, Conversion).
Insight Generation: Transforms Snowflake data into prioritized, actionable insights.
Correlation Analysis: Connects unstructured data with revenue-driving business metrics.
Contextualization Engine: Accurately contextualizes insights using Snowflake data and relevance feedback.
Large Volume Processing: Handles enterprise-scale data within Snowflake's security perimeter without LLM context limits.
Holistic Customer View: Aggregates insights from all customer touchpoints.
Team Alignment: Delivers insights via Slack for real-time priority sharing.

Use Cases

Analyzing customer churn drivers for improved retention.
Understanding root causes behind customer support contacts.
Identifying friction points in conversion funnels.
Analyzing NPS feedback to pinpoint drivers of satisfaction and dissatisfaction.
Discovering cross-selling and up-selling opportunities based on user interactions.
Automating the process of deriving insights from large unstructured datasets in Snowflake.
Connecting qualitative customer feedback with quantitative business metrics.