Context provides an AI workspace designed to automate complex tasks across various business functions like consulting, law, and biotech. This platform natively connects internal and external systems, allowing users to deploy preferred AI models for data analysis, workflow automation, and document generation. The service aims to significantly increase task completion speed and free up employee time by handling mission-critical workloads securely within an enterprise environment.
Funding
$3.5M 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.
TTFounders
Product
Problem
Developers of applications powered by large language models (LLMs) lack comprehensive tools to understand how users interact with their models and to measure model performance effectively. This makes it difficult to identify areas for improvement and ensure user satisfaction.
Solution
Context.ai provides a product analytics platform that helps businesses understand natural language in their LLM-powered products. The platform ingests conversation transcripts via API and SDKs, then uses natural language processing (NLP) to categorize user interactions, group conversations into relevant topics, and identify user intents and behavior patterns. By tracking implicit and explicit user feedback signals, Context.ai enables businesses to measure user satisfaction and identify areas where the LLM application can be improved. The platform delivers insights into how users are engaging with the application and how the product is performing, allowing teams to flag problem areas and improve the offering.
Target Audience
The primary customers are product teams and developers building LLM-powered applications who need to understand user behavior and measure model performance.
Features
- Ingestion of message transcripts via API, SDKs, and a LangChain plugin
- Automated grouping of conversations into relevant categories using an LLM
- Identification of user intents and trends in behaviors
- Tracking of performance with implicit and explicit user feedback signals
- Analysis of conversation transcripts to determine user satisfaction
- Topic tracking to identify risky topics and monitor application responses
- SOC 2 Type II compliance
- Option for self-hosted deployment for customers with strict data residency requirements