camelAI Embedded

Product Introduction

1. camelAI Embedded is an AI analytics integration solution that enables developers to embed a conversational data analysis interface directly into their applications via API and iframe. It allows end-users to interact with connected databases using natural language queries, generating instant insights, visual charts, and SQL-level analysis without requiring technical expertise. The platform supports integration with any database system while maintaining enterprise-grade security protocols.
2. The core value lies in transforming raw data into actionable intelligence through AI-powered conversations, enabling product teams to add advanced analytics capabilities within their applications within minutes. It eliminates the need for custom dashboard development by providing pre-built, customizable chat interfaces that deliver real-time data exploration to end-users.

Main Features

1. The platform offers seamless integration with enterprise databases through secure OAuth 2.0 authentication and API connections, supporting real-time data querying without requiring permanent data storage in camelAI's systems. Developers can connect PostgreSQL, MySQL, Snowflake, and other SQL/NoSQL databases through standardized connectors.
2. Customizable chat interface components include white-label UI customization, brand-aligned theme options, and adjustable response formats that support both textual explanations and interactive chart visualizations (bar, line, pie). The iframe implementation allows embedding with fewer than 10 lines of JavaScript code.
3. Enterprise security features include AES-256 encryption for data in transit and at rest, role-based access control (RBAC) down to row-level permissions, and optional self-hosted deployment through Kubernetes clusters. The platform is CASA-certified with SOC 2 compliance in progress, featuring automatic connection termination upon access revocation.

Problems Solved

1. Eliminates the technical barrier for non-SQL users to perform complex data analysis by translating natural language queries into optimized database operations through multiple AI model routing (OpenAI GPT-4 and Anthropic Claude).
2. Targets product development teams needing to implement advanced analytics features without dedicating engineering resources to build custom BI solutions from scratch.
3. Enables SaaS platforms to offer embedded analytics as a value-added feature, particularly useful for CRM, financial software, and operational tools requiring instant data insights. Specific use cases include sales performance analysis, financial reporting automation, and operational metric tracking through conversational interfaces.

Unique Advantages

1. Unlike traditional BI tools, camelAI Embedded provides real-time conversational analysis without pre-built dashboards, using dynamic SQL generation that adapts to evolving database schemas. The platform automatically maintains query context across chat sessions through persistent agent loops.
2. Proprietary query optimization technology combines multiple AI models (OpenAI and Anthropic) with automatic failover routing, achieving 98% SQL accuracy according to internal benchmarks. The system automatically verifies query results through data sampling before presenting responses to users.
3. Competitive edge comes from military-grade security implementations including private VPC peering options and on-premise deployment capabilities unavailable in competing cloud-only solutions. The platform offers granular data control through RBAC policies that sync with Active Directory and Okta integrations.

Frequently Asked Questions (FAQ)

1. How is my data stored and processed? camelAI uses transient data processing where queries execute directly against your connected databases without permanent storage. Conversation histories are encrypted using AES-256 and stored in AWS S3 buckets with automatic deletion after 90 days unless configured otherwise. Database credentials are never stored in plain text.
2. Does camelAI use my data for AI training? No data from customer connections is used for model training. The platform operates under strict data isolation, using OpenAI and Anthropic APIs with enterprise-grade data protection agreements that prohibit training on user inputs. All AI providers are contractually obligated to delete query data within 30 days.
3. How does security work for connected applications? Connections use OAuth 2.0 where available, otherwise JWT tokens with short-term validity. Database access is limited to read-only operations by default, with write capabilities requiring explicit permission configuration. Network communication uses mutual TLS (mTLS) for all API calls between camelAI and customer infrastructure.