CData's Connect AI Gives Developers Direct LLM Access to 300+ Enterprise APIs
Building AI applications that actually work with enterprise data has been a nightmare. You either spend months building custom integrations for each data source, or you copy sensitive data somewhere it doesn't belong and deal with security teams breathing down your neck.
CData Software just launched Connect AI, and it changes the game completely. Instead of building individual API connections to Salesforce, NetSuite, JIRA, and dozens of other enterprise systems, developers get one unified interface that handles everything.
The Technical Reality Check
Here's what most AI projects look like today: product manager wants an AI assistant that can analyze sales pipeline data from Salesforce, support tickets from JIRA, and financial data from NetSuite. As the developer, you're looking at three different API documentation sets, three different authentication systems, and three different data models to normalize.
Connect AI eliminates this complexity through the Model Context Protocol (MCP). You configure your enterprise connections once through OAuth, and every system looks the same to your AI application. The LLM can write SQL queries against any connected system, whether it's a CRM, ERP, or help desk platform.
"AI needs to comprehend what data means, not just where it lives," says Manish Patel, CData's Chief Product Officer. The platform exposes complete metadata from source systems, so your AI agents understand field types, relationships, and constraints without you having to map everything manually.
Real-World Implementation
Bhavik Paryani from Paryani Construction connected their ERP system and describes the impact: "I was able to pull live financial data and create an interactive dashboard right in the middle of a project meeting." This isn't about building static reports—it's about giving AI applications the context they need to answer questions you didn't anticipate.
The technical architecture preserves data in place. Instead of ETL processes that copy information into data warehouses, your AI queries hit source systems directly. This means real-time data access without the security headaches of data replication.
CData handles the hard parts: API rate limiting, pagination, error handling, and performance optimization. Your application gets consistent SQL interfaces across 300+ different enterprise systems.
Developer Experience Details
The setup process reveals how much complexity CData abstracts away. Traditional enterprise integrations require understanding each system's API quirks, authentication methods, and data structures. With Connect AI, you authenticate once through standard OAuth flows, and the platform generates MCP server URLs.
Your AI application connects to these MCP endpoints using standard protocols. Whether you're building with Claude, ChatGPT, or custom LLM frameworks, the integration looks identical across all enterprise systems.
The platform supports both read and write operations. Your AI agents can query data, update records, create new entries, and trigger actions like closing support tickets or updating project statuses. All through the same unified interface.
Performance and Scale Considerations
CData built this on top of a decade of enterprise connectivity experience. Their connectors have processed 59 trillion read queries and 132 billion write queries across thousands of real-world deployments. This isn't experimental infrastructure—it's battle-tested technology reimagined for AI workloads.
The performance optimization matters for AI applications. The platform handles query pushdowns, parallel paging, and streaming modes. When your AI agent needs to analyze large datasets, these optimizations prevent timeout issues and reduce token consumption.
Security inheritance is another technical advantage. Instead of creating new permission systems, Connect AI respects existing enterprise access controls. If a user can only see certain Salesforce records, your AI application inherits those same restrictions automatically.
Integration Patterns
Two deployment models serve different use cases. Direct enterprise customers connect their internal systems to AI assistants for business intelligence and workflow automation. Software vendors embed Connect AI to give their products access to customer data without requiring complex onboarding processes.
The embedded model is particularly interesting for developer tool companies. Instead of asking customers to set up integrations themselves, you can offer pre-built connections to their business systems. Your AI-powered application gets complete context about their organization without them leaving your platform.
CData's existing embedded relationships with companies like Palantir, SAP, and Google Cloud demonstrate this approach at scale. These aren't simple API partnerships—they're deep integrations that handle enterprise-grade requirements.
Market Timing and Technical Evolution
The launch timing aligns with MCP adoption and the shift toward agentic AI. CData already released beta MCP servers that attracted 1,600+ developers, validating demand for enterprise data connectivity in AI applications.
The beta program revealed usage patterns: daily queries across multiple systems, heavy read operations, and growing write activity. Connect AI takes these learnings and packages them into a managed service that eliminates the installation and maintenance overhead.
"We're in early innings of agentic AI," notes Patel. Current applications focus on conversational analytics—users exploring data through natural language. The next evolution involves autonomous agents that take actions based on data insights, which requires the deep connectivity that Connect AI provides.
Implementation Considerations
Connect AI launches immediately with cloud and embedded deployment options. Pricing follows CData's existing connector model, though specific AI pricing hasn't been disclosed.
For developers evaluating the platform, the key question isn't whether it works—CData's track record speaks for itself. The question is whether unified enterprise connectivity justifies the vendor dependency versus building custom integrations in-house.
Given the complexity of maintaining 300+ different API connections, plus the security and compliance requirements of enterprise data access, the build-versus-buy decision seems clear for most development teams.
The platform represents a fundamental shift in how AI applications access enterprise data. Instead of treating integration as a necessary evil, Connect AI makes comprehensive data connectivity a competitive advantage.
