CData Connect AI is the AI gateway that makes AI more accurate, for less. Built on CData's enterprise MCP platform, it gives every agent, copilot, and model one governed path to hundreds of enterprise systems, including Salesforce, NetSuite, SAP, ServiceNow, Workday, Snowflake, and Databricks, over live data instead of stale copies.
Unlike gateways that only proxy traffic to model providers, Connect AI starts at the source connection. Its Context Engine carries your schemas, metric definitions, business rules, and company knowledge with every request, so answers are grounded in how your business actually works, and a self-learning loop turns every correction and successful query into shared context that every agent on every model inherits. Identity is verified on every call, policy is enforced down to the record, and joins, filters, and calculations run in the platform so the model reads only what it needs.
Increase accuracy
Context Engine: A unified context graph of system context (schemas, objects, and relationships known on day one), curated data models, semantic metric definitions, and company knowledge, retrievable by any model over MCP. Every answer applies the same definitions, no matter which tool or model asks.
Self-learning loop: Every user correction, recurring join or filter, successful tool-call pattern, and definition surfaced from wikis, Slack, or Teams becomes a governed proposal. Once an owner approves it, every agent on every model inherits it, so accuracy compounds with use instead of decaying between manual updates.
Virtual Data Layer: Live, modeled access across sources, so agents answer from current records rather than stale copies, with cross-system joins and derived views that shape data for each use case.
Reduce cost
Model Gateway: Route every request to the lowest-cost model that fits the task across Claude, GPT, Gemini, Llama, Mistral, DeepSeek, Qwen, xAI, and local deployments, with automatic failover, per-team and per-agent budgets, and caching.
Fewer tokens per request: Joins, filters, and calculations run in the platform before data reaches the model, and context retrieval returns only the matching concept and its links. In benchmark conditions, a multi-source query dropped from 22 tool calls to one and from 183,541 tokens to 4,427.
Lean toolsets: The MCP Gateway exposes only the tools each agent needs, cutting retries and extra turns from agents picking the wrong tool.
Govern access
MCP Gateway: One governed connection layer to hundreds of enterprise systems through CData-owned, custom, or third-party MCP servers, with every agent registered on a single control plane.
Identity and record-level policy: User and agent identity resolved on every request, with policy enforced in each connected system down to the record, not just at the API edge.
Observability and audit: Guardrails, budgets, and usage visible across every team and tool, with every answer linked to one audit trail. The context graph lives in the gateway, not in any model, so switching providers never leaks or loses accumulated knowledge.
Proven results
In CData Labs benchmarks, Connect AI answered 98.5% of queries correctly versus 65–75% for other MCP providers, and delivered the same correct answer at up to a 178× cost spread across models. Works with Claude Code, Copilot, Cursor, LangChain, CrewAI, Codex, Bedrock, and any custom agent. Trusted by GSK, Salesforce, Palantir, Google, and 10,000+ CData customers.
Average Rating: 4.6/5.0
Total Reviews: 27
Who Is the Company Behind CData Connect AI?
-
Seller: CData
-
Company Website:
-
Year Founded: 2014
-
HQ Location: Chapel Hill, NC
-
Twitter: @cdatasoftware
2,000 Twitter followers
-
LinkedIn® Page: www.linkedin.com
493 employees on LinkedIn®
Who Uses This Product?
-
Top Industries: Information Technology and Services
-
Company Size: 54% Medium, 25% Small
What Do G2 Reviewers Say About CData Connect AI?
AI-generated summary from verified user reviews
Pros
- Users appreciate the intuitive interface of CData Connect AI, empowering non-technical users to create dashboards independently.
- Users praise the intuitive and user-friendly interface of CData Connect AI, making navigation effortless for all.
- Users appreciate the ease of use and integration of CData Connect AI, enhancing their productivity seamlessly.
- Users find the easy integrations with various tools to be a significant advantage of CData Connect AI.
- Users value the automation capabilities of CData Connect AI, enabling self-sufficient dashboard creation without developer dependency.
Cons
- Users express concerns over poor customer support, noting slow response times that hinder their overall experience.
- Users find the initial learning curve to be somewhat overwhelming, impacting their early experience with CData Connect AI.
- Users report occasional performance issues that can disrupt their experience with CData Connect AI.
- Users face poor performance at times, impacting usability despite decent customer support.