Most enterprise AI programs are stuck. Not because the models are bad, but because the underlying infrastructure can't keep up. Costs are climbing, productivity gains are flat, and organizations are locking down access rather than expanding it because they don't have the governance controls they need to feel comfortable.
CData Software has been watching this pattern play out across hundreds of enterprise deployments over the past year. On September 29, the company is announcing what it sees as the fix: CData Connect AI Gateway, a single control point between AI agents and the enterprise systems they need to act on.
Three Problems. One Control Point.
Marie Forshaw, CData's SVP of Product Marketing, laid out the three challenges clearly during a pre-launch briefing. They're likely familiar to anyone who's been building or deploying AI in an enterprise context.
The first is rising costs. AI budgets are burning through faster than organizations can justify. Without fine-grained visibility into who's using what model for what task, finance teams are doing the only thing they can: shutting things down.
The second is stagnant productivity. Some of that is the lockdown. But Forshaw pointed to two other contributing factors: ongoing accuracy and reliability issues with AI responses, and a near-total freeze on write operations. Most organizations have enabled read use cases — querying data, summarizing it, surfacing it. Very few have enabled AI agents to actually take action. The risk is too high, and the controls aren't there.
The third is governance. This is where the developer-versus-leadership tension gets real. Forshaw made the point directly: developers may not care much about centralized control, but CTOs and CIOs absolutely do. And without their sign-off, AI projects don't scale.
Connect AI Gateway is designed to address all three from one place.
What the Gateway Actually Does
The platform has four layers. Two of them — data connectivity and security controls — are existing CData capabilities. The two new layers are the gateway itself and a context engine, and they're where most of the interesting development is happening.
The Gateway Layer handles model routing, MCP traffic, and agent orchestration. It registers models, MCP servers, and agents once, then routes requests based on availability, cost, and latency. Intent-based routing is part of this — the system reads the complexity of a prompt and sends simpler requests to cheaper models automatically. Token budgets can be set at the team, agent, or tool level, giving organizations real cost visibility and control rather than a blunt shutdown switch.
The Context Engine is the more distinctive part of the announcement. CData's argument is that most gateway providers are just routing traffic between models and MCP servers. Because CData owns the connection all the way down to the source system, it can build up and apply business context at every step of a request — and that's what separates useful AI from AI that keeps getting things wrong.
The context engine pulls from four sources: company knowledge (documented processes, SOPs, Slack and Teams conversations), semantic definitions (imported from Power BI, BigQuery, dbt, or built natively in the platform), system context (schema, relationships, and field meanings from connected source systems), and data modeling (cross-source joins and derived views that calculate answers before they reach the model).
Everything gets normalized into Open Knowledge Format and visualized as a context graph that administrators can edit and extend.
The Self-Learning Loop
One of the more forward-looking pieces of this announcement is a self-learning capability CData is calling the self-learning loop. The idea is that every interaction makes the system smarter — prompt patterns become vocabulary, query patterns become join hints, user-level corrections get written back to the knowledge bundle, and facts that multiple users share get promoted to company-wide knowledge.
This capability is pre-beta at announcement. No customers are in production with it yet, so there's no empirical data on how long the improvement curve takes in practice. CData acknowledges that directly. The honest answer is: we'll know more in six months.
Organizations will have control over what gets promoted from personal to shared knowledge. Everything can require human approval before it becomes part of the company context, or you can set specific categories to promote automatically. That configurability matters for teams with compliance requirements.
The Numbers: Accuracy and Cost
CData's accuracy benchmarking from earlier this year showed Connect AI at 98.5% accuracy across 378 enterprise queries, compared to 65–75% for competing MCP providers. That gap matters more as AI moves from read to write operations — a 70% accuracy rate on an agent that's taking action in your ERP is not a productivity tool. It's a liability.
The newer study, published just before this briefing, tested 22 models against live enterprise data — with and without Connect AI's data layer configured. With it, every model answered correctly. Without it, every model made mistakes. The cost spread between the cheapest model tested (Mistral Small) and the most expensive (Claude Fable 5.1) was 175x.
Jared Johnson, CData's Director of Technology Evangelism, explained the mechanism clearly. When you ask an AI to list your five healthiest customers, it has no idea what "healthy" means in your context. You can put that definition in the system prompt, but that doesn't guarantee it gets honored. When you build that definition into a derived view inside Connect AI, the model can't get it wrong — because it doesn't have access to the raw fields. It only gets the calculated answer. The model isn't doing the reasoning. CData is.
That's the architecture. And it's why the same cheap model can deliver the same correct answer as one that costs 175 times more.
Developer Setup: Minutes to Hours
Adobe cut regression test delivery from ten weeks of manual effort to roughly one week by connecting to SAP through Connect AI and running a test automation agent. For developers wondering what setup actually looks like: basic authentication to a source system can be done in minutes. A working agent with governed access typically takes hours to a day. From there, it's an iterative process — start with minimal configuration, dial in derived views and custom tools over time as the workflow becomes clearer.
The path from 70% to 98.5% accuracy isn't a configuration switch. It's a development process. But the starting point is lower-friction than building your own MCP server stack.
The Honest Case for Switching
For development teams already running their own MCP servers, the question is fair: why hand that off to a managed platform? Johnson's answer was practical — it can seem cheaper and faster to manage your own servers when you're working on one system. The complexity compounds when you're trying to manage MCP access across multiple systems with different security models, different permission structures, and different teams using different tools.
What CData is offering is the centralized governance layer that makes it possible to turn on write operations without giving a CIO a reason to shut the whole program down. That may matter less to individual developers in the short run. It matters a lot to the people deciding whether to renew the AI budget.
Bottom Line
CData Connect AI Gateway enters an increasingly crowded market. Several AI gateway announcements have come out in the past few months, and CData is not the only company making the data-layer argument. But the company has a legitimate differentiator in owning the source system connection — not just the routing layer above it — and the cost study is a concrete illustration of what that unlocks.
The self-learning context engine is the most interesting long-term bet in this announcement, and also the one with the least evidence behind it yet. Worth watching as customer data comes in.
Connect AI Gateway is available through an early access program beginning September 29, 2026. More information at cdata.com.