Connected data. Right model. Exact context. Controlled cost
Everything enterprise AI depends on, governed from the prompt down to the record.
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Build working AI agents connected to your live on-prem systems, databases, and SaaS apps in minutes, and know it has the governance guardrails to scale across your organization.
6 deals are slipping 14+ days past close with no customer call in 3 weeks—$980K in exposure. The largest is Datacore at $185K, last call 24 days ago.
9 closed-won deals from Q2 have no matching invoice—$412K total. 5 are waiting on PO numbers; the rest closed in the last billing cycle.
11 reqs are open past 45 days. 6 sit with two engineering managers—both APAC backfills, averaging 68 days in onsite scheduling.
SQL Server shows 41,208 orders yesterday. Databricks matches exactly; Snowflake landed 41,067—141 rows behind, all from the final hourly load still in flight.
I found 5 committed deals with no customer contact in 14+ days—$1.1M of forecast.
The quietest:
Bracewell Marine $320K Landon Collins 19 days
NorthernTrust renewal $275K Jordan Kilpatrick 16 days
3 cost centers are 10%+ over budget—headcount is $410K of the $640K total overage.
The variance, by cost center:
Cloud infrastructure +18% $0
Field engineering +14% $260K
Support EMEA lost 7 regretted over two quarters—only 3 seats are backfilled.
By team:
Support EMEA 7 3 4
Sales NA 3 3 0
Snowflake is $12K under MySQL on 30-day ARR—one late-arriving batch from June 26.
The comparison:
MySQL (source) $4.218M 38,412 —
Snowflake $4.206M 38,290 −$12K
4 of 11 AEs under 3×—the largest gap is EMEA enterprise.
Enterprise coverage—Q2 by segment
EMEA enterprise 1.8×
NA enterprise 2.6×
APAC enterprise 2.9×
14 invoices with no matching PO—$310K, half from two vendors.
Invoices without matching POs—Q2
Meridian Cloud $96K
Atlas Staffing $58K
12 others $156K
2 queues over SLA—both are short-staffed against open reqs.
SLA breaches by queue—last week
Billing 18% −2 seats
Enterprise 9% −1 seat
Self-serve 3% Full
3 loads ran late—2 have open Jiras, orders_cdc doesn't.
Overnight loads—last night
orders_cdc 2h 10m None
billing_sync 55m DATA-812
events_rollup 40m DATA-809
SELECT [Territory], [Coverage_X], [Open_AE_Reqs] FROM [CData].[Salesforce].[territory_coverage]
Show thinking
Two territories sit under 3× coverage—APAC enterprise is furthest behind at 2.1×, with 2 open AE reqs.
APAC enterprise 2.1× 2
EMEA enterprise 2.8× 1
NA enterprise 3.4× 0
Three intercompany pairs don't net to zero—$18.4K total across the four ledgers. The largest is UK–US at $11.2K, an FX translation timing gap: Xero booked May's rate while NetSuite carried April's. The remaining two are sub-$5K rounding on shared-services allocations. All three clear with next month's revaluation run.
Counting accepted offers, Sales runs 6 heads over plan while Support sits 4 under—the company nets out at +2. The Sales overage is all NA enterprise, where three Q1 backfills closed faster than forecast; Support's gap is APAC, with two accepted offers not yet started. No department is structurally off-plan.
Four enterprise accounts dropped 20%+ in weekly active users this month. The steepest is Meridian Health at −34%, owned by Landon Collins—the drop began the week their SSO migration started, so it may be access friction rather than churn risk. The other three are all sub-$100K renewals owned by the APAC team.
Accurate answers from any model, for less
The gateway knows what you mean by pipeline, burn rate, on-hand inventory, or open headcount. That context travels with every request to any model, at the lowest token cost, governed to the record.
One gateway for every AI connection
Explore the gatewayControl at every step of the AI workflow
See governance controlsContext that compounds with every request
Learn about contextHundreds of connectors, built by CData
Browse data connectorsBetter answers at a fraction of the cost
In CData Labs studies, Connect AI answered 98.5% of 378 real prompts correctly, against 65% to 75% for other MCP approaches. All 22 models tested got it right—so an economy-tier model does the job at 178x less cost.
98.5%
accuracy rate
98.5% correct vs. 65–75% for other MCP providers on the same queries—because every request is grounded in your schema and business context first. Based on internal testing by CData Software (Q4 2025). No independent third-party verification. Actual accuracy gaps varied among platforms and MCP approaches, testing conducted using sandbox accounts containing known data sets that mirror production account structures. Results may not be representative of performance in live production environments, and results may vary. Organizations should conduct their own independent testing before making purchasing or implementation decisions. 75% range of average accuracy across platforms, results differ by MCP approach.
97.6%
fewer tokens spent
Queries resolve server-side and return only the answer—97.6% fewer tokens than handing agents raw data. Based on internal testing by CData Software (Q2 2026). No independent third-party verification. Actual token gaps varied among configurations, testing conducted using sandbox accounts containing known data sets that mirror production account structures. Results may not be representative of performance in live production environments, and results may vary. Organizations should conduct their own independent testing before making purchasing or implementation decisions.
178×
difference in model cost
The same correct answer, up to 178× apart in cost. Connect AI controls for accuracy and safety, so model choice becomes a cost decision without tradeoffs. Based on internal testing by CData Software (Q3 2026). No independent third-party verification. Actual cost gaps varied among models, testing conducted using sandbox accounts containing known data sets that mirror production account structures. Results may not be representative of performance in live production environments, and results may vary. Organizations should conduct their own independent testing before making purchasing or implementation decisions.
Enterprise MCP connectivity changes how every request executes
Most AI gateways sit in front of MCP servers someone else built—they route requests and log results, but run on what that server exposes. Connect AI ships with hundreds of CData-built MCP servers, so it knows the fields, relationships, and rules behind every connection: policy holds at the record, data is handled before the model sees it, and what the gateway learns stays with you.
The same CData connectivity powers Google, Palantir, Salesforce, Microsoft, SAP, and ServiceNow, and thousands of enterprise customers run on it today.
Production AI starts and scales with CData
Get started and keep governance as you scale.
Govern AI that's already running
Put the agents and tools your teams use behind the gateway. Every request carries an identity, stays in scope, and gets logged.
Start small, scale to thousands
Start with one team, then roll out company wide with user provisioning, managed auth, and SSO built-in.
Hundreds of sources, ready to use
Connect your databases, SaaS apps, and files to the AI tools your teams work in.
+ hundreds more sources
Proven in production
CData runs critical data workflows for internal teams—and ships as embedded connectivity inside customer-facing products.
Medical Engagement Systems Architect, GSK
Auto
schema adaptation—new columns detected and synced without code changes
90 days
of IT pipeline wait time eliminated for the medical affairs team
400+
columns in Veeva Vault CRM objects—all handled without manual maintenance
CIO, Anaqua
2 AI tools
Deployed Anthropic Claude and Microsoft Copilot
5 systems
Gave access through AI to five core business systems
6 weeks
From evaluation to company-wide rollout
Director of Product Management, Google Cloud
Hundreds
of data sources now accessible within Google Cloud—SaaS, NoSQL, legacy systems, and more
Zero
custom integration code required to connect Google Cloud to enterprise data sources
Head of Engineering, Palantir Technologies
One
unified metadata layer harmonizing data access across all source systems inside Foundry
Faster
time to AI-ready data for Foundry users, with CData handling all connectivity complexity
VP of Product, Salesforce Data Cloud
More ways to solve your data needs with CData
Connect AI Gateway
The managed AI gateway for the enterprise—connecting users, agents, and models with hundreds of sources. Apply context to every request for higher accuracy, and lower token cost, governed down to the record.
CData Sync
Continuous, change-aware replication from on-prem and cloud sources into Snowflake, Databricks, and Fabric. Incremental CDC keeps volumes current without straining production.
CData Embed
Ship white-labeled, enterprise-grade connectivity inside your product—hundreds of sources powering your AI features with passthrough auth and audit trails.
CData CLI
The CData CLI gives Cursor, Claude Code, and other AI coding assistants the schemas and connection wiring they need to generate accurate, runnable code against CData drivers, so the app your agent writes works the first time.
Python SDK
DB-API 2.0. Connect, run SQL, get rows. Drop it into any Python app or notebook to read and write live data across hundreds of sources with pandas, SQLAlchemy, and the tools you already use.
Put AI to work in your business
Connected data, the right model, and exact context on every request—governed from the prompt down to the record.