Connected data. Right model. Exact context. Controlled cost
Everything enterprise AI depends on, governed from the prompt down to the record.
The AI gateway between people, agents, systems, and models
Agents and individuals go to work on your connected SaaS apps, databases, and on-premises systems, with policy enforced at the record and every request routed to the right model.
8 deals are slipping past close by 14+ days—$1.2M in exposure. The largest is Datacore at $185K, now 31 days past its original close date.
Q1 ties out to within $340 across both systems—3 exceptions, all FX rounding on intercompany entries posted on 2026-03-31.
12 reqs are open past 60 days. Engineering holds 7 of them—APAC backfills are the bottleneck, averaging 74 days in onsite scheduling.
Found 5 differences on prod.orders: 4 columns added and 1 renamed—payment_token → token (v32).
I found 46 opportunities created today (June 18, 2026) in Salesforce.
A few of the most recent:
Bracewell Marine Bracewell Marine Group Landon Collins Qualified NorthernTrust NorthernTrust Jordan Kilpatrick Discovering
Two enterprise renewals slipped to June—net -$210K vs plan.
The variance, by account:
Aldermore SaaS $640K $520K -$120K
Brightwell Group $310K $220K -$90K
Support EMEA is up to 14%—3x the company baseline.
The teams trending up:
Support EMEA 7 50 14%
Sales NA 3 82 3.6%
p95 = 184 ms, up 12% since the v32 deploy on Tuesday.
By endpoint:
/orders/read 184 ms v32 +12%
/orders/list 142 ms v32 +4%
4 of 11 AEs under 3x;—the largest gap is EMEA enterprise.
Enterprise coverage—Q2 by segment
EMEA enterprise 1.8x
NA enterprise 2.6x
$2.4M above plan; cloud infrastructure is 60% of the overage.
PO line items above plan—Q2
Cloud infrastructure $1.44M
Prof. services $0.62M
Network ops breaching on 18% of P2s—down 2 engineers since March.
SLA breaches by queue
Network ops 18%
Endpoint support 7%
2 jobs failed on schema drift; 1 ran 3x longer than baseline.
ETL jobs—last 24 hours
orders_cdc Schema drift
billing_sync Failed
SELECT [Territory], [Coverage_X], [Quota_Target] FROM [CData].[Salesforce].[territory_coverage]
Show thinking
APAC enterprise is the only segment below target—2.1x coverage against a 3x quota.
APAC enterprise 2.1x 3.0x
EMEA enterprise 2.8x 3.0x
£1,240 is unmatched across 6 entries—every one is VAT timing that straddles the period boundary, not a posting error. Xero recognized the VAT on the invoice date while NetSuite booked it on settlement, so the gap clears once March settles. Nothing here needs a manual journal.
Sales is running 6 heads over the approved plan while Support sits 4 under, so the company nets out at just +2 against plan. The Sales overage is all in NA enterprise, where three Q1 backfills closed faster than forecast; Support's gap is APAC, still waiting on two open reqs. No single department is structurally off-plan.
Enterprise daily active orgs are up 9% month-over-month, while self-serve has stayed flat for the third straight month. The enterprise lift tracks the two large rollouts that finished onboarding in early June, not broad-based expansion. Self-serve activation is the metric to watch heading into Q3.
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.