The data layer behind confident AI
With one centralized layer, you can connect AI agents to the right data, context, and systems—controlling what AI can access, and reducing what it costs to run.
Start fast with AI connected to your business systems
Build working AI agents connected to your live SaaS apps, databases, and on-prem systems in minutes, and know it has the governance guardrails to scale across your organization.
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.
AI fails on the data layer—not the model
Most AI errors trace back to incomplete system access, missing or too much context, or a lack of governance. CData resolves all three before AI ever generates a response.
Reach every system, through one layer
Hundreds of SaaS apps, databases, and on-prem systems are unified behind one relational interface, so any MCP-enabled AI tool can interact with live data.
Give AI the right organizational context
Engineer the data, tools, and definitions your AI workflows need—with built-in source intelligence that cuts unnecessary calls and keeps agents focused on what matters.
Govern what AI can see and do
Every AI-to-data interaction is governed, authenticated, and audited through precisely scoped tools and access controls—users and agents interact only with what they're allowed to.
One data layer for every AI user, agent, and system
Every team is using AI. Give them a data layer that connects every user and agent to live business context, securely and at scale.
Production AI starts and scales with CData
One data layer that offers higher accuracy and token efficiency without sacrificing governance for speed.
Spend tokens wisely
A versatile tool architecture, precise context definition, and server-side data handling ensure your tokens are spent effectively.
Govern AI that's already running
Get visibility and control over every agent-to-data interaction before ungoverned AI becomes a data exposure problem.
Start small and scale fast
Get a team up and running in a matter of hours and scale up to thousands of users.
Engineer your organizational context for AI
Curate the data, tools, and definitions on top of CData's built-in intelligence of every source system.
Semantic intelligence
Embeds deep API knowledge into connector-specific MCP instructions and skills that teach LLMs how each system works—cutting unnecessary tool calls and token consumption.
Powerful tool design
Three-tier tool architecture of universal tools, source tools, and custom tools enable versatility to respond to any request and precision for deterministic action.
Defined data views
Specify exactly which datasets, schemas, and views are accessible to AI with cross-source virtualized datasets.
Precise agent toolkits
Toolkits that precisely scope the tools and data available can be deployed as a dedicated MCP server so agents have exactly the context needed and nothing more.
Universal tools that respond to any request · Source tools tuned to each system · Custom tools for deterministic action
Proven in production
CData runs critical data workflows for internal teams—and ships as embedded connectivity inside customer-facing products.
Co-Founder & CTO, Euphonic AI
25%
of R&D capacity reclaimed by eliminating custom connector work
100+
diagnostic scenarios run continuously across every customer's revenue stack
60+
RevTech tools unified under a single ODBC connectivity layer
Founder, TheNoah.AI
80%+
reduction in data integration timelines—from months to under a week
<2 wks
from implementation to live, governed, real-time enterprise data access
Zero
custom connectors built or maintained since embedding CData
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
VP of Product Development, Tableau
250+
enterprise data sources accessible from Tableau—SaaS, NoSQL, databases, APIs, and more
Real-time
live data access with no replication—query enterprise sources as they are, not as they were
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
CData Connect AI
The first managed MCP platform for enterprise AI—live, governed access to hundreds of sources with connectivity, context, and control intact.
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.
If you run it, we can connect to it
Connect any MCP-enabled AI client to any database, on-prem system, or SaaS app.
Build AI on data you can trust
Live, governed connectivity for analytics, applications, and AI.