Platform / Semantic Context

Context AI can clearly understand

CData gives each connector the context AI needs to interpret prompts, fields, and business logic correctly.

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The Problem

Accessing data is one thing. Enabling AI to understand how it works is the real test.

Access without context produces confident wrong answers

Basic MCP gateways can turn prompts into API calls, but without understanding rules and relationships, responses look credible and are still wrong.

Without source context, every query becomes trial and error

AI learns each system by failing through it — more tool calls, more tokens, slower responses, lower accuracy.

Your custom data is where AI gets lost fastest

Custom fields, internal definitions, and proprietary systems hold the real business logic. If AI can't interpret that context, it can't answer reliably.

Context overload drives hallucinations and token bills

Systems that fail to synthesize the right context confuse their LLMs and run up costs.

How It Works

How CData turns access into understanding.

CData resolves source-specific meaning before the query runs — so AI plans the right call instead of failing through trial and error.

Interpret the request in the context of the source system

CData identifies the system behind the query and applies source-specific context such as field mappings, entity relationships, fiscal logic, and platform conventions

Build the right execution plan

Instead of guessing through trial and error, it selects the right fields, filters, and operations up front. For multi-source queries and workflows, CData federates and normalizes context across systems for consistent execution.

Hand off a contextualized request to the execution layer

Once the context is resolved, CData passes a fully-contextualized request to the execution layer for governed query execution. See Data Access and Action for how execution optimizes at the source.

Context first. Accurate answers, not plausible mistakes.

Key Capabilities

What semantic context fixes

Business terms

Business terms resolve correctly

“Q2 revenue” resolves to your calendar, your logic, and your fields, not a generic guess.

Complex queries

Accuracy holds up as queries get more complex

Give AI source-aware toolsets and query patterns upfront so it does not waste calls discovering them on the fly.

Custom data

Custom and proprietary data becomes AI-readable

Add definitions for custom fields, proprietary systems, and internal terminology so AI can interpret your real data model.

Cross-system

Results stay consistent across systems

Apply a consistent semantic context across CRM, ERP, data warehouse, and project systems without flattening each source into the same lowest-common-denominator model.

Benchmark

Proof that context improves accuracy.

0%

query accuracy across 378 enterprise queries

In the CData MCP Accuracy Benchmark, CData outperformed basic MCP approaches that scored between 65% and 75% across CRM, ERP, project management, and data warehouse scenarios.

0%

ERP was the clearest gap

In the same benchmark, the native NetSuite MCP server scored 0% on ERP queries. CData scored 100%.

Implementation Path

From AI access to understanding in days

Day 01

Connect your first source

Apply source-specific context already built into the connector so AI can interpret fields, relationships, and conventions without manual prompting Owner and Milestone sections stay the same

Owner: IT / Data infrastructure

Milestone: Your first connected system is live, and AI can answer queries against it with source-aware context already in place.

Day 03

Add semantic context

Define internal field meanings, business rules, and company-specific terminology to drive the AI accuracy enterprises need. Owner and Milestone sections stay the same

Owner: Data team / Business analysts

Milestone: Custom fields and internal systems are now understandable to AI in business terms, not just technical schema terms.

Day 07

Standardize governed definitions

Teams can now define reusable business metrics and logic to drive consistency across queries, dashboards, and applications. Owner and Milestones stay the same

Owner: Data team / Analytics / Platform owners

Milestone: Business-critical queries are defined once and answered consistently across users and systems.

Security & compliance

Data control that preserves governance.

Governance model

No data movement required
Semantic context respects user permissions
Every query is logged at the interaction level
Customer-defined context is encrypted and isolated per account
Run-time PII entity detection and masking

Certifications & controls

SOC 2 Type II — Completed.
ISO/IEC 27001:2022 — Completed.

FAQ

Questions teams ask first.

How is this different from prompt engineering?

Prompt engineering asks the user to explain the data correctly every time. CData embeds that context in the connector, so the system already knows how the source works before the query is written.
What happens when a business term maps to different fields in different systems?

Each CData connector includes source-specific context for how that system represents common business concepts: field meanings, entity relationships, and platform conventions. The same business term can resolve differently in Salesforce than in NetSuite, because CData's context layer knows how each system actually works.
Can this work with systems CData did not originally build connectors for?

Yes. User-defined semantic context can be applied to internal databases, proprietary systems, and custom applications so AI can interpret those sources correctly at query time.
Does adding semantic context slow performance down?

No. In practice, it reduces wasted tool calls and trial-and-error cycles, which can improve both response efficiency and token consumption.
Do I need to define everything manually?

No. Source-level context is already built into CData's connectors for supported systems. You only need to add definitions for the custom fields, proprietary logic, and internal terminology unique to your environment.

Give AI the context it needs before the first query.

Talk to our team about improving accuracy across your enterprise systems with source-level semantic context. Or explore how Semantic Context fits into the broader CData platform.