AI that knows what your business actually means

Models do not know that Acme Corp and Acme Corporation are the same account, or that your stage names mean something different from what they assume. Syntaxia organizes your full GTM context into AI-ready data: resolved entities, shared semantics, and trusted lineage. That foundation powers Decision Intelligence, your own LLMs with bring-your-own-key, and external agents through MCP so they reason on real data, not guesses.

How it works

Syntaxia organizes your full GTM context into AI-ready data: resolved entities, shared semantics, and trusted lineage. That same foundation powers Decision Intelligence, your own LLMs with bring-your-own-key, and external models through MCP.

Foundation steps

Three steps from connected systems to AI-ready context.

  1. 1

    Unify the GTM stack

    Resolve records across CRM, enrichment, and conversation systems into clean canonical entities.

  2. 2

    Define business meaning

    Stages, metrics, and relationships become explicit, machine-readable semantics for people and models.

  3. 3

    Serve every consumer

    Expose the same governed context to Decision Intelligence, bring-your-own-key LLMs, and external agents over MCP.

Connected GTM systems

live

Salesforce

stages · owners · ARR

live

HubSpot

lifecycle · activity

live

ZoomInfo

firmographics

live

Gong

call notes · intent

Governed GTM context

AI-ready

One business model for people and models

Resolved customers, pipeline meaning, and lineage organized so Decision Intelligence and LLMs reason on the same reality.

  • Resolved entities

    One account, contact, and opportunity identity across systems

  • GTM semantics

    Pipeline, stages, and metrics defined once for every consumer

  • Lineage

    Every field traces back to the source that produced it

Native

Decision Intelligence

Teams and workflows decide from the same governed GTM reality.

BYOK

Your LLM · BYOK

Bring your own model key. Reason on Syntaxia context without reinventing your ontology.

MCP

External LLMs · MCP

MCP-ready access so outside agents query real business data with shared meaning.

What It Enables

Decision Intelligence on real GTM context

Teams decide from the same resolved customers, pipeline meaning, and lineage the rest of the platform uses.

Bring your own LLM

Connect your model with your own key and ground it in Syntaxia context instead of rebuilding an ontology in the prompt.

MCP-ready for external agents

Outside LLMs and tools query governed business data through MCP, so answers stay tied to your real GTM reality.

Frequently asked questions

Keep exploring

AI-ReadyData

Capability

AI-Ready Data

Governed GTM context for Decision Intelligence and LLMs. MCP-ready and bring-your-own-key so models reason on your real business data.

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