Accelerate data product delivery with Semy
Turn complex data, semantic and context engineering tasks into a prompt, then connect agents to each data product’s governed MCP endpoint.
A custom agent for building Intelligent Data Product
Every enterprise now invests in AI engineering for software. Semy brings the same acceleration to the design and delivery of your Data Products
Complete complex task
Purpose-trained for end-to-end delivery: generate a data product from a prompt, design governance workflows, embed context for agents via MCP.
Governed and auditable
Trained on real-world use cases and grounded in your context, Semy can still err. Every action has chain-of-thought, dev-in-loop approval, rollback.
Embedded and extensible
Semy lives in your VS Code workspace. Choose any coding model – GitHub Copilot, Codex, Opus, Cortex Code – or any LLM via our extensible SDK.
How Semy engineers your Data Products
From semantic modeling to governed reasoning and embedded AI, Semy works inside the same DataOps lifecycle your engineers already use
SemQL: the data, semantic, and context engineering language
Semarchy’s declarative syntax behind over a trillion golden records. SQL-like, with built-in graph traversal – simple to learn, designed for agents.
Model + visualize semantics
Logical ER-modeling for entities, relationships and rules, with built-in graph crawling – no complex SQL joins or dedicated graph database tool.
Agent-optimized schema
SemQL compiles to .SEML (YAML-based): human-readable and structured for agents to consume data without bolting on a catalog context layer.
Go from prompt to Intelligent Data Product in minutes
Ask Semy to generate a complete data product from a sample CSV and your instructions – semantic model, entities, attributes, workflows and more.
Contextual, compliant
Custom-trained, connected in real time to our secure MCP docs and governed access to your files – so only valid SemQL, grounded in your specs.
Easily extensible
Update an attribute, connect another domain or add workflows as demand grows – version, branch and rollback with our DataOps-first approach.
Safely reason through complex data engineering requirements
Even leading coding agents make mistakes. Semy reasons in a governed way on needs like GDPR and consent workflows, without bypassing oversight.
Auditable agent reasoning
Monitor, log and trace every suggestion and action, with version control, automated validation testing and CI/CD adherence – and rollback.
Dev-in-loop approval
Review and approve every agent plan before execution – one line of SemQL or hundreds across files. Developers are always in full control.
Enhance core data product capabilities and experiences
Semantically enrich data products, add vector-based matching using Snowflake’s Cortex AI, or embed an agentic assistant directly in any data app.
Smarter data products
Make consumption more effective for humans and agents: enrich missing context, augment deterministic quality rules with AI-driven ones.
Better user experiences
Give users intuitive ways to surface insights with AI agents, or small AI automations inside stewardship workflows that cut manual work and errors.
SemQL assistance without ever leaving your workspace
Semy works in your DXP workspace and understands your projects’ context, so you build without switching from VS Code to a coding agent and back.
Your models, your choice
Bring your own key and use any model your IT and security teams approve – Copilot, Codex, Claude – each interacting securely with Semy and MCP.
SemQL Assistant
AI-powered help writing valid, ready-to-use SemQL expressions from natural language, lowering the barrier for any developer.
Enterprises across industries are leveraginDataOps in SDP to scale their data product delivery, increase agility and speed time to trusted insights.
Hogan Le
Senior Manager, Governance and BI Systems, Chipotle
James Wilson
VP of Analytics and Enterprise Solutions, Rexel USA
Explore and learn more
From blog posts to product guides and everything in between, we’re here to help you along your data journey.
