Shanc vs Omni

Omni grounds its AI inside a model you build. Shanc builds the model.

Omni's AI is genuinely well-grounded — inside the semantic model your team defines and maintains by hand. Shanc builds that layer for you from your schema and code, keeps it current automatically, and serves it to any agent, not only Omni's.

Try Shanc

Side by side

ShancOmni

Who builds the layerShanc, from your dataYour team, by hand

Documents your data for youYes, automaticallyNo — AI sits on top of the model you author

Stays currentAuto-refresh on every changeManual — the model doesn't self-refresh

A question you haven't modeledAnswered from the live layerNeeds modeling first

GroundingStrong, across your whole warehouseStrong, inside the model you built

Serves any agentAny agent, over MCPGrounding stays inside Omni's BI product

What's different

No modeling project to start

With Omni, the work begins with modeling — someone defines the topics, metrics and joins before the AI is useful. Shanc infers all of that from your schema, query logs and code, so the layer exists the day you connect your sources, not the quarter after.

The layer maintains itself

Keeping an Omni model in sync with a changing warehouse is ongoing, manual work your data team owns. Shanc picks up every schema and query change on its own, so definitions don't drift and nobody is assigned to chase them.

Answers past the edges of your model

Omni is reliable inside the metrics you've modeled — and a genuinely new question needs new modeling before the AI can touch it. Because Shanc's layer is built from your whole warehouse, it can answer questions you never pre-defined.

Grounding that travels

Omni's grounding lives inside Omni's BI product. Shanc runs as an MCP server, so the same grounded layer serves Claude, GPT, Gemini or your own agents — you're not tying your context to one interface.