Shanc documents tables, metrics and business logic, then keeps everything current as your data evolves. So your team and AI agents understand the data they work with.
Shanc documents your warehouse, keeps it current automatically, and serves that context to every agent and team.
Shanc reads your warehouse schema, Git history and existing docs to document your tables, columns and the different names your teams use for the same things. Understand what your data means without tracking down the developer who built it.
Analysts lose 15% of their time updating the layer.
Shanc builds and maintains the layer, so there’s no context to paste at the start of each session.
People paste in schemas from memory at the start of each session.
Shanc turns that context into one shared layer.
Build the layer yourself and it’s an engineer’s salary to keep current. Run an agent without one and you pay to reload your schema into context on every query.
Without a layer, an agent reloads your whole schema and definitions on every query — you pay for those tokens every time. Shanc loads the context once, so each question uses far fewer.
A typical request takes 1–4 weeks.
Your business partners get answers they can trust, and you’re not left worrying they’ve acted on wrong numbers.
An ungrounded agent answers analytical questions correctly 10–30% of the time.
Running the same questions through Shanc — validated against a maintained layer of real definitions — roughly doubles that.
Shanc runs on your own machine or inside your cloud, alongside the warehouse it reads. Compute against your data happens where the data already lives — never on infrastructure Shanc operates, and never through a Shanc-hosted service.
SOC 2 Type I in progress. Read-only, least-privilege access to your warehouse — Shanc never requests write scope.
For teams whose data can't touch a cloud: Shanc installs entirely within your own datacentre, including air-gapped environments. No outbound connection, no dependency on any Shanc-hosted service.
You can, but Claude doesn't know what your data means. It doesn't know that rev_net is the revenue Finance uses, which table is canonical, or how two tables are meant to join. So it guesses, and a wrong answer is worse than no answer. The missing piece is context — what every table and metric actually means. Shanc builds that and keeps it current, so Claude reasons over facts instead of guessing.
You can build a context layer — plenty of teams do. Building it isn't the hard part; keeping it alive is. The layer is accurate the day you finish it and starts drifting the next time a developer ships a schema change. Maintaining it by hand becomes a standing job: someone tracks every commit, updates definitions, re-checks joins. That's the cost Shanc removes. It reads your git history and query logs and updates the layer on its own, so you get what you'd build yourself, without the person whose whole job is keeping it from going stale.
An LLM is only as good as the context it's given, and on its own it has none of yours. Ask it about your data and it fills the gaps with plausible guesses — the more fluent it sounds, the easier those guesses are to miss. More prompting doesn't fix it; it produces more confident wrong answers. Shanc gives the model a factual layer to reason over, so it works from your real definitions instead of inventing them. Same model, roughly twice the accuracy on analytical tasks.
A skill is a set of instructions you write for an agent. It's static, it isn't grounded in your actual schema, and it goes out of date the moment your data changes — so the agent follows the instruction confidently and still returns the wrong number. Shanc isn't instructions about your data, it's a structured layer built from your data: real tables, real definitions, real query logs, validated before anything runs and updated as the data moves.
Most tools here either replace your stack or still need a person to maintain them. BI platforms ask you to move your analytics into their interface. Warehouse-native layers get curated by hand. The ones that auto-generate a layer mostly build it once and leave you to keep it current. Shanc sits under the stack you already have, maintains the layer itself from your commits and query logs, and serves it to whatever agent you already use over MCP. Nothing to migrate, nobody assigned to keep it alive.
Ask about your own data and get answers grounded in your real definitions — the way your agents will.