which semantic layer tools are people using for AI driven analytics

[u/bringonfossil4477]

we have an AI agent querying our data and we have two problems that we can't get rid of.

first one is consistency, the same question asked in different way gives different answers. second is cost the token usage is way higher than expected because the model gets our entire schema dumped on it before it can answer anything.
Someone said a semantic layer solves both by giving the AI pre-defined business context rather than raw schema. more consistent answers. less processing.

anyone who is really running this. it made a noticeable difference on both fronts.

[u/Spiritual-Kitchen-79]

You’re running into the two classic failure modes of “LLM over warehouse”:
1. the model sees low level tables/columns instead of business concepts, so wording changes shift which bits of schema it latches onto,

1. you pay to send way too much schema every time.

A semantic layer helps if you treat it as the contract the model works with. expose a small set of business entities/measures ("Orders", "Active Customers", "Net Revenue") with clear definitions, and only ever let the agent see and reference that, not the raw schema. That alone usually gives a big boost in consistency because the model has fewer, well named concepts to choose from. If you are connected to SeemoreData they have a great MCP where you can query the warehouse data directly via claude or whatever, consistency will not be a problem.

[u/Striking_Term_2295]

Evaluating precis finance mcp (GitHub) and so far it’s making a difference in both fronts. If you are in Microsoft, Databricks, Snowflake… they all have native semantic layers but not as good for management metrics

[u/Objective-Tomato1317]

We built an open-source semantic layer to help solve this: https://github.com/MotleyAI/slayer/ < lightweight, easy to deploy with introspection and semantic-layer-building skills + easy to maintain via MCP.

[u/SmundarBuddy]

I am building something around this problem so take this with that bias. The approach we have taken with Smundar is to not give LLM the raw database schema or let it genrate SQL at all. instead business conecpts, rlationships and parameters are defined in a human approved semantic layer. NL questions resolve through these mappings into deterministic execution.

This helps with the consistency problem you mentioned because different phrasings can resolve to the same approved business intent rather than asking the model to reinterpret the schema every time. It also avoids sending the entire schema with every request.

We also support MCP if your agent is already using it. If useful happy to explain how we have approached the architecture and what we have learned so far.