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The Complete Buyer’s Guide to a Semantic Layer for AI

As a savvy data and analytics leader, you may be looking to use a semantic layer to help your organization make smarter data-driven decisions at scale. But it can be hard to know what to choose and how to implement it well.

Our buyer’s guide compares every approach on the market, from letting the LLM figure it out to a universal semantic layer. It gives you ten criteria to test, a 30-day proof of value, and a scorecard to rank vendors on your own questions.

Does the semantic layer compute the right answer on your own questions? Every part of this 22-page buyer’s guide comes back to that. It compares the approaches on the market, from letting the LLM figure it out to a universal semantic layer. It gives you ten criteria to test, each with a question to put to the vendor and a test to run on your own data. It also includes a 30-day proof-of-value plan and a scorecard for ranking vendors yourself.

What you’ll learn:

  1. Why BI went from threatened to legacy in 18 months, why buyers now judge a semantic layer by its answers, and five signs you need one.
  2. The six approaches on the market, from hoping the LLM gets smarter to a universal semantic layer, and which one computes metrics instead of describing them.
  3. Ten evaluation criteria, from measured accuracy on your own questions to security and governance that pass through, each with a question for the vendor and a test to run.
  4. How to run a 30-day proof of value with the questions your team actually asks.
  5. A scorecard that weights 25 features, from computation and accuracy testing to agents and security, plus how AtScale fits each criterion.

What’s inside the buyer’s guide

Why now: What changed for buyers

  • Why buyers judge by answers now
  • What a semantic layer does for AI
  • Five signs you need one

Evaluate: What to look for

  • The six approaches on the market
  • Ten criteria to evaluate
  • How AtScale fits the criteria

Prove it: How to decide

  • Run a 30-day proof of value
  • The scorecard
  • Bring your own questions

Score them yourself

The guide’s scorecard weights 25 features, from computation and accuracy testing to agents and security. These are our scores for five approaches, out of 510:

  • 126: Hope the LLM gets smarter
  • 164: Build your own skill
  • 218: Vendor X ontology
  • 282: Vendor Y database semantics
  • 498: AtScale

Change the weights, add your vendors, and see if the ranking holds. Rescore every approach with your own weights in the interactive scorecard.

Ten criteria to test before you buy

For each one, the guide gives you a question to put to the vendor and a test to run on your own data.

  1. Computes metrics instead of describing them: A layer that only supplies descriptions leaves the model to write the SQL.
  2. Measured accuracy on your own questions: A vendor’s demo questions tell you little about your data.
  3. Asks before it answers: Many wrong answers start with a vague term that maps to more than one metric.
  4. Works for agents as well as people: Agents need the same definitions, their own identity and a record of what they acted on.
  5. Open to every AI client and BI tool: AI clients change every few months, and BI tools won’t disappear overnight.
  6. An open, portable model kept as code: Definitions an agent depends on shouldn’t be locked in a proprietary format.
  7. AI-assisted modeling, with a person approving: Agents ask about more of the business than analysts did, and a slow model delays every workflow.
  8. Runs where your data lives: Most enterprises run more than one platform, and the one in five years may not be today’s.
  9. Performance and cost at AI scale: AI sends far more queries than dashboards did, and every answer costs tokens and compute.
  10. Security and governance that pass through: The semantic layer serves every analytical query, so it has to fit your security stack.

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