Attest – Test AI agents with 8-layer graduated assertions
8-layer assertion pipeline cuts LLM-judge calls by ~80%—free layers handle deterministic checks first.

Semantic Model layer solves LLM inconsistency that broke AskEdith.
Data teams, business analysts, startups
Hex · Mode · ThoughtSpot
To everyone that says "just link Claude to your db”: imagine the chaos of conflicting definitions and analysis that would show up in a business setting.
Ask “what’s our revenue?” twice, two days apart or to a different model. There’s no guarantee that you’ll get the same results. Now imagine giving that to all of the non-technical users at your company.
It's not a model problem. We learned this the hard way. When we launched AskEdith here in 2022, and you told us (https://news.ycombinator.com/item?id=33435361): “you'll still have to check the SQL,” “trust is everything,” “answers won’t be consistent”. You were right.
Now, Athenic defines KPIs and formulas deterministically in a Semantic Model. The Semantic Model is made up of modular, composable units that can make up complex analysis, while guaranteeing determinism and accuracy. The LLM’s only responsibility is interpreting your question (which even non-technical users can double check).
`revenue = sum(order_total − refunds) where status = 'completed'`
Ask for revenue and everyone gets the same number, every time.
Three years of learnings from working with top startups and Fortune 500 companies later, we just shipped 2.0. Chat-to-insight, plus dashboards and automations that run on a schedule and land in your email. Tell us we're wrong.
8-layer assertion pipeline cuts LLM-judge calls by ~80%—free layers handle deterministic checks first.
Identity-based memory vs similarity—clean separation of deterministic truth from probabilistic reasoning.
Cube alternative for AI agents, but semantic layers already exist.
Deterministic graphs instead of vector embeddings sound clever, but long-context windows and RAG tools already solve this problem cheaper.
APCA-driven generation fixes semantic color drift that pywal and base16 ignore.
Good explainer on PDF metadata, but this is a known issue with standard library fixes.