§ 01 — Vendor-neutral AI analytics certification
Prove the answer.
Not just thenumber.
Xemeta runs certified business questions against the AI analytics systems you've deployed — Snowflake CoWork, Cortex Analyst, Databricks Genie, internal NLQ apps — then reports whether each answer was accurate, stable, and derived the right way. Not whether it merely looked plausible.
READ-ONLY · OUT-OF-BAND · RUNS BESIDE THE TOOLS YOU HAVE, NEVER IN THE QUERY PATH
§ 02 — The accuracy reality
Vendors claim 90%+.
Independent benchmarks disagree.
Real-world accuracy on complex business questions sits at 60–70%, even with a curated semantic layer — and degrades multiplicatively once a workflow spans more than one step.
The hatched segment marks an estimate rather than a measured benchmark score — the only bar above that isn't a fixed number is also the one closest to what your business users actually see.
3-step workflow
~34%
at 70% accuracy per step, assuming independence
5-step workflow
~17%
errors compound; agentic pipelines rarely stop at one step
7-step workflow
~8%
correlated errors often make the real outcome worse
Monitoring catches a query that fails to execute or a result that looks statistically unusual. It structurally cannot catch a query that ran cleanly and returned a plausible number derived from the wrong metric, grain, or filter.
§ 03 — The semantic intent engine
“Right answer, wrong question” is invisible to monitoring.
The most common production failure in enterprise AI analytics isn't a crash — it's a query that executes cleanly and returns a plausible value built on the wrong metric, entity, grain, filter, or join path. It only surfaces once an upstream change breaks the hidden assumption. The semantic intent engine catches it before that happens, by comparing what the AI did against what it was specified to do.
Semantic AST
Parse the generated SQL past syntax, into meaning. Every column resolved to its role — metric, dimension, filter, grain. Every entity resolved to its canonical form. Implicit assumptions surfaced: time zone, fiscal calendar, currency, null handling.
Intent specification
Compare the semantic AST against what your data team authored for that question — the approved metric, grain, entity, filters, and join path. The output is a structural diff with typed categories: wrong_metric, wrong_grain, missing_required_filter, non_canonical_join_path, stale_metadata_used.
Structured report
Not a pass/fail boolean. A report an analytics engineer can act on and a compliance officer can sign: which parts of the intent matched, which didn't, and exactly why — down to the specific metric that was substituted.
§ 04 — Not another observability dashboard
Monitoring tells you a number looks unusual. Xemeta tells you why it's wrong.
Anomaly detection learns a baseline from historical query logs and flags what deviates from it. That's a different engine, solving a different problem, for a different buyer — which is why it doesn't compete with what Xemeta does.
| Dimension | Monitoring platforms | Xemeta |
|---|---|---|
| Validation paradigm | Statistical anomaly detection | Deterministic specification comparison |
| Source of truth | Learned baseline from historical data | Human-curated intent specification |
| Input | Streaming query logs, lineage data | Generated SQL + intent spec + semantic model |
| Output | Alerts, anomaly scores, lineage diagrams | Typed failure categories, semantic diffs, signed attestations |
| Failure mode caught | “This number is statistically unusual” | “This number was derived from the wrong metric” |
| Buyer | Data ops, SRE | Analytics engineering, compliance, internal audit |
§ 05 — The platform
Eight modules. Two do the differentiating.
Everything routes toward the same goal: a certified, evidence-backed answer to “can we trust what the AI told us?”
Test Studio
Author certified question test cases: business question, intent spec, reference SQL, tolerance rules, required entities, metrics and filters, certification status.
Regression Runner
Manual, scheduled, or change-triggered runs against connected AI analytics platforms. CI/CD integration, environment and version comparison.
Semantic Coverage Explorer
Surfaces the gaps in your semantic layer before they cause a wrong answer: missing synonyms, ambiguous terms, missing join paths, duplicate definitions.
Answer Comparator & Semantic Intent Engine
COREFrom exact match and numeric tolerance up through full semantic AST comparison. The differentiated component — and the reason a plausible answer can't pass as a correct one.
Cross-Platform Benchmarking
Run identical certified questions against multiple AI analytics systems. Compare consistency, latency, failure rate, and domain-specific reliability.
Reliability Dashboard
Reliability score by domain, regression history, semantic coverage, and release confidence — for leadership and engineering.
Audit Evidence
Every run retained: prompt, generated SQL, result, semantic model version, pass/fail reason, and the semantic intent diff.
Attestation Generator
CORESigned certification documents for compliance, audit, and model risk management — the artifact a Chief Risk Officer can actually put their name on.
§ 06 — Built for regulated enterprise
The conversation we keep having.
Representative of what we hear from data platform and compliance leaders once AI analytics moves past pilot.
“We are enabling natural-language analytics, but we need a repeatable way to prove these answers are accurate before executives and business users rely on them.”
Head of Data Platform“Our model risk function is asking how we validate AI-generated analytical outputs, and our current answer of 'spot checks' won't survive an audit.”
Chief Compliance Officer“We are running Snowflake Intelligence in one domain and Databricks Genie in another, and we have no way to know which is producing better answers for similar questions.”
Enterprise Data Architect“A semantic model change broke three previously working questions, and we only found out when a business user complained.”
Analytics Engineering LeadSigns a team is ready
- Snowflake CoWork or Cortex Analyst in active use beyond pilot
- AI analytics producing answers for non-data users in at least one domain
- At least one incident where AI gave a plausible-but-wrong answer
- Compliance, internal audit, or model risk asking how outputs are validated
§ 07 — The Xemeta index
A public, quarterly scoreboard for enterprise AI analytics.
The same certified questions, run against Snowflake CoWork, Cortex Analyst, Databricks Genie, and internal NLQ tools — published as a standing, cross-platform comparison instead of a vendor's own benchmark.
Answer consistency and SQL consistency across platforms, per domain.
Latency and failure rate under identical certified question sets.
Semantic adherence — not just “did it answer,” but “did it answer right.”
Platform-specific weaknesses by industry vertical, updated quarterly.
§ 08 — Case studies
Published as design-partner pilots complete.
Xemeta is pre-general-availability — there are no completed engagements yet. Case study rights are part of every design-partner pilot, and the first ones publish here as they wrap. In the meantime, here's what the deliverable itself looks like.
SAMPLE DELIVERABLE
Attestation Report — Wealth Management, Q1 Revenue
The same example used throughout this page, rendered as it actually looks: business question, intent diff, typed failure categories, and the evidence trail a compliance reviewer signs off on.
1 PAGE · 73% MATCH · WRONG_METRIC · MISSING_REQUIRED_FILTER
§ 09 — Design partners
Recruiting three design partners in financial services.
A free, 90-day pilot in exchange for product feedback and case study rights. We're looking for wealth management firms, regional banks, or insurance carriers already running Snowflake Cortex Analyst.