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xAQUA

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๐Ÿง  AI & ML

The model is not the problem. The data underneath it is.

Every enterprise AI failure traces back to the same thing: models trained or prompted against ungoverned data they didn't understand. xAQUA gives every model, agent, and copilot a grounded semantic layer โ€” so AI answers come back with lineage, trust scores, and reasoning you can defend.

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Use Case ยท 04
๐Ÿง 
The AI Reality Check

80% of AI projects don't ship. The model isn't why.

POCs demo beautifully. Production is a different story. The reason is almost never the model โ€” it's the ungoverned data underneath.

๐ŸŒซ๏ธ
Hallucinations on real data
Your LLM looks brilliant in the demo. On day-one in production it confidently invents revenue numbers, misnames products, and confuses two customers with the same first name.
~30%
hallucination rate on ungrounded enterprise data
๐Ÿงช
Features never reach production
Data scientists build features in notebooks against snapshots that drift. Engineering teams can't reproduce them. The feature ships once and decays silently.
80%+
of POCs that never reach production
๐Ÿ”’
PII leaks into prompts
Someone ships a chatbot. It pulls full customer records into the LLM context. Now Social Security Numbers are in someone else's training data. Now you're on the front page.
Real
and increasingly common breach pattern
How xAQUA Fixes It

Grounded models. Auditable agents. Trustable answers.

xAQUA puts the semantic layer underneath every AI call โ€” so models work with governed definitions, masked PII, resolved identities, and full lineage on every output.

01

Ground every LLM call

The LLM Gateway intercepts every model call, enriches with semantic context, masks sensitive fields via SenseMask, and routes through the right model for the task. Hallucinations drop. Defensibility goes up.

LLM GatewaySenseMaskSemantic Context
02

Feature store that doesn't drift

ClickML builds, trains, and serves features against the same governed semantic layer your dashboards use. Same definitions. Same lineage. Same versioning. No more notebook-to-production gap.

ClickMLFeature StoreMLOps
03

Retrieval grounded in your data

RAGConvo pipelines feed LLMs from your governed knowledge base โ€” catalogue, documents, structured data โ€” with role-based masking and lineage on every retrieved fact. Answers are accurate and auditable.

RAGConvoVector SearchHybrid Retrieval
04

Cezu routes intent safely

Every user question hits Cezu first. Cezu classifies intent across 23 routes, picks the right agent (analyst ยท scientist ยท governance), and never exposes raw asset IDs to the LLM. PII never travels.

CezuIntent RouterLLM Gateway
AI Architecture Layers

Every model call. Grounded, masked, audited.

User intent enters Cezu. Cezu routes to agents. Agents call the LLM Gateway. The Gateway grounds in the semantic layer, masks PII, and logs lineage. Outputs ship with Trust Score.

USER ยท INTENT CEZU ยท INTENT ROUTER AGENTS & MODELS LLM GATEWAY ยท MASK + GROUND SEMANTIC GROUND TRUTH ๐Ÿ’ฌ User asks: "What's our member churn risk for Q3?" ๐Ÿ’ฌ Cezu ยท Universal AI Concierge classifies intent ยท 23-route taxonomy ยท selects agent analytics_query prediction_request document_search governance_check ...18 more ๐Ÿ”ฌ Data Scientist ClickML ยท RAGConvo predict ยท ground ยท retrieve ๐Ÿ“Š Data Analyst Athyna ยท ConverseSQL queries ยท explanations ๐Ÿ“ Doc Intelligence DocIQ ยท RAGConvo extract ยท summarize ๐Ÿ›ก๏ธ Governance Qualix ยท SenseMask quality ยท policy ยท audit ๐Ÿ” LLM GATEWAY ยท governs every model call ๐Ÿšซ PII Masking ๐Ÿ“ Semantic Context ๐Ÿงญ Model Routing ๐Ÿ“œ Audit Log + Lineage SHARED SEMANTIC LAYER ยท SOURCE OF TRUTH SemantIQ ยท Glossary ยท Bindings ยท Lineage ยท Quality ยท Identity Returns Trust: 94% โฌ† Every model call is masked, grounded, routed, and logged โ€” before the LLM ever sees the data.
Cezu intent routerAI agents & ML modelsLLM Gateway ยท masking & policySemantic ground truth
🤝
xAQUA augments your AI teams, not replaces them. Data scientists stop wrestling with snapshots and drift โ€” and ship models grounded in the same semantic truth the rest of the business uses.
<5%
Hallucination Rate
On governed enterprise queries
100%
Auditable Outputs
Lineage and reasoning on every answer
Zero
PII Leakage
Masking at the Gateway, never the prompt
Days โ†’ Hours
Model Time-to-Prod
Feature store eliminates notebook gap
Customer Story ยท In Production
A government deployment delivered a grounded AI assistant for 30,000+ sensitive documents โ€” in a fully air-gapped environment.
The agency needed AI search across decades of policy documents โ€” but couldn't allow data to leave the perimeter, couldn't accept hallucination on legal text, and couldn't expose PII in retrieved passages. xAQUA's LLM Gateway, RAGConvo retrieval, and SenseMask masking ran end-to-end on-premises, with cryptographic lineage on every answer. Officers got answers in seconds with full traceability.
30,000+
Documents searchable in air-gap
Zero
Data leaves the perimeter
100%
Answers carry lineage
Related Across the Platform

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Stop demo-ing AI that doesn't ship.
Ship AI that you can defend.

See how xAQUA grounds every model, agent, and copilot โ€” with lineage, trust, and zero PII leakage โ€” in a 30-minute demo on your stack.

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