The Clarista substrate connects data engineering, governance, and AI into a single governed system of intelligence.


Faster & smarter with One-Fabric:
Explore before you build with a zero-copy data fabric across medallion stages. One semantic model serves BI/ML/GenAI. Materialize only when needed to cut cost. Platform neutral: cloud providers, lake-houses, SaaS apps and on-premises sources.
Unify data across clouds, on-prem, and SaaS with direct reads (federation). When APIs require, run event or scheduled syncs. Unify cross-source data in minutes; materialize selectively for performance.
Configure data work-flows with drag & drop transformers to deliver cloud lake-houses (Snowflake, Databricks etc.). Accelerate complex transformations with AI Copilot . Optimize medallion steps and reduce materialization. Version control every work-flow.
Celery watchers detect changes from databases, APIs, and files; capture inserts, updates, deletes. Ordered retries and lineage tracking, health dashboards, and alerts keep pipelines trustworthy.
Ingest PDFs, statements, and contracts; extract tables and fields. Auto-classify, mask PII, link to mastered entities, publish governed Smart Tables for document comparison, and automate RAG pipelines for AI.
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Turn governance into business
value:
AI automates classification,
data quality, and policies to activate
a business-ready catalog and
semantic views raising trust,
accelerating access, and proving
compliance without heavy manual
effort.
AI Copilot assisted unified catalog and glossary enable governed, business-ready semantic views; link terms to technical columns and metrics; explore instantly across sources without extra engineering.
Auto-profiling and GenAI-authored rules at business/technical levels; scheduled checks, notifications, role-based dashboards; enforce SLAs with trend tracking.
GenAI-driven classification, tagging, masking; RBAC/ABAC with row/column filters and time windows; audit-ready enforcement across engines to satisfy CISO and compliance requirements.
Link records by common IDs and ML text matching; apply survivorship rules and confidence; steward review; publish mastered entities downstream for analytics and operations.
