| Key features | - Step-level observability with traces showing status, latency, credits, and governance for every step
- Natural-language governance policies with flag and block thresholds
- Model portability: swap 200+ models at any time without code changes
- Automatic content ingestion and vector database indexing for RAG
- Persistent memory banks for conversation context
- Cost tracking per model, agent, and step with credit-based pricing
- Structured output validation ensuring quality responses
- HTTP endpoints for every agent with SDKs for Python, JavaScript, Go, and C#
- ML-based prompt scanner blocking injection attacks in real time
- Evaluation framework with evals that run locally, in CI, and in production
| - Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
- Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
- Provides Web-UI, MCP server and REST APIs for accessing semantic context
- Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
- Role-level access controls and data classifications including PII, PHI and SPII
- Only reads metadata without accessing or moving actual data from systems
- Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
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| What makes it different | - All production LLM features unified in one runtime with no orchestration stack to assemble
- Local development environment matches production exactly for debugging and verification
- Swap between 200+ models without changing code or workflow logic
- Governance and compliance built-in with real-time prompt injection blocking and policy evaluation
- Transparent credit-based pricing where all operations consume predictable, tracked credits
| - Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
- Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
- Ensures consistent answers across all agents and teams using the same definitions and business logic
- Provides complete traceability and auditability of AI decisions with documented context and ownership
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Seclai
Kanlet