Blockchain Audit Trail

Implement data mesh architecture: domain-owned data products, federated governance, self-service infrastructure, and automated discoverability. Decentralize data ownership.

• ✦Domain data product definition and ownership
• ✦Federated governance with global policies

End-to-end ML model lifecycle management: experiment tracking, model registry, CI/CD for ML, A/B testing, monitoring, and automated retraining.

• ✦Experiment tracking and comparison
• ✦Model registry with versioning and staging

Sub-second analytics on streaming data with pre-aggregation, materialized views, and real-time dashboards. ClickHouse, Druid, or Pinot managed service.

• ✦Sub-second query on billions of rows
• ✦Streaming ingestion (Kafka, Kinesis, Pub/Sub)

Enforce data contracts between producers and consumers. Schema registry, breaking change detection, compatibility modes, and automated documentation.

• ✦Centralized schema registry (Avro, Protobuf, JSON)
• ✦Breaking change detection and prevention

Push data from warehouse to operational systems (CRM, ad platforms, support tools). Activate warehouse data in tools your teams already use.

• ✦Push data from Snowflake/BigQuery to SaaS tools
• ✦Sync to Salesforce, HubSpot, Braze, Zendesk

Evaluate, compare, and benchmark LLMs: accuracy, latency, cost, safety, and fairness. A/B test models in production with automated evaluation pipelines.

• ✦Multi-model evaluation (OpenAI, Anthropic, Gemini, local)
• ✦Accuracy, latency, cost benchmarking

Monitor data pipelines for anomalies: volume, schema, freshness, distribution, and lineage. Catch data issues before they corrupt downstream models and decisions.

• ✦Volume and freshness anomaly detection
• ✦Schema change monitoring

AI-powered enterprise knowledge management: auto-organize documents, answer questions over your knowledge base, and surface relevant info in workflow tools.

• ✦Auto-organize and tag documents
• ✦Natural language Q&A over knowledge base