Services

We advise executive leadership on which AI bets to place first, how to layer services over a proven data platform, and how to sequence roadmap investments from $50M toward $100M+ ARR without overloading engineering or diluting product focus.

• Executive roadmap sequencing
• AI investment prioritization
• Go-to-market alignment for AI features
• Team and capability planning

We design connector strategies and intelligent AI context layers that let enterprise customers query historical data in the warehouse (e.g. Databricks, Snowflake, BigQuery, Athena) or lake (e.g. Iceberg, Delta Lake, Parquet) of their choice — with the governance, access control, and latency expectations that production workloads demand.

• Connector strategy and architecture
• AI context layer design
• Warehouse and lake integration patterns
• Governance and access-control models

We build inference routing and observability platforms that log model responses in OpenTelemetry format, benchmark commercial and open-source models, and route traffic based on accuracy, cost, and latency requirements.

• Model benchmarking and selection
• OpenTelemetry response logging
• Real-time cost and latency telemetry
• Dynamic routing by workload

We bring semantic search to production enterprise databases without requiring a standalone vector store, deploying native vector indexes inside Postgres, Oracle, and BigQuery. We also architect MCP servers that enable agentic, self-service data pipeline creation.

• Native vector search (HNSW/IVF) in Postgres, Oracle, BigQuery
• Call-center and support AI enablement
• MCP server architecture
• Agentic data pipeline design