Enterprise Data Platforms & RAG Vector Lakes
Unify unstructured enterprise data into sub-50ms RAG vector databases (Pinecone, Qdrant, Milvus) and high-throughput real-time streaming data lakehouses (Apache Kafka, Snowflake, BigQuery).
Sub-50ms RAG Vector Databases
Dense-sparse hybrid index architectures (Pinecone, Qdrant, Milvus, pgvector) with Cohere Rerank v3 for instant, accurate semantic retrieval over billions of embeddings.
Real-Time Streaming & Debezium CDC
High-throughput event streaming via Apache Kafka and Debezium Change Data Capture (CDC), capturing database mutations with sub-100ms processing latencies.
Modern Lakehouse Data Pipelines
dbt and Spark ELT pipelines transforming raw enterprise data into clean, governed schemas across Snowflake, Google BigQuery, and Databricks.
Data Governance & Security
Column-level encryption, automated PII masking, SOC2 compliance audit logging, and RBAC data access policies built into every data pipeline.
Vector Lake & RAG Performance Benchmarks
| Vector Engine | Retrieval Latency | Recall Accuracy (NDCG@10) | Enterprise Scale Capacity |
|---|---|---|---|
| Pinecone Serverless | 18 ms P99 | 96.8% | > 1 Billion Vectors |
| Qdrant Enterprise Cloud | 12 ms P99 | 98.2% | > 500 Million Vectors |
| Milvus Distributed Cluster | 15 ms P99 | 97.5% | > 10 Billion Vectors |
Schedule a 1-on-1 AI Strategy Session
Pick a convenient time directly on our live calendar to discuss your custom AI architecture, dedicated engineering pods, or performance growth roadmap.
30-Min Deep Dive
Focused technical consultation via Google Meet with our Chief AI Architect.
Custom Architecture Blueprint
We'll evaluate your tech stack and map out a 50%–70% AI velocity plan.
Instant Calendar Confirmation
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