Building AI that earns its place in production.

Problem. Pure vector RAG kept failing on relational, multi-hop questions. Similarity search alone has no sense of how facts connect.

Approach. Knowledge graph traversal fused with vector search and reranking, so answers draw on relationships as well as resemblance.

Result. Grounded answers to questions plain similarity search cannot touch.

Problem. AI features passed their demos, then degraded quietly in production. Nothing stood between a regression and the users.

Approach. Golden sets, groundedness and faithfulness checks, and LLM observability wired into the release pipeline.

Result. Every AI change now clears a measured quality bar before it ships.

Problem. Batch-era ingestion pipeline couldn't feed near-real-time analytics.

Approach. Re-architected ingestion with streaming primitives, autoscaling and backpressure-aware design.

Result. 400x throughput improvement, same team, lower operational cost.

Problem. Point-to-point integrations collapsing under enterprise scale.

Approach. Cloud-native event backbone: schema governance, replay, multi-region resilience.

Result. A platform processing 100 trillion messages that downstream AI systems rely on today.