Enterprise RAG & Agentic AI
The infrastructure layer beneath RAG and agentic systems: vector stores, embedding pipelines, secure gateways, evaluation hooks, and cost control. You (or your app team) build the product. We make it production-grade.
How it works
The retrieval and agent infrastructure flow, from raw data to production APIs.
What we build
- Self-hosted vector databases (Qdrant, Weaviate, pgvector)
- Embedding & ingestion pipelines with CI/CD
- Secure API gateways, auth, and rate limiting
- Evaluation, logging, and observability hooks
- Deployment patterns for LangGraph / LlamaIndex-style stacks
Scope
We handle
- Vector DB & embedding pipeline infrastructure
- Gateway, auth, and observability
- Deployment & scaling patterns
- Cost and performance tuning
Better with a partner
- Prompt / agent application logic
- Product UX & frontend
Typical engagement
Infrastructure-first delivery. Your team owns the product layer while we make retrieval and agents production-grade.
- 01
Architecture review
Map data sources, retrieval patterns, latency and cost targets, and security boundaries.
- 02
Infra stand-up
Deploy vector store, embedding pipelines, gateways, and deployment patterns for your agent stack.
- 03
Production readiness
Logging, evaluation hooks, rate limits, and cost guardrails at the infrastructure layer.
- 04
Scale & tune
Optional retainer for index growth, re-embedding strategy, and performance optimization.
Ready to take your AI workloads to production?
Let's talk about your platform: cloud, hybrid, or edge. Start with a short, no-pressure conversation.