Vector Databases
Pinecone, Weaviate, and pgvector infrastructure for high-performance semantic search and embeddings.
Discovery → Design → Build → Test → Deploy / Support
A disciplined cadence that keeps stakeholders aligned and shipping predictable.
- Step 1Discovery
Vector budget, latency SLOs, scale.
- Step 2Design
Embedding model, index type, sharding.
- Step 3Build
Ingestion, retrieval, hybrid scoring.
- Step 4Test
Recall, p95 latency, cost per query.
- Step 5Deploy / Support
Re-embed, autoscaling, cost optimization.
Tooling we ship with
Battle-tested frameworks, models, and platforms — chosen for outcomes, not fashion.
What you'll get out of an engagement
Predictable delivery, measurable outcomes, and a system your team can own.
Production-grade architecture from day one
Senior engineering leadership embedded in your team
Evaluation harnesses and observability baked in
Knowledge transfer, runbooks, enablement
Common questions
Related services
AI/ML Solutions
Custom machine learning models, predictive analytics, computer vision, and natural language processing tailored to your business.
Intelligent Chatbots
OpenAI-powered conversational assistants and virtual agents that integrate with your tools and brand.
RAG Systems
Retrieval-Augmented Generation over your private knowledge bases — accurate, sourced, and current.
Ready to ship something users love?
Tell us what you’re building. We’ll bring a senior team to the kickoff call.