Why Hire GKAI Studio
Accurate Answers
Ground responses in your data — reduce hallucinations on company knowledge.
Any Data Source
PDFs, Notion, Confluence, tickets, SQL databases, and APIs.
Secure Retrieval
Role-based access so users only retrieve documents they are allowed to see.
Performance Tuned
Chunking strategies, reranking, and caching for sub-second retrieval.
RAG Architecture We Build
Production RAG for US enterprises requires more than embedding PDFs. We engineer end-to-end retrieval pipelines.
- Document ingestion, chunking, and metadata tagging
- Vector stores: Pinecone, pgvector, MongoDB Atlas Vector Search
- Hybrid keyword + semantic search with reranking
- Evaluation frameworks for answer quality and citation accuracy
- Monitoring for drift, latency, and retrieval failures
Why US Teams Choose RAG
RAG turns generic LLMs into domain experts on your policies, products, and support history — without expensive fine-tuning on day one.
Our Development Process
Every engagement follows a proven four-phase delivery model — from discovery through production launch.
Discovery
Requirements workshop, technical audit, and architecture proposal aligned to US business goals.
Design
UX flows, API contracts, database schema, and sprint roadmap with clear milestones.
Build
Agile development with weekly demos, code reviews, and staging environments.
Launch
AWS deployment, monitoring, documentation, and post-launch support handoff.
Tech Stack
Case Studies & Resources
Frequently Asked Questions
Retrieval-Augmented Generation combines search with LLMs so answers cite your actual documents — critical for support, compliance, and internal knowledge.
Pinecone for managed scale, pgvector for Postgres-native stacks, and MongoDB Atlas for MERN teams already on MongoDB.
We build golden question sets, measure citation accuracy, track hallucination rates, and run regression tests before each release.
Yes. Data stays in your VPC or approved cloud region with encryption, access logs, and optional on-prem ingestion.
Typically 4–8 weeks for a focused knowledge base with ingestion, search UI, and LLM answer generation.