Typical Challenge
Teams scattered wikis, drives, aur file stores mein answers dhundhne mein struggle karti hain. Naye hires ko runbooks dhundhne mein zyada waqt lagta hai aur outdated pages inconsistent procedures ka sabab ban sakte hain.
Tamam example solutions
Example Solution
RAG & AI-Powered Search
Representative use case
Illustrative scenario: internal team ya product surface Confluence, Notion, PDFs, aur file stores par scattered documentation par semantic search aur RAG hasil karti hai.
Typical industry: SaaSTypical delivery scope: Task Desk package ke zariye scoped
Hamara Approach
Internal documentation par vector embeddings ke sath semantic search. Natural-language query interface cited answers source links ke sath deta hai. Access patterns aur version metadata stale content flag karne mein madad karte hain.
Yeh Solution Kya Address Kar Sakta Hai
- Natural-language queries se internal documentation search karein
- Approved sources se grounded cited answers return karein
- Runbooks aur policies tak tez access se onboarding support karein
- Customer-facing apps ya internal tools mein semantic search embed karein
- PostgreSQL pgvector ya dedicated vector stores ke sath combine karein
Example Technology Stack
- OpenAI Embeddings
- PostgreSQL pgvector
- LangChain
- Qdrant
- Next.js