Build retrieval-augmented generation that answers from your docs, tickets, and product knowledge, with citations and safe fallbacks when context is missing.
RAG is the practical cure for many hallucination problems: retrieve first, then generate. Done poorly, it still invents. Done well, it cites sources and admits when context is missing.
I build RAG pipelines for SaaS and internal tools: ingest, chunk, embed, retrieve, generate, and cite. Evaluation with real questions is part of delivery, not an afterthought.
Based in Ahmedabad, I wire RAG into Laravel and Node products, often with FastAPI workers when Python tooling is the better fit for embeddings and retrieval jobs.
If users need a chat surface, continue to AI Chatbot Development. If you need hire capacity, continue to Hire AI Developer. If the work is an embed in your environment, continue to the AI-first Forward Deployed Engineer page.
What I use to ship this kind of AI work.
What you get when we build this AI workflow for your product.
Turn docs, tickets, and knowledge bases into chunks sized for retrieval quality, not vanity volume.
Store vectors in pgvector, Pinecone, or Chroma based on stage, ops comfort, and scale.
Return weak-hit behavior: ask clarifying questions or escalate instead of inventing answers.
Generate only from retrieved context and show citations reviewers can open.
Test with real questions from your team and measure groundedness, not just fluency.
Expose RAG as chat, API, or sidecar service inside Laravel, Node, or FastAPI products.
Project, MVP sprint, or dedicated hire depending on roadmap urgency.
Guides connected to production AI delivery.
A practical, 1,700+ word engineering rollout strategy for introducing AI features to an existing, revenue-generating Laravel SaaS application — covering risk classification, feature flagging with Laravel Pennant, shadow mode testing, human review gates, and production monitoring.
A comprehensive, 1,700+ word architectural decision guide comparing FastAPI and Laravel for AI backends — evaluating hosted API orchestration, local ML ecosystem access, database integration, async performance, and hybrid architectures.
What AI Developer Ahmedabad means in practice: local collaboration for Gujarat/India teams plus remote delivery habits for global SaaS products.
Common questions about scope, architecture, and how to start.