RAG · FastAPI · OpenAI · pgvector · Postgres
AI you can actually trust in production
A raw chatbot invents answers. We build retrieval-augmented (RAG) assistants that retrieve the relevant facts from your content first, then answer from those — accurate, on-topic and traceable.
What we build
- Ingestion pipelines that chunk and embed your docs, courses and data
- Retrieval on Postgres/pgvector, with guardrails to stay on-topic
- FastAPI + OpenAI answer generation, embedded in your app as a chat panel
- Monitoring so you can see what was asked and answered
Who it's for
Products, platforms and support teams that want instant, reliable answers from their own knowledge base — without hallucinations.
How we work
We proved this pattern on our AI-tutor demo inside Pragyanta. The same approach drops into any product — see Custom Web Platforms.
How We Deliver
Discovery
We map your goals, users, workflows, integrations, and technical requirements before writing a single line of code.
Solution Design
We define architecture, user flows, data models, integrations, and delivery boundaries before build work accelerates.
Development
Sprint-based build with progress updates, code reviews, and continuous testing.
Launch & Improve
Go-live support, monitoring, operational handover, and iteration once real users begin using the system.
Related Resources
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