In banking and pharma, an AI giving you a “confident guess” isn’t just annoying—it’s a compliance nightmare. We’re past the chatbot hype phase. This session is about building AI systems that work in the real world, where regulations exist and mistakes have consequences.
RAG is what bridges the gap between impressive LLM demos and tools you can actually deploy in production.
🎯 The Real Problems We’re Tackling
Making AI show its work: It’s not enough to stop hallucinations. We need the AI to cite sources so you can verify the answer matters. Think “Wikipedia footnotes” but for enterprise decisions.
The language reality: Indian businesses don’t operate in textbook English. Documents mix multi regional language phrases, regional terms, and English in the same paragraph. We’ll dig into how semantic search handles this messy, real-world language mixing.
The unglamorous stuff that breaks projects: Getting a demo working is easy. Keeping it running six months later? That’s the hard part. We’re talking about:
∙ What happens when your knowledge base doubles in size
∙ How to not blow your budget on embedding costs
∙ Keeping response times reasonable when users are actually waiting
Why Google Cloud: Instead of duct-taping together five different tools, we’ll see how Vertex AI Search and Vector Search give you a stable foundation that doesn’t require a PhD to maintain.
💻 Building Something Useful: Compliance Automation
Forget generic chatbots. We’re building a pipeline for the stuff nobody wants to do manually but everyone needs done right—compliance reporting and VAPT documentation.
Here’s the workflow:
1. Retrieve: You’ve got a security finding. The system automatically pulls the relevant CERT-In guidelines, ISO standards, or NIST frameworks that apply to this specific issue.
2. Augment: It looks at your organization’s past assessments. “We saw this vulnerability before in Q2 2024—here’s what remediation actually worked.”
3. Generate: Produces a draft report that’s citation-heavy and audit-ready. Your security expert reviews it instead of writing from scratch. Hours saved, accuracy improved.
This is RAG solving the boring-but-critical work that eats up your team’s time.
Curious: Has anyone here tried building something like this and hit unexpected roadblocks? What was harder than you thought?
Looking forward to this! 🔥