India AI Impact Summit 2026

Your AI.
Your Data.
Your Device.

Session 1 — Running powerful AI models locally on AI PCs for absolute privacy and zero cloud costs.

0
Data sent to cloud
₹0
Per token cost
<2s
First response
Local AI Processing
The Problem

Every prompt you type
leaves your device.

  • ChatGPT, Gemini, Claude — all send your prompts to remote servers for processing.
  • Your data is stored, logged, and often used to train their models.
  • Confidential docs, PII, trade secrets — all at risk.
Data flowing to remote servers
The Solution

Bring the AI
to the data.

Instead of sending sensitive data to a cloud, run the exact same powerful models right here on your AI PC. Completely offline. Completely private.

  • 100% Offline — No internet required after setup.
  • Zero Cost — No API fees. Ever.
  • Instant — No network latency. No queues.
CPU vs GPU vs NPU - Local AI Processing
The Tool

Intel AI Playground

One app. Every AI capability. Running entirely on your machine.

Local Chat

Chat with Llama 3, Phi-3, Mistral — all running on your CPU/GPU/NPU.

Image Generation

Stable Diffusion XL Turbo. Create photos, art, and designs locally.

RAG (Chat with PDFs)

Upload any document. Ask questions. Get cited answers grounded in your data.

Model Hub

One-click download for 20+ optimized models. No terminal needed.

System Prompts

Customizable personas. Make the AI behave as analyst, tutor, or coder.

MCP / Agents

Connect to external tools and APIs. Build intelligent agents.

Core Concept

What is RAG?

Retrieval-Augmented Generation.

Without RAG, the AI makes up answers (hallucination). With RAG, the AI reads your specific document before answering — like an open-book exam.

Key Insight: RAG doesn't change the model. It gives the model better context to work with.
Your PDF
Vectorize
Retrieve
Answer

Your PDF is split into chunks → converted to vectors → relevant chunks are retrieved → fed to the LLM as context → grounded answer.

Live Mission

The ₹500Cr
Government Tender.

You are a Senior Bureaucrat. A 500-page confidential RFP for a Smart City project just landed on your desk.

⚠️ Constraint: This is classified. You cannot upload it to ChatGPT. You have 15 minutes.
Confidential
Project "Neo-Bangalore"
Smart City Infrastructure RFP • 500 Pages
Step 1 of 3

Ingest the Document

Feed the classified PDF into AI Playground's local RAG engine. The NPU will convert every page into searchable vectors.

// In AI Playground
1. Click "Add Files"
2. Select "Tender_Document.pdf"
3. Wait for the green bar → "Vectorization Complete"
// The NPU just converted 500 pages into math.
Drop PDF Here
Step 2 of 3

The Inquiry

Ask the critical questions. Notice how the AI cites specific pages.

// Prompt 1: Executive Summary
"Summarize the key deliverables for the IoT
infrastructure. What are the penalty clauses?"

// Prompt 2: Technical Requirements
"List all mandatory sensor specifications.
Include power and connectivity requirements."
💡 Pro Tip: Click the citation numbers [1][2] to verify the source text!
What are the penalty clauses for delay?
According to Section 8.3 [Page 142], delays exceeding 90 days incur a penalty of ₹2.5L per day, capped at 10% of total contract value...
Step 3 of 3

The Compliance Trap

Every tender hides risk. Find the clause that could disqualify a bid.

// The Critical Question
"Does this tender require data servers to
be located physically within India?
Quote the exact clause number and text."
// Follow-up: Financial Risk
"Is there a force majeure clause?
What happens if a vendor goes bankrupt mid-project?"
RISK IDENTIFIED
Data Localization Clause Found
Mission Complete

Zero data leaked.
15 minutes. Done.

You just analyzed a classified 500-page government tender without a single byte leaving your laptop.

0
Bytes sent to cloud
3
Critical risks found
<15m
Total time taken
1 / 10