hackAccino

Problem- Over 900 million rural Indians face complex, unstructured administrative and legal documents (such as land records, microfinance loan agreements, and labor contracts). Often written in regional languages or dense administrative jargon, these documents leave citizens highly vulnerable to unfair agreements, hidden predatory terms, and devastating land disputes. Solution: AEGIS-India is an AI-powered Rural Document Intelligence & Legal Guidance platform designed to bring parity and transparency to everyone. By photographing a document and speaking a regional voice query (in Hindi, Kannada, or English), users trigger an adversarial multi-agent jury system that analyzes the document from all angles. How It Works: The architecture is orchestrated using a structured agent pipeline: 1. Intake Agent: Ingests the multimodal document photo and spoken audio to extract and classify core text. 2. Skeptic & Advocate Agents: Run simultaneously in an adversarial debate. The Skeptic aggressively hunts for hidden risks, unfair clauses, and predatory terms. The Advocate provides fair context, validating standard local practices. 3. Judge Agent: Weighs arguments from both debaters, assigns a structured severity rating (LOW to CRITICAL risk), and files a clear verdict. 4. Guide Agent: Translates the legal findings into a localized action-plan, plain regional language summaries, and nearby legal aid contact directories. Google AI Stack Integration: - Gemini 2.5/3.5 Flash: Serves as the high-speed backend reasoning engine, optimized in "Fast Mode" to deliver snatches of agent outputs in under 3 seconds per step for snappy, latency-free live presentation. - Multimodal Processing: Processes high-resolution document images and spoken regional dialects (Hindi, Kannada, English) without manual data entry. - Google Search Grounding: Under "Deep Deliberation Mode," agents verify arguments in real-time against verified databases and live regional statutes (such as the Karnataka Land Reforms Act). - Structured Outputs: Strictly enforces JSON schemas on model responses to ensure the vanilla HTML/CSS frontend parses lists, severities, and verdict details with maximum reliability.

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