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RAG Agent โ Verifiable Document Intelligence
Ingest, index, and query complex enterprise files (PDF, DOCX, CSV, TXT, Markdown, and Code) with zero hallucinations. Powered by stateful LangGraph agent orchestration, hybrid vector retrieval, and verifiable citations pinned to source database chunks.
Zero Hallucinations
Strictly grounded in validated document chunks with verifiable citation badges.
LangGraph Agent
Stateful multi-turn reasoning, dynamic query decomposition, and autonomous tools.
Isolated Workspaces
Role-based access control (RBAC), room multi-tenancy, and persistent context.
Enterprise Privacy
AES-256 encrypted vector embeddings at rest and TLS 1.3 streaming security.
Industry Briefing: AI Breakthroughs, PropTech Markets & Security Defense
Autonomous Agentic RAG & Reasoning Models
Enterprise workflows are transitioning from static vector similarity searches to multi-agent LangGraph architectures that autonomously verify citations and self-correct.
Predictive Underwriting & Automated Lease Audits
Commercial real estate firms are accelerating deal velocity by using AI agents to parse 200+ page lease agreements, rent rolls, and zoning covenants in seconds.
Securing Vector Stores & Prompt Injections
OWASP Top 10 for LLMs identifies indirect prompt injection, vector data poisoning, and unauthorized retrieval leakage as paramount threats to enterprise generative AI.
- 1. Passkeys & MFA: Mandate hardware FIDO2 or authenticator apps; eliminate SMS OTPs to block credential phishing.
- 2. Zero-Trust & RBAC: Segment workspace rooms so sensitive documents are never indexed into general pools.
- 3. Encryption at Rest & Transit: Enforce AES-256 for vector embeddings and TLS 1.3 for all streaming SSE channels.
- 4. Prompt Injection Guardrails: Sanitize all retrieval inputs and scrub PII or secrets before LLM synthesis.
- 5. Immutable Backups & Audit Logs: Maintain tamper-proof revision trails and rotate API keys regularly.