Document Intelligence & On-Device RAG Engine
Local Multi-Format Document Retrieval with Cross-Encoder Re-Ranking
Upload corporate PDFs, technical manuals, policy guidelines, and spreadsheets. The avatar answers questions grounded strictly in your documents, processing everything locally with zero cloud data leakage.
Sub-Millisecond Retrieval Stack with Cross-Encoder Precision
DIHUAVA's Document Intelligence engine provides a complete retrieval-augmented generation (RAG) stack running entirely on local device hardware. Documents are chunked and embedded locally, retrieved via vector similarity search, and then re-ranked by an on-device cross-encoder before the answer is generated.
The cross-encoder re-ranker solves the critical accuracy flaw of standard vector search. It evaluates passage relevance in context, ensuring cross-language questions (e.g. asking a question in Hindi about an English PDF manual) retrieve exact, truthful answers. If a question is not covered in your documents, our grounding guard ensures the avatar politely declines to answer rather than hallucinating details.
Engineering Standards
- 100% On-Device GPU Processing (Zero Cloud Latency)
- Deterministic Low-Latency Lip-Sync Precision
- Air-Gapped Data Security Designed for GDPR/HIPAA Privacy Alignment
- 24/7 Continuous Commercial Hardware Reliability
Feature Demonstration Videos
Select a demonstration below to inspect local-first execution.
Drag & Drop PDF Upload to Instant Grounded Answer
Duration: 0:40Drag a technical PDF manual into the dashboard, watch local vector indexing, and ask a question answered strictly from the PDF.
Micro-Features & Engineering Detail
100% Air-Gapped Local Search
Embedding, vector search, cross-encoder re-ranking, and answer generation are executed locally. No document API call ever leaves your building.
Cross-Encoder Re-Ranking Engine
Two-stage retrieval architecture (find, then judge) delivers materially higher factual accuracy, especially for cross-language queries.
Cross-Language Document Querying
Ask questions in Hindi, Arabic, or Spanish and receive grounded answers pulled directly from your English source PDFs.
Post-Generation Grounding Guard
Answers are verified after generation: product IDs, prices, and policy terms must trace back to retrieved source text or they are suppressed.
Time-Boxed Answer-Time Ceiling
Retrieval is strictly time-boxed so complex queries degrade gracefully to a short, honest response instead of creating a long pause.
Vocabulary Feedback Loop
Mines technical terms, acronyms, and brand names from your documents into the ASR speech recogniser so spoken queries are heard correctly.
Isolated Per-Persona Knowledge
Each avatar persona maintains its own isolated document vector store—preventing cross-department data exposure.
Multi-Format Local Processing
Native support for Markdown (.md), plain text (.txt), Word documents (.docx), and enterprise PDFs with automatic hash deduplication.
How It Works End-to-End
Drag & Drop Documents
Upload enterprise PDFs, policy documents, or spreadsheets into your persona dashboard.
Local Chunking & Vector Indexing
The device chunks, embeds, and indexes the files locally in 10–60 seconds, mining custom terminology into the ASR speech model.
Two-Stage Vector Search & Re-Ranking
When a visitor asks a question, vector search retrieves candidate passages, and the cross-encoder re-ranker selects the most accurate source context.
Grounded On-Device Response
The local LLM synthesizes a natural answer, verified by the grounding guard to ensure zero hallucination before the avatar speaks.
Input Requirements Checklist
Primary Industry Applications
Frequently Asked Questions
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