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 Guarantee
- β100% On-Device GPU Processing (Zero Cloud Ping)
- βDeterministic Low-Latency Lip-Sync
- βAir-Gapped Data Security Compliant with GDPR/HIPAA
- β24/7 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 storeβpreventing 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 10β60 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
Do our confidential corporate PDFs get sent to external cloud servers?
No. All document parsing, vector embedding, cross-encoder re-ranking, and response generation occur 100% locally on your kiosk hardware.
What happens if a visitor asks a question that isn't in our documents?
Our post-generation grounding guard detects that no source text exists and instructs the avatar to politely decline rather than inventing false answers.
Can a visitor ask in Spanish if our documents are written in English?
Yes! The cross-encoder re-ranker understands cross-language semantic relationships, retrieving relevant English passages and answering fluently in Spanish.
How fast is document indexing?
Document indexing typically takes 10 to 60 seconds per file, depending on document length and local GPU processing power.
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