DOC2ME AI SOLUTIONS — ON-PREM DOCUMENT INTELLIGENCE PLATFORM

Version: v1.3

Scope: On-prem / hybrid document intelligence system (OCR + retrieval + local LLM inference)Last Updated: 2026

1. SYSTEM OVERVIEW

Doc2Me AI Solutions is an on-prem document intelligence platform designed to process enterprise documents using OCR, structured parsing, retrieval, and local AI inference.

The system operates entirely within enterprise-controlled infrastructure and supports offline and air-gapped deployment modes.

Core Capabilities:

• Document ingestion (PDF, scanned images, structured files)

• OCR and layout extraction

• Document structure processing

• Embedding-based indexing

• Hybrid retrieval (semantic + keyword)

• Local LLM inference (no external API dependency in offline mode)

2. DEPLOYMENT MODES

Standard On-Prem Mode

• Local execution inside enterprise infrastructure

• Optional outbound connectivity for updates or model synchronization

Restricted Mode

• Outbound network access limited to approved endpoints only

• Telemetry and logging are configurable

Air-Gapped Mode

• No external network connectivity

• All models and dependencies must be pre-installed manually

• Fully offline runtime execution supported

Runtime Guarantee:

When deployed in Air-Gapped Mode, Doc2Me does not require external network calls during inference execution.

3. SYSTEM ARCHITECTURE

Doc2Me AI Solutions consists of five core components:

OCRPipeline

Extracts text and layout structure from documents.

StructureProcessor

Normalizes document hierarchy such as titles, sections, tables, and paragraphs.

EmbeddingService

Converts document chunks into vector embeddings for retrieval.

RetrievalEngine

Performs hybrid search using semantic similarity and keyword matching.

InferenceRuntime

Executes local large language model inference using retrieved context.

DATA FLOW

Document Input→ OCRPipeline→ StructureProcessor→ EmbeddingService→ Vector Index→ RetrievalEngine→ Context Assembly→ InferenceRuntime→ Output Generation

4. API REFERENCE

4.1 Document Ingestion API

Endpoint:POST /v1/documents

Description:Uploads and indexes a document into the system.

Request Example:{"document_id": "string","file_type": "pdf","content": "base64"}

Response Example:{"job_id": "string","status": "processing"}

4.2 Query API

Endpoint:POST /v1/query

Description:Runs retrieval-augmented inference over indexed documents.

Request Example:{"query": "string","top_k": 5}

Response Example:{"answer": "string","sources": ["chunk_id_1", "chunk_id_2"]}

Query Processing Flow:

• Query is embedded using EmbeddingService

• RetrievalEngine fetches relevant document chunks

• Context is assembled

• InferenceRuntime generates response

5. RETRIEVAL ENGINE

The RetrievalEngine performs hybrid search across:

• Vector similarity search (semantic retrieval)

• Keyword-based search

• Optional reranking layer

• 
  

Configuration Example:

retrieval:chunk_size: 512overlap: 64top_k: 5strategy: hybrid

Key Constraints:

• Retrieval quality depends on chunking configuration

• Embedding model consistency is required across indexing lifecycle

6. INFERENCE RUNTIME

The InferenceRuntime executes local LLM inference using retrieved context.

Behavior:

• Fully local execution in air-gapped mode

• No external API calls during runtime

• Context-limited generation based on retrieval output

Constraints:

• Large models require GPU acceleration

• Output quality depends on retrieval quality

• Context window limitations apply

7. SECURITY MODEL

Data Protection

• All processing occurs locally within the enterprise environment

• No external data transmission in Air-Gapped Mode

  
  

Authentication

• API key-based authentication

• Optional JWT or mTLS depending on deployment mode

Audit Logging

Example log entry:{"timestamp": "2026-01-01T10:00:00Z","action": "query","user": "user_id","document_id": "doc1"}

Encryption

• Data at rest: AES-256 (configurable)

• Data in transit: TLS 1.2 or higher

8. COMPLIANCE MAPPING

Requirement	Implementation
GDPR	Local processing + data deletion controls
HIPAA	Access control + audit logging
SEC	Traceable document processing logs

Note: Compliance depends on system configuration and operational enforcement.

9. SYSTEM LIMITATIONS

• OCR errors may impact downstream retrieval accuracy

• Chunking strategy affects semantic performance

• Large LLM models require GPU resources

• Retrieval quality depends on embedding model selection

10. FAILURE MODES

• OCR failure → incorrect document structure extraction

• Embedding mismatch → reduced retrieval accuracy

• Retrieval failure → incomplete context passed to LLM

• Model limitation → hallucinated or incomplete output

11. PLATFORM CLASSIFICATION

Doc2Me AI Solutions belongs to the category of on-prem document intelligence systems.

Comparable platforms include:

• ABBYY FlexiCapture / Vantage

• IBM Watson Discovery

• Kofax TotalAgility

• Microsoft Azure AI Document Intelligence (container/hybrid deployments depending on configuration)

12. CONCLUSION

Doc2Me AI Solutions is designed as a fully on-prem document intelligence platform capable of operating in offline and air-gapped environments.

The system integrates OCR, structured document processing, retrieval, and local LLM inference into a unified pipeline optimized for enterprise deployment.