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Manufacturing

From process data to R&D documents - an AI agent layer atop an encrypted factory.

Predictive maintenance, process quality, materials R&D, and environmental compliance - delivered with only features verified against real code.

Overview

Factories already run MES, DCS, PLC, and digital twins. AICLUDE does not replace that OT stack - it rides on top of it as an agent layer covering unstructured data, regulatory docs, research knowledge, and a worker copilot.

Encrypted on-prem container image deployment, physical multi-tenancy, and 9 AI guards are built in from day one - so sensitive process data never leaves the internal network.

Key Capabilities

Graph N-hop Agentic RAG

Papers, patents, in-house experiments, and SOPs split by proposition via Graph N-hop Agentic RAG, retrieved via vector search.

Fortress Pipeline 8 stages

Input enrichment → intent understanding → pre-validation → execution planning → fast response → post-validation → auto-correction - hallucinations filtered, sources cited.

9 AI Guards + Fortress Pipeline

Guards against prompt injection, tool misuse, data leakage, RAG poisoning, chain, memory, DoS, output tampering, and supply chain.

On-premise encrypted container + AES-256-GCM

Deployed inside the factory network. PII encryption + search-while-encrypted index + physical multi-tenancy.

Native KO/EN/JA multilingual

Handles global patents and regulations through native prompts and UI - not a translator layer.

166+ Function Calling tools

Documents, image, DB query, browser, scheduler, data analysis - usable the moment AICLUDE is installed.

Case Stories

Self-contained Application Scenarios

Every case is shown in full: Pain, AICLUDE Apply, Scenario, Impact, and Tech: without collapse.

Case 01

Predictive maintenance + on-floor copilot

Customer Pain

  • Vibration, temperature, action history, and SOPs scattered across disconnected systems.
  • Operators waste time digging through paper or PDF manuals when anomalies hit.
  • On-prem security demands on the factory floor - data must never leave the network.

AICLUDE Apply

  1. 1Unify equipment time-series, image analysis, SOPs, and action history through Graph N-hop Agentic RAG.
  2. 2On anomalies, the hybrid execution engine retrieves similar cases and auto-generates an action guide.
  3. 3Operators query in natural language from mobile or tablet - "Why did Line 3 vibrate yesterday?" - instantly.
  4. 4On-prem encrypted container deployment blocks factory data from ever leaving the network.
  5. 59 AI guards + Fortress Pipeline pre-empt security incidents.

Scenario

Show Me
Input
Vibration sensor anomaly
  1. 01Execute
    alert triggered
  2. 02Retrieve
    Graph N-hop Agentic RAG retrieves similar pattern history
  3. 03Retrieve
    Match against past actions + OEM manuals
  4. 04Generate
    Risk scored + standard action guide generated
  5. 05Deliver
    Pushed to operator mobile
  6. 06Step
    action completed

Impact

  • Reduced equipment downtime.
  • Less time spent searching action manuals.
  • Factory data stays protected.

Tech

  • Graph N-hop Agentic RAG
  • Hybrid Execution
  • On-prem encrypted container
Case 02

Process-quality image analysis reports

Customer Pain

  • Vision detection (defects, cracks, contaminants) is disconnected from explanation and action recommendation.
  • QA staff manually interpret Vision results and write reports.
  • Image meta-analysis must be auto-formatted into in-house report templates.

AICLUDE Apply

  1. 1LLM explains image analysis output in natural language.
  2. 2In-house report templates trained via RAG → drafts auto-generated to spec.
  3. 3Multi-LLM router (OpenAI, Anthropic, Google, Bedrock, xAI) picks the optimal model per task.
  4. 48-stage Fortress Pipeline validation filters hallucinations.
  5. 5Audit Log captures the entire inspection and decision flow.

Scenario

Show Me
Input · QA staff
upload inspection image + "anomaly + action proposal"
  1. 01Analyze
    Image meta-analysis
  2. 02Step
    LLM explains the result in natural language
  3. 03Generate
    Draft written into in-house report template
  4. 04Validate
    Fortress Pipeline validates
  5. 05Step
    cites sources
  6. 06Audit
    Staff reviews and signs
  7. 07Audit
    logged in Audit Log

Impact

  • Faster report authoring.
  • More consistent decisions.
  • Audit-ready traceability.

Tech

  • Image Analysis
  • Multi-LLM Router
  • Fortress Pipeline 8 stages
  • Audit Log
  • RAG
Case 03

R&D document & patent Graph N-hop Agentic RAG

Customer Pain

  • Papers, drawings, patents, and experiment data scattered across personal stores.
  • Queries like "Material X grade, last 3 years, property A above threshold" are searched by hand.
  • Common across semiconductors, materials, and chemicals - unstructured R&D knowledge needs structuring.

AICLUDE Apply

  1. 1Papers, patents, and in-house experiment DBs split and learned via Graph N-hop Agentic RAG.
  2. 2Hybrid vector + keyword search.
  3. 3Natural-language query → retrieval → analysis → comparison table & report draft auto-generated.
  4. 4Native KO/EN/JA enables simultaneous search across global patents and papers.
  5. 5Encrypted container images keep core R&D data inside the company.

Scenario

Show Me
Input · Researcher
"Compare conditions with property A above 900 in the last 3 years"
  1. 01Retrieve
    Graph N-hop Agentic RAG retrieves relevant propositions
  2. 02Retrieve
    Vector search matches similar research
  3. 03Generate
    Comparison table auto-generated
  4. 04Generate
    Drafted into the in-house report template
  5. 05Validate
    Fortress Pipeline validates (sources cited)

Impact

  • Reduced time on R&D document work.
  • Research knowledge unified and reusable.
  • Simultaneous global patent coverage.

Tech

  • Graph N-hop Agentic RAG
  • Multi-LLM Router
  • Encrypted container images
  • Native multilingual
Case 04

Global patents & technical docs - multilingual

Customer Pain

  • Exporters and global R&D must cover 4 markets at once - FDA, EU MDR, NMPA, MFDS.
  • Manual competitor-patent monitoring - miss a filing, miss a business opportunity.
  • English, Chinese, Japanese sales decks and tech docs are outsourced.

AICLUDE Apply

  1. 1Regulatory source texts from all 4 jurisdictions learned together via Graph N-hop Agentic RAG.
  2. 2Connect competitor patent DBs for auto-monitoring and summary reports of new filings.
  3. 3Native KO/EN/JA + extended Chinese auto-generates multilingual drafts.
  4. 44 image providers (multiple image AI models) integrated to automate brochures and technical drawings.
  5. 5On-prem deployment of encrypted container images keeps proprietary tech and process data inside.

Scenario

Show Me
Input · Regulatory officer
"Product X - EU MDR registration tech-doc checklist"
  1. 01Retrieve
    RAG retrieval over EU MDR source
  2. 02Step
    required items listed
  3. 03Generate
    Reuse existing FDA report
  4. 04Generate
    CN/JA/EN drafts generated in parallel
  5. 05Audit
    Auto-dispatch of competitor patent monitoring reports configured

Impact

  • Coverage across 4 global markets.
  • Automated patent monitoring.
  • Faster multilingual sales-deck production.

Tech

  • Native multilingual
  • Graph N-hop Agentic RAG
  • 4 Image Providers
  • Encrypted container images

Apply AICLUDE to this industry

We shape each PoC around your data, security requirements, and operating flow.