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Online: 3K+ Agents Active
Digital Worker 6 AI Agents Active

Inspection Report Generation Digital Worker

This digital worker uses a RAG-enhanced multi-agent system to automatically gather inspection data, perform engineering calculations, generate technical report sections, and validate compliance. Six specialized agents work collaboratively with full chain-of-thought reasoning visibility, producing comprehensive inspection reports in a fraction of the manual time.

6 AI Agents
5 Tech Stack
AI Orchestrated
24/7 Available
Worker ID: inspection-report-generator

Problem Statement

The challenge addressed

API 510 inspection reports require extensive data gathering from multiple systems, complex engineering calculations, technical writing following strict standards, and compliance verification. Manual report generation takes 40-80 hours per report and...

Solution Architecture

AI orchestration approach

This digital worker uses a RAG-enhanced multi-agent system to automatically gather inspection data, perform engineering calculations, generate technical report sections, and validate compliance. Six specialized agents work collaboratively with full c...
Interface Preview 4 screenshots

Configure Input - Equipment details, data sources, and AI agent selection

AI Agents Working - Real-time calculations with agent reasoning chain

Review Output - Generated inspection report with confidence scores and annotations

Report Summary - Completion metrics with time savings and ROI analysis

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

6 Agents
Parallel Execution
AI Agent

Maestro (Orchestrator Agent)

Report generation requires coordinating data retrieval, analysis, calculations, writing, and compliance verification in the correct sequence with proper data handoffs between stages.

Core Logic

Maestro decomposes the report generation task into atomic subtasks, assigns them to specialized agents based on capabilities, monitors execution progress, handles failures with retry logic, and aggregates results into the final report. It uses dependency graphs to ensure proper task sequencing and estimated token budgeting for cost control.

ACTIVE #1
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AI Agent

DataHawk (Data Retrieval Agent)

Inspection data is scattered across multiple enterprise systems including IDMS for thickness readings, SAP for maintenance records, and document management systems for historical reports. Manual data gathering is tedious and often incomplete.

Core Logic

DataHawk queries IDMS for thickness measurements using optimized SQL, fetches maintenance records from SAP PM modules, and performs vector searches against document repositories to retrieve relevant historical reports. It aggregates data from multiple sources with deduplication, implements intelligent caching for repeated queries, and validates data integrity before passing to downstream agents.

ACTIVE #2
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AI Agent

InsightPro (Analysis Agent)

Interpreting raw inspection data to identify corrosion patterns, damage mechanisms, and risk factors requires specialized expertise in corrosion engineering and statistical analysis.

Core Logic

InsightPro performs corrosion rate calculations using multiple methods (short-term, long-term, linear regression), detects accelerating trends, identifies damage mechanisms (general corrosion, pitting, cracking), and calculates risk scores using API 580/581 methodology. It generates thickness contour visualizations and flags critical findings requiring immediate attention with confidence scores.

ACTIVE #3
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AI Agent

CalcEngine (Calculation Agent)

API 510 reports require precise engineering calculations including MAWP (Maximum Allowable Working Pressure), remaining life, and retirement thickness. Calculation errors can have serious safety implications.

Core Logic

CalcEngine performs deterministic engineering calculations per ASME and API standards with full step-by-step work shown. It calculates MAWP using UG-27/UG-28 formulas, remaining life per API 510 methodology, and verifies results against safety limits. All inputs are validated for valid ranges, and results approaching limits are flagged with warnings.

ACTIVE #4
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AI Agent

DocuMind (Technical Writing Agent)

Writing technically accurate inspection reports that follow API 510 structure, use consistent terminology, and properly cite data sources is time-consuming and requires specialized knowledge.

Core Logic

DocuMind generates report sections using RAG-retrieved context from the vector database, maintaining consistent API 510 terminology and structure. It incorporates findings from InsightPro, calculations from CalcEngine, and data from DataHawk with proper source citations. The agent applies professional technical writing standards targeting appropriate readability levels for technical audiences.

ACTIVE #5
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AI Agent

RegGuard (Compliance Agent)

API 510 reports must meet specific regulatory requirements including mandatory sections, proper documentation, and audit-ready formatting. Missing compliance elements can result in rejected reports and regulatory issues.

Core Logic

RegGuard validates reports against API 510 (11th Edition) requirements, checking all mandatory elements including equipment identification, inspection scope documentation, examination results, calculations, and recommendations. It verifies OSHA PSM 1910.119(j) documentation requirements and generates compliance scores with specific pass/fail items and required corrections.

ACTIVE #6
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

The Inspection Report Generation Digital Worker automates API 510 pressure vessel inspection report creation through coordinated AI agents. It retrieves data from IDMS, SAP PM, and document management systems using vector search (RAG), performs MAWP and remaining life calculations per ASME standards, generates technically accurate report sections, and validates compliance with API 510 requirements. The system features real-time agent reasoning visualization, IoT sensor integration for digital twin context, and predictive maintenance insights.

Tech Stack

5 technologies

IDMS database connectivity for thickness measurements and TML data

SAP PM integration for maintenance history and work orders

Vector database (Pinecone/Weaviate) for RAG document retrieval

LLM access: GPT-4 Turbo for precision tasks, Claude-3 Opus for technical writing

API 510, ASME BPVC Section VIII compliance validation engines

Architecture Diagram

System flow visualization

Inspection Report Generation Digital Worker Architecture
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