Enterprise Document Intelligence
Deploys a 12-agent orchestrated RAG pipeline that ingests documents, performs semantic retrieval, analyzes content with specialized agents (market intelligence, compliance, supply chain), synthesizes insights with cross-reference validation, applies self-reflection for quality assurance, and generates executive summaries with citations and recommendations..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Enterprise Document Intelligence - Document corpus selection, analysis query input, and LLM model configuration panel
Multi-Agent Orchestration - Active agents dashboard with task metrics, agent communication, and reasoning logs
LLM Reasoning Chain - Chain-of-thought visualization with retrieval steps, token usage metrics, and cost tracking
Analysis Results - Executive summary with key findings, risk assessment, recommendations, and document citations
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Multi-agent document analysis requires precise coordination of task flow, resource allocation, and error recovery across diverse specialized agents.
Core Logic
Manages the execution DAG (Directed Acyclic Graph) for agent orchestration. Routes tasks to appropriate agents based on query analysis. Handles inter-agent data transfers and message passing. Manages state across workflow steps. Implements error recovery and retry logic. Aggregates final outputs from all contributing agents.
Strategic Planner Agent
Complex queries require decomposition into optimally sequenced sub-tasks with dependency awareness to maximize parallel execution and minimize latency.
Core Logic
Analyzes query complexity and required capabilities. Decomposes tasks into parallel workstreams with dependency mapping. Builds execution DAG with critical path identification. Estimates resource requirements and execution time. Hands off optimized execution plan to Orchestrator for agent coordination.
Retrieval Agent
Finding relevant information across large document corpora requires semantic understanding that goes beyond keyword matching while maintaining precision.
Core Logic
Generates query embeddings using text-embedding-3-large. Executes hybrid search combining vector similarity (cosine) with BM25 lexical matching. Applies cross-encoder reranking for precision. Uses MMR (Maximal Marginal Relevance) for diversity. Assembles optimized context windows within token limits. Reports retrieval metrics (chunks searched, similarity scores, retrieval time).
Analysis Agent
Extracting structured insights from unstructured document content requires entity recognition, relationship mapping, and pattern detection capabilities.
Core Logic
Performs named entity recognition (organizations, dates, monetary values, legal terms). Maps relationships between extracted entities across documents. Runs sentiment analysis on document sections. Executes pattern detection for recurring themes. Applies risk assessment models to identified clauses. Outputs structured entity graphs and risk scores.
Market Intelligence Agent
Document analysis lacks external context about market conditions, competitor activities, and industry trends that affect interpretation of findings.
Core Logic
Queries real-time market data APIs (Bloomberg, Reuters, Gartner). Analyzes competitor landscape and pricing dynamics. Detects market trends affecting document subject matter. Correlates document findings with current market conditions. Generates market context enrichment with actionable competitive intelligence.
Compliance Monitor Agent
Documents must be evaluated against complex regulatory frameworks (SOX, GDPR, HIPAA, PCI-DSS) which requires specialized knowledge and systematic checking.
Core Logic
Maps document clauses to regulatory requirement frameworks. Checks compliance status across multiple regulations simultaneously. Identifies violations with severity classification. Generates audit trails for compliance verification. Produces compliance scorecards with gap analysis and remediation recommendations.
Supply Chain Analyst Agent
Contract and vendor documents require evaluation of supply chain implications including vendor performance, procurement optimization, and risk exposure.
Core Logic
Analyzes vendor performance data from document context. Calculates vendor risk scores (single-source, geographic, financial, quality). Identifies procurement optimization opportunities (consolidation, negotiation, alternative sourcing). Generates supply chain risk assessments with mitigation strategies.
Synthesis Agent
Findings from multiple agents and document sources must be correlated and synthesized into coherent, actionable insights.
Core Logic
Fuses information from Analysis, Market Intelligence, Compliance, and Supply Chain agents. Correlates findings across multiple document sources. Invokes LLM (Claude 3.5 Sonnet) for insight synthesis with chain-of-thought reasoning. Generates structured recommendations based on correlated findings. Produces comprehensive key findings with evidence mapping.
Memory Agent
Each analysis session starts from scratch without leveraging insights from previous analyses, missing patterns and context that would improve results.
Core Logic
Maintains knowledge store with findings from past analyses. Retrieves relevant historical context based on query similarity. Detects recurring patterns across sessions (e.g., vendor issues flagged multiple times). Stores current findings for future contextual recall. Enables organizational learning from accumulated analysis history.
Validation Agent
AI-generated insights may contain errors, hallucinations, or claims not supported by source documents, undermining trust and accuracy.
Core Logic
Cross-references all claims against source document chunks. Verifies citations point to correct documents and page numbers. Runs hallucination detection model to identify unsupported claims. Checks consistency across findings. Generates validation reports with confidence scores and verification status for each claim.
Reflection Agent
Analysis quality varies and there is no systematic self-evaluation to identify improvement opportunities or confidence calibration issues.
Core Logic
Self-evaluates analysis quality across dimensions (completeness, accuracy, reasoning depth). Identifies potential blind spots and reasoning gaps. Runs confidence calibration to ensure scores reflect actual accuracy. Triggers iterative refinement for low-confidence claims. Makes autonomous decisions about whether additional analysis passes are needed.
Output Agent
Analysis results must be formatted appropriately for different audiences (executives vs. technical teams) and output formats (reports, dashboards, exports).
Core Logic
Generates executive summaries tailored for C-suite audiences. Structures technical details with full citation trails. Formats risk matrices and recommendation prioritization. Produces markdown reports, structured JSON outputs, and exportable formats. Adapts verbosity and detail level based on output configuration.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization