Haneesh Raj

Posted on Sep 17

Automating Business Intelligence: How We Built a Multi-Agent AI System for Business Data Extraction

#ai #multiagent #buisnessintelligence #promptengineering

Imagine manually researching 500 competitors for your quarterly business review. You start with the first company website, hunting for their address, industry classification, and business model. Three hours later, you've covered just 12 companies with questionable data accuracy. The current approaches are fundamentally broken. Manual research is painfully slow and inconsistent, with different analysts extracting different information from identical websites. Off-the-shelf tools lack sophistication for complex business contexts, while generic web scraping misses nuanced intelligence. Organizations take thousands of hours on research that could be automated, delaying critical decisions and missing competitive opportunities because comprehensive analysis takes too long.
What if AI could understand businesses like humans do - recognizing brands, categorizing industries, and extracting locations - but at machine speed and scale? That's exactly what we built: a multi-agent AI system that automates competitive intelligence extraction with human-level accuracy.

Meet Business Insights: Your Multi-LLM Data Extraction System

Business Insights is an automated data extraction platform that transforms how organizations gather business information from websites. Instead of spending hours manually scraping and processing company data, our multi-LLM agent system processes hundreds of websites simultaneously, extracting structured business profiles in minutes. Built on a specialized multi-agent AI architecture, it combines computer vision models, large language models, and intelligent orchestration to understand and extract business data with human-level comprehension - but at unprecedented speed and scale.

Our platform delivers four critical data extraction workflows that power comprehensive business intelligence: precise business categorization using merchant category codes (MCC) , comprehensive address extraction with multi-stage geographic verification, automated brand asset detection and image processing, and intelligent business profile synthesis.

Unlike generic web scraping tools, our multi-LLM agent system understands business context through intelligent prompt engineering and model specialization. While traditional solutions extract raw HTML data, we deliver structured intelligence - categorizing businesses through LLM reasoning, validating addresses through multi-source verification, and processing brand assets through computer vision. Our agent orchestration ensures reliability and data quality that manual extraction and basic automation simply cannot match.

The Complexity of Intelligent Business Data Extraction

Rule-based extraction systems break with website design changes and can't adapt to new content patterns. Manual data extraction doesn't scale and introduces human inconsistency and bias. Off-the-shelf tools provide generic data points but miss the nuanced business context that drives intelligent categorization and validation.

Extracting meaningful business data from websites isn't just web scraping - it's AI reasoning at scale. Websites have inconsistent structures, varying content layouts, and different ways of presenting the same information. A restaurant might list its address in the footer, contact page, or embedded within descriptive text. Business categories aren't standardized - one company calls itself "digital marketing," another "growth consulting," yet both need the same MCC classification. Brand assets appear in multiple formats, sizes, and contexts across different page sections.

Our Multi-Agent Architecture

Traditional single-LLM solutions try to be a Swiss Army knife - handling everything from image recognition to text classification in one model. Our multi-agent architecture takes a specialized approach: each agent is optimized for specific tasks with tailored prompts, models, and processing logic. This specialization dramatically improves accuracy and cost efficiency. Instead of sending complex multi-modal prompts to expensive models, we route visual tasks to vision-optimized agents and text tasks to language-specialized agents. Each agent becomes an expert in its domain, leading to better results and lower operational costs.

The Information Extraction Orchestrator manages the entire workflow, coordinating agent execution, handling data validation, implementing retry logic, and ensuring consistent output formatting across all specialized agents.

The Brand Detection Agent handles comprehensive brand analysis using computer vision and text processing models. It extracts company names from multiple sources (brand images, page titles, headers), detects visual brand assets and logos, and simultaneously analyzes location context and business addresses. This agent combines visual processing with intelligent text extraction to build complete brand profiles.

The MCC Classification Agent specializes in business categorization using a two-stage LLM approach. It analyzes website content to determine merchant category codes through primary classification followed by confidence-based validation, ensuring accurate industry mapping.

The Address Extraction Agent operates through a three-stage process: initial content scanning, structured extraction, and search-based validation. It handles complex address scenarios like PO boxes, multiple locations, and international formats.

Brand Detection Agent: Multi-Modal Intelligence

The Brand Detection Agent combines computer vision with intelligent text analysis to build comprehensive brand profiles. When processing a website, it first extracts all images and filters them for potential brand assets using size and position heuristics. These images are then processed through a vision-language model that simultaneously analyzes visual elements and any embedded text.

What makes this agent unique is its multi-field extraction approach. Instead of just identifying logos, it extracts company names, brand descriptions, image URLs, and location context in a single pass. The agent processes hundreds of images per website, identifies company names with high confidence scores, and extracts contextual information from various page elements like headers, footers, and navigation areas. The agent’s confidence scoring considers multiple factors: text clarity in images, brand consistency across the site, and contextual validation.

async def detect_brand_elements(self, images):
    for image in filtered_images:
        response = await vision_model.analyze(
            image=image,
            prompt=self.brand_extraction_prompt,
            fields=["company_name", "description", "location", "brand_assets"]
        )
        results.append(self.score_confidence(response))
    return self.merge_brand_intelligence(results)

MCC Classification Agent: Two-Stage Precision

The MCC Classification Agent employs a sophisticated two-stage approach that mirrors human business analysis. Stage 1 performs broad categorization, analyzing the business model, target customers, and primary activities to select an appropriate MCC category range. This prevents the common error of jumping to specific codes without understanding the business context.

Stage 2 then performs granular classification within the selected range. The agent first identifies broad category ranges based on business operations, customer interactions, and revenue models. The second stage then refines this selection by analyzing specific business activities, comparing alternative MCC codes, and selecting the most accurate classification based on primary business functions.

The agent’s strength lies in its comparative analysis. It doesn’t just select an MCC code — it evaluates alternatives and explains its reasoning. Multiple potential codes are considered and systematically compared, with the agent providing clear justification for its final selection. This approach ensures accurate business categorization that reflects actual business operations rather than surface-level descriptions.

// Two-stage MCC classification
async def classify_business(self, content, screenshot):
    # Stage 1: Category range selection
    stage1_result = await self.llm.analyze(
        content=content,
        prompt=self.category_range_prompt,
        task="select_mcc_range"
    )

    # Stage 2: Specific MCC within range
    stage2_result = await self.llm.analyze(
        content=content,
        mcc_range=stage1_result.range,
        prompt=self.specific_mcc_prompt,
        task="final_classification"
    )

    return self.validate_mcc_logic(stage1_result, stage2_result)

Address Extraction Agent: Multi-Source Validation

The Address Extraction Agent operates through intelligent optimization and multi-source validation. It first checks if the Brand Detection Agent already found address information — avoiding duplicate processing while leveraging cross-agent intelligence. If no address is available, it performs targeted content scanning using regex patterns and contextual analysis to identify address components.

The agent’s sophistication shows in its validation pipeline. After extracting potential addresses, it attempts geocoding validation through Google Places API to verify accuracy and obtain coordinates. When geocoding fails, the agent doesn’t discard the address but instead applies a confidence penalty and uses the best available information.

For complex scenarios, the agent employs a search API fallback that queries Google Search with the company name and extracted business information. This three-tier approach ensures maximum address capture while maintaining data quality. The confidence scoring reflects the validation level: high confidence for geocoded addresses, medium for validated addresses, and adjusted confidence for unvalidated but structured addresses.

// Address extraction with validation
async def extract_address(self, content, brand_data):
    # Optimization: Use brand-detected address if available
    if brand_data.address:
        address = brand_data.address
        source = "optimized_brand"
    else:
        address = await self.extract_from_content(content)
        source = "website_content"

    # Geocoding validation
    geocoding_result = await self.geocode_address(address)
    if geocoding_result.success:
        return self.create_validated_address(address, geocoding_result)
    else:
        return self.create_unvalidated_address(address, source)

Orchestration & Optimization Strategies

The Information Extraction Orchestrator implements intelligent workflow management that maximizes efficiency while ensuring data quality. It coordinates agent execution based on content analysis — running Brand Detection and MCC Classification in parallel while optimizing Address Extraction based on brand analysis results. This orchestration reduces unnecessary API calls and processing time.

Error handling and retry logic are built into every level. Individual agent failures don’t crash the entire extraction process. The orchestrator implements exponential backoff for API timeouts, alternative model fallbacks for processing failures, and cross-validation between agents when data overlaps. The system delivers complete business profiles by leveraging cross-agent optimization and maintaining extracted data through smart fallbacks, ensuring high success rates even when individual components encounter processing challenges.

Measurable Performance Across All Metrics

Our multi-agent system delivers consistent, measurable results across thousands of website extractions. The platform maintains a 96% overall success rate for complete business profile extraction, with individual agent performance exceeding 95% accuracy. Processing speed averages 75–100 websites per hour depending on site complexity and content volume. Token usage optimization keeps operational costs at $0.012 per website extraction, making large-scale competitive intelligence economically viable.

The system demonstrates remarkable consistency across different website types and industries. E-commerce sites, service businesses, restaurants, and professional services all achieve similar accuracy rates. Brand detection succeeds on 98% of sites with identifiable visual assets, while MCC classification maintains 95% accuracy across diverse business models. Address extraction achieves 92% success rate with geocoding validation, and 96% when including unvalidated but structured addresses.

Traditional single-LLM approaches often cost $0.05–0.15 per website due to inefficient model usage and redundant processing. Our multi-agent architecture reduces costs by 75% through intelligent task routing and model specialization. Simple text extraction uses cost-effective models, while complex reasoning tasks are routed to premium models only when necessary.

Token usage optimization plays a crucial role in cost efficiency. Instead of sending massive prompts to expensive models, we pre-process content, filter relevant information, and send targeted prompts to specialized agents. Parallel processing eliminates sequential bottlenecks, and cross-agent optimization prevents duplicate API calls. The result: enterprise-grade accuracy at a fraction of traditional costs.

Processing speed improvements come from architectural decisions rather than just faster hardware. Parallel agent execution allows simultaneous brand detection, MCC classification, and address extraction. Smart caching prevents re-processing of similar content, and intelligent retry logic minimizes failed extractions that require reprocessing.

The system scales linearly with available resources. Single-threaded processing handles 25–30 sites per hour, while parallel execution on standard hardware achieves 75–100 sites per hour. Cloud deployment with auto-scaling can process thousands of websites simultaneously, making it suitable for large-scale market research, competitive analysis, and database enrichment projects.

Experience Business Insights Today

Ready to automate your competitive intelligence? Business Insights is available as an open-source project with comprehensive documentation and setup guides. Clone the repository, follow our quick-start instructions, and begin extracting business data in minutes. Our demo endpoints let you test the system with your own website URLs before full deployment.

Contributing to the Future of AI Data Extraction

Business Insights thrives as an open-source project because of community contributions. We know there are still challenges to solve — handling complex international websites, improving accuracy for niche industries, and optimizing costs further. Whether you’re fixing bugs, adding new agent capabilities, or improving documentation, your contributions help advance AI-powered data extraction for everyone.

Check out our GitHub repository for contribution guidelines, open issues, and development roadmap. From code improvements to feature requests, every contribution moves the project forward. Join our growing community of developers building the future of automated business intelligence.

Acknowledgments

This project was developed through a collaboration between myself and Professor Junwei Huang, whose guidance and expertise in AI systems was instrumental throughout the development process. His mentorship helped navigate the complex challenges of multi-agent architecture design and pushed me to explore innovative approaches to automated data extraction.

Ready to get started? Visit our GitHub repository — and join the community building tomorrow’s competitive intelligence tools.