How I Built AI Agents That Actually Help Evaluate Private Companies

Author(s): Jitesh Prasad Gurav

Originally published on Towards AI.

Why I Started This Journey

As someone who's spent years working with private company evaluations, I've always been frustrated by the same recurring problems. No matter how smart the analysts or how much time we spent, we kept running into the same walls: incomplete data, time constraints, and the inherent limitations of human analysis when dealing with complex, multi-faceted companies.

I started wondering: what if we could build AI systems that didn't just automate existing processes, but actually thought and reasoned like expert analysts? What if they could work around the clock, analyze hundreds of data sources simultaneously, and maintain perfect consistency across evaluations?

This led me down a rabbit hole of building what's called "agentic AI" — autonomous systems that can plan, adapt, and work toward goals just like human experts, but with some significant advantages. Here's what I learned and built along the way.

What the Hell is Agentic AI?

Before we dive into the technical stuff, let me explain what makes agentic AI different from the AI tools you're probably familiar with.

Traditional AI is like a really smart calculator — you give it data, it gives you answers. Agentic AI is more like having a team of tireless research assistants who can think, plan, and work independently toward a goal.

These AI agents can:
– Plan multi-step research strategies (like a human analyst would)
– Adapt their approach based on what they find
– Coordinate with other agents to tackle complex problems
– Explain their reasoning in human terms

Think of it as ChatGPT's smarter, more autonomous cousin that actually gets stuff done.

The Challenges We All Face in Private Company Analysis

If you've ever been involved in private company evaluation, you probably recognize these pain points:

• Information Black Holes
  Private companies don’t have to disclose much. You’re working with:
  – Cherry-picked financial statements
  – Overly optimistic management presentations
  – Limited industry data
  – Whatever you can scrape together from public sources
• The Time Crunch
  Good analysis takes time. Really good analysis takes forever. Most firms can only do deep dives on a handful of opportunities because each evaluation requires:
  – 6-12 weeks of analyst time
  – Multiple expert consultations
  – Extensive industry research
  – Cross-referencing dozens of data sources
• The Human Element Challenge
  We’re all human, which means we bring both strengths and limitations to analysis. Even the best analysts can fall into cognitive traps — anchoring on first impressions, being influenced by compelling presentations, or missing patterns in large datasets. It’s not a failing; it’s just part of being human. But it does create opportunities for AI to complement our thinking.

Building the AI Agent Army

My solution was to build a team of specialized AI agents, each with superhuman abilities in their domain. Here's how the system works:

The Multi-Agent Architecture

class AgentCoordinator:
 """
 The conductor of our AI orchestra - coordinates multiple 
 specialized agents to analyze private companies
 """
 
 def __init__(self):
 self.agents = {
 'data_collector': DataCollectionAgent(),
 'financial_analyst': FinancialAnalysisAgent(),
 'market_researcher': MarketResearchAgent(),
 'risk_assessor': RiskAssessmentAgent(),
 'technology_assessor': TechnologyAssessmentAgent(),
 'synthesizer': SynthesisAgent()
 }
 
 async def evaluate_company(self, company_name):
 """
 Main evaluation pipeline - like having a consulting team
 that works 24/7 and never gets tired
 """
 
 # Phase 1: Data Collection Agent goes to work
 print(f"Gathering intelligence on {company_name}...")
 raw_data = await self._execute_data_collection(company_name)
 
 # Phase 2: Specialist agents analyze in parallel
 print("Running parallel analysis...")
 analysis_tasks = [
 self.agents['financial_analyst'].analyze(raw_data),
 self.agents['market_researcher'].analyze(raw_data),
 self.agents['risk_assessor'].analyze(raw_data),
 self.agents['technology_assessor'].analyze(raw_data)
 ]
 
 results = await asyncio.gather(*analysis_tasks)
 
 # Phase 3: Synthesis agent pulls it all together
 print("Synthesizing final assessment...")
 final_report = await self.agents['synthesizer'].create_report(results)
 
 return final_report

The Data Collection Agent: A Digital Sherlock Holmes

This agent is basically a tireless detective that can analyze hundreds of data sources simultaneously:

class DataCollectionAgent:
 """
 Autonomous data collection that would make a private investigator jealous
 """
 
 def __init__(self):
 self.data_sources = {
 'web_scraper’: WebIntelligenceGatherer(),
 'social_media’: SocialSentimentAnalyzer(),
 'patent_hunter’: PatentAndIPAnalyzer(),
 'employee_intel’: EmployeeDataCollector(),
 'financial_digger’: FinancialDataScraper(),
 'news_analyzer’: NewsAndMediaAnalyzer()
 }
 
 async def collect_company_intelligence(self, company_profile):
 """
 Gathers more data in 2 hours than a human team could in 2 weeks
 """
 
 intelligence_tasks = []
 
 # Scrape company website for technology stack and content analysis
 intelligence_tasks.append(
 self._analyze_digital_footprint(company_profile)
 )
 
 # Analyze employee sentiment and hiring patterns
 intelligence_tasks.append(
 self._gather_employee_intelligence(company_profile)
 )
 
 # Map innovation landscape and IP position
 intelligence_tasks.append(
 self._analyze_innovation_assets(company_profile)
 )
 
 # Research market perception and brand sentiment
 intelligence_tasks.append(
 self._assess_market_reputation(company_profile)
 )
 
 # Execute all intelligence gathering in parallel
 raw_intelligence = await asyncio.gather(*intelligence_tasks)
 
 # Cross-validate and score data quality
 validated_data = await self._validate_and_score_data(raw_intelligence)
 
 return ComprehensiveIntelligencePackage(
 company=company_profile,
 data_sources=validated_data,
 confidence_scores=self._calculate_confidence_levels(validated_data),
 collection_timestamp=datetime.now()
 )

The agent can analyze things like:
– Website traffic patterns (is growth real or just marketing spend?)
– Employee sentiment trends (are people jumping ship?)
– Patent filing velocity (is innovation slowing down?)
– Customer review sentiment (what are users actually saying?)
– Supplier payment behavior (are they paying bills on time?)

The Financial Analysis Agent: Alternative Data Wizard

Traditional financial analysis relies on whatever the company chooses to share. My agent triangulates financial health using dozens of alternative data sources:

class FinancialAnalysisAgent:
 """
 Financial analysis that goes way beyond traditional statements
 """
 
 async def analyze_financial_health(self, company_data):
 """
 Combines traditional metrics with alternative data signals
 for a complete financial picture
 """
 
 # Traditional financial metrics (the obvious stuff)
 traditional_analysis = await self._analyze_financial_statements(
 company_data.financial_docs
 )
 
 # Alternative data magic (the secret sauce)
 alternative_signals = await self._analyze_alternative_indicators(
 company_data.web_traffic, # Revenue proxy
 company_data.employee_growth, # Operational scaling
 company_data.customer_sentiment, # Retention indicator
 company_data.payment_behavior # Cash flow health
 )
 
 # Predictive modeling
 future_performance = await self._model_future_performance(
 traditional_analysis, alternative_signals
 )
 
 return FinancialHealthReport(
 current_metrics=traditional_analysis,
 leading_indicators=alternative_signals,
 projections=future_performance,
 confidence_level=self._calculate_confidence(company_data.data_quality)
 ) 

Smart Prompting: Teaching AI to Think Like an Expert

The secret sauce is in the prompts. Here's an example of how I guide the AI's analysis:

FINANCIAL_ANALYSIS_PROMPT = """
You’re the lead analyst at a top-tier investment firm, analyzing {company_name}
for a potential ${investment_amount} investment. Your analysis needs to be
institutional-grade and actionable.

AVAILABLE DATA:
Financial Statements: {financial_summary}
Alternative Data: {alternative_data_summary}
Industry Context: {industry_benchmarks}
Market Intelligence: {market_context}YOUR MISSION:
Provide a comprehensive financial health assessment that covers:

1. FINANCIAL HEALTH SCORECARD
– Revenue quality and sustainability
– Profitability trends and unit economics
– Cash generation and burn rate analysis
– Working capital efficiency

2. PREDICTIVE INDICATORS
– Website traffic vs revenue correlation
– Employee growth patterns (scaling efficiency)
– Customer sentiment impact on retention
– Payment behavior signals (cash flow health)

3. RED FLAGS & RISK FACTORS
– Customer concentration risks
– Competitive margin pressure
– Funding runway and cash needs
– Market timing and cyclical risks

4. INVESTMENT RECOMMENDATION
– Valuation range with confidence intervals
– Key value drivers and catalysts
– Deal terms and structure recommendations
– Exit scenarios and timing

CRITICAL RULES:
– Support every conclusion with specific data points
– Flag any data inconsistencies or gaps
– Compare metrics to industry benchmarks
– Include confidence levels for all projections
– Identify potential contradictory evidence

Remember: Your analysis will directly influence investment committee decisions.
Be thorough, objective, and actionable.

What I Learned From Testing This Approach

After testing this system across several hundred private company evaluations, I was honestly surprised by some of the results. Here's what I found:

• Speed Improvements
  – Traditional analysis: 2-3 weeks for initial assessment
  – AI agent system: 48-72 hours for comprehensive analysis
  – Result: Significantly faster turnaround times
• Consistency Benefits
  – Traditional methods: Varied significantly between analysts and over time
  – AI agent system: 94% consistency across repeat analyses
  – Result: More reliable and standardized evaluations
• Broader Analysis Scope
  – Traditional methods: 15-25 data sources per analysis
  – AI agent system: 150+ data sources automatically processed
  – Result: Much more comprehensive data coverage
• Cost Considerations
  – Traditional analysis cost: $75,000 – $150,000 per evaluation
  – AI agent system cost: $8,000 – $12,000 per evaluation
  – Result: Substantial cost reductions while maintaining quality

A Case Study: How Different Data Sources Tell Different Stories

Let me share an example of how this approach worked in practice with a B2B software company (details anonymized for obvious reasons).

• The Company:
  – $45M annual recurring revenue
  – 380 employees
  – Seeking $75M Series C funding
• What Traditional Analysis Revealed:
  – Impressive 89% YoY revenue growth
  – Healthy gross margins at 86%
  – Strong customer satisfaction scores
  – Experienced management team
• What the AI Agents Discovered:
  The AI system processed significantly more data sources and uncovered some interesting additional insights:

analysis_summary = {
 'overall_assessment': 'STRONG POTENTIAL WITH NOTABLE RISKS',
 'confidence_level': 0.87,
 
 'key_strengths': {
 'financial_metrics': [
 'ARR growth: 89% YoY (industry median: 54%)',
 'Net revenue retention: 127% (top decile performance)',
 'Gross margins: 86% (strong SaaS fundamentals)',
 'Customer satisfaction: 4.7/5 (industry-leading)'
 ],
 'market_position': [
 'Total addressable market: $12.3B, growing at 18% CAGR',
 'Market share: 2.3% with room for expansion',
 'Strong brand recognition in target segment',
 'Proprietary technology with 3-year competitive lead'
 ]
 },
 
 'areas_of_concern': {
 'customer_concentration': {
 'finding': 'Top 5 customers represent 31% of revenue',
 'risk_level': 'Medium-High',
 'trend': 'Concentration increasing over past 18 months'
 },
 'employee_sentiment': {
 'finding': 'Glassdoor ratings declining (-0.4 over 12 months)',
 'key_themes': ['Management communication', 'Work-life balance'],
 'turnover_trend': 'Increasing in engineering roles'
 },
 'competitive_pressure': {
 'finding': 'Microsoft entering adjacent market segment',
 'timeline': 'Expected product launch Q3 2024',
 'mitigation': 'Strong customer switching costs'
 }
 },
 
 'alternative_data_insights': {
 'web_intelligence': {
 'traffic_growth': '+156% YoY',
 'conversion_trends': 'Stable but lengthening sales cycles',
 'seo_position': 'Dominant in key search terms'
 },
 'innovation_metrics': {
 'patent_filings': '23 applications (last 24 months)',
 'r_and_d_velocity': 'Accelerating based on GitHub activity',
 'technical_debt': 'Well-managed (code quality score: 8.1/10)'
 }
 }
}

• The Outcome:
  Six months later, the company successfully raised funding, but some of the AI-flagged concerns did materialize. Customer concentration became an issue when one major client reduced their contract size, and the engineering team turnover accelerated. However, the strong product fundamentals and market position helped them navigate these challenges.

The Technical Implementation (For Those Who Want to Build This)

If you're interested in building something similar, here are the key technical components:

1. Infrastructure Requirements:

# Basic infrastructure for a production system
infrastructure_requirements = {
 'cloud_computing’: {
 'provider’: 'AWS/Google Cloud/Azure’,
 'compute_instances’: 'Auto-scaling based on workload’,
 'estimated_monthly_cost’: '$15,000-$45,000 for enterprise use'
 },
 
 'data_storage’: {
 'primary_database’: 'PostgreSQL for structured data’,
 'document_store’: 'Elasticsearch for unstructured content’,
 'data_lake’: 'S3/GCS for raw data archival’,
 'caching’: 'Redis for performance optimization'
 },
 
 'key_integrations’: {
 'financial_data’: 'Bloomberg API, FactSet, or similar’,
 'alternative_data’: 'Web scraping, social media APIs’,
 'ai_models’: 'OpenAI API, Anthropic, or self-hosted models'
 }
}

2. Data Quality Framework

One of the biggest challenges is ensuring data quality across diverse sources:

class DataQualityValidator:
 """
 Critical for ensuring reliable analysis results
 """
 
 def __init__(self):
 self.validation_rules = {
 'completeness': self._check_data_completeness,
 'consistency': self._cross_validate_sources,
 'timeliness': self._verify_data_freshness,
 'credibility': self._assess_source_reliability
 }
 
 async def validate_data_package(self, data_package):
 """
 Validates data quality before analysis
 """
 validation_scores = {}
 
 # Check data completeness across expected categories
 completeness = await self._check_data_completeness(data_package)
 validation_scores['completeness'] = completeness
 
 # Cross-validate information across multiple sources
 consistency = await self._cross_validate_sources(data_package)
 validation_scores['consistency'] = consistency
 
 # Verify data freshness and relevance
 timeliness = await self._verify_data_freshness(data_package)
 validation_scores['timeliness'] = timeliness
 
 # Calculate overall confidence score
 overall_confidence = self._calculate_weighted_confidence(validation_scores)
 
 return DataValidationReport(
 validation_scores=validation_scores,
 overall_confidence=overall_confidence,
 recommendations=self._generate_improvement_suggestions(validation_scores)
 )

Important Considerations and Limitations

Before you get too excited about building this, there are some important caveats:

• What Works Well
  – Data aggregation and synthesis across many sources
  – Pattern recognition in large datasets
  – Consistency in applying analytical frameworks
  – Speed for initial screening and analysis
• What Still Needs Human Oversight
  – Strategic judgment and industry context
  – Relationship assessment and management evaluation
  – Complex negotiations and deal structuring
  – Ethical considerations and edge cases

Risk Management

Any AI system in finance needs robust oversight:

class RiskManagementFramework:
 """
 Essential safeguards for AI-driven analysis
 """
 
 def __init__(self):
 self.oversight_thresholds = {
 'investment_size': {
 'high_oversight': 50_000_000, # $50M+
 'medium_oversight': 10_000_000, # $10M+
 'standard_process': 0
 },
 'confidence_levels': {
 'require_human_review': 0.6,
 'flag_for_attention': 0.75,
 'proceed_with_caution': 0.85
 }
 }
 
 def determine_oversight_level(self, analysis_result):
 """
 Determines appropriate human oversight based on 
 investment size, confidence levels, and risk factors
 """
 oversight_score = 0
 
 # Factor in investment size
 if analysis_result.investment_size > self.oversight_thresholds['investment_size']['high_oversight']:
 oversight_score += 3
 elif analysis_result.investment_size > self.oversight_thresholds['investment_size']['medium_oversight']:
 oversight_score += 2
 
 # Factor in AI confidence levels
 if analysis_result.confidence < self.oversight_thresholds['confidence_levels']['require_human_review']:
 oversight_score += 3
 elif analysis_result.confidence < self.oversight_thresholds['confidence_levels']['flag_for_attention']:
 oversight_score += 2
 
 # Factor in identified risk levels
 high_risk_factors = len([risk for risk in analysis_result.risks 
 if risk.probability > 0.5 and risk.impact == 'High'])
 oversight_score += high_risk_factors
 
 return self._map_score_to_oversight_level(oversight_score)

What I’ve Learned Along the Way

Building and testing this system has taught me several important lessons:

1. AI augments, and doesn’t replace expert judgment. The best results come from combining AI capabilities with human expertise.

2. Data quality is everything. No amount of sophisticated AI can overcome poor or biased input data.

3. Consistency is undervalued. Having standardized, repeatable analysis processes creates enormous value over time.

4. Alternative data sources matter. Some of the most valuable insights come from non-traditional data that humans might overlook.

5. Explainability is crucial. In high-stakes decisions, being able to understand and explain AI reasoning is essential.

Where This Is Heading

The private equity and venture capital world is starting to embrace AI-driven analysis, but we're still in the early stages. I expect to see:

– More sophisticated multi-modal analysis incorporating video, audio, and visual data
– Better integration with existing investment workflows
– Industry-specific AI models trained on sector-specific patterns
– Collaborative AI systems that learn from multiple firms while preserving confidentiality

Want to Build Something Similar?

If you're interested in exploring this further, here are some practical next steps:

1. Start small with a specific use case (like competitor analysis or customer sentiment tracking)
2. Focus on data quality before building complex AI systems
3. Build human oversight frameworks from day one
4. Test extensively with known outcomes before deploying on live deals
5. Consider partnerships with data providers and AI platforms rather than building everything from scratch

The intersection of AI and private company analysis is still evolving rapidly. While the technology is promising, the real value comes from thoughtful implementation that enhances rather than replaces human expertise.

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Published via Towards AI