Leanrs

AI is transforming investment analysis and financial modelling, but most discussions remain theoretical. This article showcases 8 real examples of how our analysts use AI to deliver institutional-quality research and models faster and more comprehensively than traditional methods.

From deep-dive credit analysis to complex financial modelling and automated portfolio monitoring, these examples highlight where AI creates measurable value in professional workflows. Each deliverable represents actual client work, demonstrating not just what's possible, but what we're already doing.

1. Revenue Driver Analysis: Spreading Complex Business Models

Understanding revenue composition across geographies, products, and customer segments is essential for accurate forecasting but often requires piecing together information scattered across filings, presentations, and transcripts.

For companies like Tesla with multiple revenue streams (vehicles, energy, services, regulatory credits), AI helps our analysts build comprehensive revenue bridges that would traditionally require hours of manual extraction and reconciliation.

Discover the revenue drivers spreading methodology, here

2. Update Financial Models in 5 Minutes Post-Earnings

The day earnings are released, analysts face a race to update models with new guidance, actuals, and revised assumptions. What traditionally takes 30-60 minutes of manual data entry and formula checks now takes 15 minutes with AI-assisted workflows.

Our analysts leverage AI to instantly extract quarterly results, update forecasts, and recalculate valuations, allowing portfolio managers to make informed decisions while the market is still digesting the news.

Read the 5-minute model update workflow, here

3. Map Private Market Players Quickly and Efficiently

Private market intelligence, identifying competitors and mapping sector landscapes, is critical for due diligence and sector research. Traditional methods involve days of manual searching and fragmented data compilation.

Our analysts used AI to efficiently map the private PET recycling industry, identifying companies with profiles on operations, locations, establishment history, and recycling processes. This approach significantly accelerates the initial market mapping phase that would traditionally require extensive manual research.

View the complete market mapping process, here

4. Deep-Dive Capital Structure Analysis in 15 Minutes

Traditional debt analysis requires manually extracting covenant details, maturity schedules, and refinancing risks from 100+ page credit agreements. AI transforms this multi-hour process into a 15-minute comprehensive screening tool.

Our analysts use AI to rapidly assess capital structures for credit investors evaluating potential investments, identifying key risks, covenant restrictions, and refinancing triggers that would otherwise require extensive manual review.

See the complete analysis methodology, here

5. Build Sophisticated Real Estate Models from Scratch

Multifamily real estate models require detailed unit-level economics, lease-up assumptions, operating expense projections, and multiple exit scenarios. Building these models from scratch traditionally takes 1-2 days of careful construction.

Our analysts use AI to accelerate the model-building process, from property data extraction to spreading operational numbers, setting up projections and calculating key metrics in hours rather than days.

6. Healthcare Service Models: Multi-Component Financial Planning

Specialized sectors like healthcare require domain-specific financial models. For therapist practice valuations, our analysts built comprehensive models covering P&L generation, individual therapist performance dashboards, and recruitment planning tools.

This three-component approach provides investors and operators with both high-level financial projections and granular operational insights, essential for evaluating practice acquisition opportunities.

7. Extract Earnings Insights and Track Management Sentiment Shifts

Understanding what management emphasizes and what they avoid discussing is as important as the numbers themselves. Tracking sentiment shifts across quarters helps identify inflection points before they're obvious in the financials.

Our analysts use AI to systematically extract key quotes, identify thematic changes, and flag instances where management tone diverges from results, providing portfolio managers with early signals of strategic shifts or emerging risks.

Read the sentiment analysis framework →

8. Automated Portfolio Intelligence: Daily News Delivered at 9 AM

Staying current on portfolio company developments without drowning in irrelevant noise is a persistent challenge. Manual news monitoring is time-consuming and prone to missing critical updates.

Using n8n automation workflows, our analysts set up intelligent news monitoring that delivers filtered, summarized updates on portfolio companies every morning, capturing only material developments like strategic announcements, regulatory changes, or competitive threats.

These examples demonstrate how AI transforms investment analysis and financial modeling workflows. But the key insight isn't about automation replacing expertise. It's about augmenting analyst capabilities.

Our approach keeps analysts firmly in the loop. AI handles data extraction, pattern recognition, and repetitive model building. Analysts provide the critical layer: judgment, context, validation, and strategic insight. This combination delivers research and models that are both faster and higher quality than either approach alone.

The result is institutional-grade analysis and sophisticated financial models at a pace that matches today's markets. Our analysts spend less time on mechanical tasks and more time on what matters: identifying opportunities, stress-testing assumptions, and generating actionable insights.

How Our Analysts Use AI: 8 Client Deliverables

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