VentureLens
How a research team gave each analyst 10x the coverage, by handing 39 of every 40 hours of grunt work to an AI Employee.
VentureLens Leadership
Investment Intelligence Platform
“Our analysts were spending 39 of every 40 hours moving data around. We needed every number in a report to be auditable, not generated. Dojo Labs built it so the calculations run through one engine, against one source of truth, and the same inputs produce the same output every time.”
VentureLens Leadership
Investment Intelligence Platform
Measurable Outcomes
that drive ROI.
~95%
Analysis Time Reduced
98%
Extraction Accuracy
10x
Coverage Per Analyst
< 5 min
Time to Report
By integrating our computation layer, VentureLens transformed from a services-heavy model to a scalable, automated platform.
Client Overview
About VentureLens
VentureLens is a fintech investment intelligence company building tools for financial analysts, portfolio managers, and investment firms. The pitch was simple: take the 40-plus hours an analyst spends per company and compress it into an hour of actual thinking. The other 39 were not analysis. They were data hunting, format-juggling, and spreadsheet plumbing, eating the team's capacity to do the work that actually moves capital.
Industry
Fintech / Investment Intelligence
Client
VentureLens
Data Sources
15+: SEC filings, Bloomberg, Yahoo Finance, Quandl, gov databases
Engagement Type
Full-Stack AI Platform, Build & Integration
The Problem
The Challenge
Before we came in, every comprehensive company analysis at VentureLens was a 40-plus hour exercise, and almost none of those hours were spent on actual analysis. Analysts were hunting data across 15+ disconnected sources, cross-referencing figures with different formats and update cadences, reading earnings calls for sentiment signals, and building Excel models from scratch one cell at a time. By the time the data was clean enough to think with, the window for thinking had usually closed.
Hunt SEC filings, balance sheets, and earnings transcripts across multiple portals
Cross-reference financial figures from sources with different formats and update cadences
Read analyst reports and news for sentiment, risk language, and strategic signals
Compile every data point into Excel by hand, one cell at a time
Spend 2 to 3 more days formatting the output into a presentable report
The Core Problem
A research team's capacity is capped by how fast its analysts can get to the thinking, and at VentureLens 39 of every 40 hours went to data hunting and spreadsheet plumbing instead. They needed a Employee that does that grunt work and hands back clean, source-traceable numbers, so each analyst could cover ten times the companies without the figures becoming guesswork.
What We Built
Our Solution
We built the AI Employee behind VentureLens: a five-layer system that takes a ticker or research brief and returns a fully structured, Excel-ready investment report in under five minutes, doing the 39 hours of data work so the analyst gets straight to the call. Every number in that report is computed against source, not predicted.
01. Intelligent Data Collection Engine
We built a multi-source ingestion architecture that scrapes SEC filings, corporate sites, and government databases while pulling live API feeds from 15+ financial data providers. Failover, format normalization, and a built-in compliance layer for SEC and international data standards are wired in from the start.
Automated scraping of SEC filings, 10-Ks, 10-Qs, and earnings transcripts
Live API integration to 15+ financial data providers with automated failover
Built-in compliance framework for SEC and international data standards
Standardization engine handling inconsistent formats, missing values, and outliers
02. NLP Financial Document Processing
We built custom NLP models trained on financial domain language to extract structured signals out of unstructured sources: analyst reports, earnings call transcripts, market news, corporate communications. Sentiment, risk language, and strategic signals come out as quantified data the rest of the system can act on.
Custom financial domain NLP for entity recognition, sentiment scoring, risk language detection
Automated processing of analyst reports and corporate communications into structured data
Real-time sentiment scoring across market news with quantified output
95% accuracy in key metric extraction from unstructured financial narratives
03. Deterministic Financial Computation Engine
This is the part that matters. Every financial metric calculation runs through a deterministic Python engine: ratios, trend analysis, anomaly detection, time-series forecasting. No LLM touches these numbers. The same data produces the same metric every time.
Input: standardized financial data plus NLP-extracted qualitative signals
Processing: deterministic ratio engine, ML anomaly detection, time-series forecasting
Output: financial ratios, trend analysis, anomaly flags, 89% accurate short-term forecasts
04. Automated Excel Report Generation
We built a dynamic Excel generator that pulls computed metrics, NLP-extracted insights, and forecasting outputs into a fully formatted, investment-ready spreadsheet. Formula logic, source attribution, and assumptions documentation are embedded in every output.
Dynamic Excel output with intelligent formatting and formula logic
Automated assumptions documentation and source attribution in every report
Scheduled report delivery with configurable parameters
92% reduction in data entry errors through automated validation
05. Real-Time Control Dashboard
We built a real-time monitoring dashboard that gives analysts visibility into live data and manual override controls for assumption-driven decisions. Sub-second response on dashboard queries, support for 50+ concurrent users, throughput of 1M+ data points per hour.
Sub-second query response for live financial metrics and dashboard widgets
Manual input adjustment for analyst-driven assumption overrides
Support for 50+ simultaneous users with maintained performance
1M+ data points per hour throughput at 99.9% uptime
Tech Stack
Technologies Used
| Layer | Technology | Role |
|---|---|---|
| Backend API | Python / FastAPI | Service layer, routing, business logic |
| Web Scraping | Custom Python Architecture | SEC filings, corporate sites, gov databases |
| API Integration | Bloomberg, Yahoo Finance, Quandl | Live financial data with failover |
| NLP Engine | Custom Financial NLP Models | Document processing, sentiment, extraction |
| Compute Layer | Deterministic Python Engine | All metric calculation, ratios, anomaly detection |
| ML / Forecasting | Supervised + Time-series ML | Pattern recognition, 89% trend accuracy |
| Excel Generation | Dynamic Report Automation | Investment-ready formatted output |
| Cloud | AWS (distributed) | Scalable hosting, caching, 99.9% uptime |
Why We Built a Computation Layer
Financial metrics are real arithmetic. Return on equity, debt-to-equity, EBITDA margin, DCF. Every one of them is a calculation with a defined formula that must produce the same result given the same inputs. Always.
An LLM asked to produce those numbers will produce something that reads like financial analysis. But the numbers are the model's prediction of what those metrics should look like, shaped by training data, not the result of arithmetic against the actual filings. In an investment context, the gap between those two is the difference between a fiduciary report and an expensive guess.
So we routed every numerical request through a deterministic engine. Same data, same formulas, same result, every time. The LLM does language, the engine does numbers, and each can be audited on its own terms.
The Transformation
Before & After Dojo Labs
Before
40+ hours per comprehensive company analysis
Manual data collection across 15+ disconnected sources
2 to 3 days to produce a formatted Excel report
85% accuracy with human data entry errors
Analysis limited to a handful of companies at a time
After
End-to-end analysis in under 2 hours
Automated unified ingestion from all 15+ sources
Excel reports generated in under 5 minutes
98% extraction accuracy with automated validation
10x companies covered with the same headcount
Roadmap
What's Next
We architected the platform from day one for horizontal scale. Phase 2 expands the analytical surface, not the underlying infrastructure:
Portfolio monitoring with real-time anomaly alerts
Sector sweep engine for parallel analysis across an entire market segment
Natural language query: analysts ask in plain English, the engine returns computed results
CRM and workflow integration so outputs flow into deal management
Custom scoring models with client-defined weighting frameworks applied deterministically
Phase 2 is an extension of what we built, not a rebuild.
Ready to build something like this?
Book a 30-minute call. We'll discuss where your AI handles numbers, identify hallucination risks, and map out your computation layer.
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