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Executive Summary
Alternatives.pe is a leading investment intelligence platform, trusted by global investors and institutions to provide timely, accurate and structured insights into private capital markets. A core capability of their platform is to continuously monitor changes in portfolios, team appointments and strategic activity from over 10,000+ investment firms.
To meet this need, Alternatives.pe partnered with AgentScale AI to deliver Project Sherlock — a large-scale, fully-automated AI-powered data acquisition system. Project Sherlock is a production-grade platform that continuously monitors and tracks updates across 10,000+ investment firm websites, investor newsletters, and media sources.
The outcome is the launch of a highly accurate, real-time investment intelligence product, enabled through a scalable, intelligent data infrastructure.
The Client: Alternatives.pe
Alternatives.pe is trusted by institutions worldwide for real-time visibility into the global private markets ecosystem, aggregating data on capital deployment, fund formation and team movements across venture capital, private equity, and alternative asset managers.
If Alternatives.pe relied on analysts to manually monitor websites, newsletters and market news there would be inherent limitations on scale, timeliness and accuracy. Further, standard commercial tools evaluated by Alternatives.pe were unable to intelligently deal with the spectrum of sites to be monitored and deliver the operational reliability necessary for their critical business needs — particularly at the scale of 10,000+ firms.
Given the strategic importance of this capability, Alternatives.pe partnered with AgentScale AI to build a robust, fully-automated AI-enabled data platform capable of providing reliable, scalable and high-accuracy investment data extraction and monitoring.
Strategic Objectives
Alternatives.pe engaged AgentScale AI with the following objectives:
- ✅Automation: Fully automate structured data extraction from 10,000+ investor websites, newsletters, RSS feeds and email newsletters.
- ✅Accuracy: Achieve rigorously validated data accuracy (>90%+) across three data categories: Portfolio Companies, Team Executives and Investment News.
- ✅Scalability: Reliably process data at scale, extracting structured data from 10,000+ URLs daily, while controlling for cost, latency and long-tail edge cases.
- ✅Auditability: Ensure comprehensive historical tracking, change detection and auditable records of all updates to support long-term strategic analysis and trend reporting.
Why AgentScale AI?
AgentScale AI specializes in engineering business-critical AI systems with rigorously validated data accuracy and production-level scalability. Alternatives.pe sought a long-term partner with deep AI expertise — capable of delivering proven, production-grade AI automation systems at enterprise scale.
AgentScale AI was selected for our proven track record:
- 🔹Deep specialization in building and deploying reliable, scalable and rigorously evaluated, business-critical LLM-powered systems for production environments.
- 🔹Engineering systems that balance AI performance, cost efficiency and latency without compromising reliability.
- 🔹Demonstrated success in developing differentiated AI capabilities directly supporting core product lines.
The Solution: A Modular AI Data Acquisition Engine
AgentScale AI developed Project Sherlock as an integrated, modular AI data acquisition system, incorporating advanced proprietary AI algorithms, structured prompt engineering and automated data validation processes — with a robust infrastructure providing full reliability, cost efficiency and auditability.
1. Intelligent Web Navigation
- Daily automated extraction of structured data from over 10,000+ unique URLs, including advanced browser interaction automation, dynamic content rendering and intelligent fallback navigation.
- LLM-based inference models identify relevant portfolio company pages, executive team listings and investment news items through semantic inference, achieving high accuracy and coverage.
2. Structured Data Extraction and Validation
- Conducted detailed comparative evaluations across multiple LLM models (e.g. GPT-4o, GPT-4o-mini, Gemini 2.0 Flash, Claude Sonnet 3.5), managing and optimizing across accuracy, cost and latency.
- Designed and enforced structured data schemas and vocabularies to ensure highly precise and consistent extraction of portfolios, teams and news data.
- Independently validated data accuracy metrics exceeding >90%+, confirmed through manual evaluation of a statistically significant randomized sample representing 10% of collected data.
3. Update Detection with Historical Traceability
- Developed sophisticated proprietary normalization and caching algorithms to reliably track and detect entity-level additions, removals and modifications of portfolio companies, team executives and investment news data.
- Implemented a robust update detection pipeline, enabling auditability, historical traceability and confidence in long-term data quality.
4. Multiple Data Source Integration
- Expanded data coverage through integration support of email newsletters and RSS feed ingestion, capturing data updates beyond websites.
- Automated classification of investment metadata (fund identifiers, portfolio companies, dates, news classifications), enriching Alternatives.pe's data ecosystem.
Technical Excellence: Building Enterprise-Grade AI Infrastructure
Project Sherlock required designing scalable infrastructure to support a highly heterogeneous web data ecosystem, while maintaining data accuracy under real-world production constraints.
Key challenges and solutions included:
- ✅Edge case management: Continuous iteration and improvement of data extraction methods, identifying and prioritizing edge-cases based on statistical frequency to maximize data accuracy and coverage.
- ✅Performance optimization: Evaluation and migration to better models reduced LLM inference costs by 80% while maintaining high accuracy through a proprietary prompt engineering and evaluation framework.
- ✅System resilience: Modular cron jobs, parallelized processing, and cloud-native deployment infrastructure ensured uninterrupted operation at scale to support 10,000+ websites daily.
The result is a high-performance data acquisition engine that delivers enterprise-grade performance, ensuring cost-efficient, high-confidence and resilient operational stability.
Business Impact: A New Standard in Investment Intelligence
The Project Sherlock system powers a vital data acquisition pipeline for Alternatives.pe, enabling fully-automated monitoring of global investment firm activity.
- 🚀Daily High-Scale Automation: Structured data extraction from 10,000+ investor websites, newsletters and news feeds, with weekly monitoring of data updates.
- 🚀New Commercial Product Line: Enabled a differentiated, high-value data offering directly commercializable by Alternatives.pe.
- 🚀Operational Efficiency: Fully automated workflows completely eliminates the need for analyst hours, enabling accurate and up-to-date investment insights with >90%+ accuracy and coverage.
Ready to Partner with AgentScale AI?
AgentScale AI is focused on solving the most complex, high-stakes automation problems for modern businesses. We deliver rigorously validated, production-grade AI capabilities with independently verified accuracy exceeding >90%+. We partner strategically with clients committed to leveraging AI as a core differentiator, providing clear SLA-backed accuracy commitments and long-term partnership alignment.
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