Many AI platforms struggle because they try to do too much. Broad, unfocused models lead to unreliable results, especially in complex financial workflows. Hudson Labs takes a different approach. By specializing in investment research, we achieve dramatically higher accuracy and reliability.
The Hudson Labs Co-Analyst is designed for specific equity research tasks—extracting, organizing, and analyzing financial data with precision. Unlike generalist AI models, it does not attempt to answer everything. This focus allows us to deliver more accurate, relevant, and actionable insights.
Hudson Labs Co-Analyst
The Co-Analyst extracts structured information from earnings calls, consistently capturing guidance, Q&A, and KPIs with commentary. It is factual and does not generate false information.
Now in beta, the Co-Analyst is live at platform.hudson-labs.com/coanalyst, making earnings call analysis easier than ever.
What Makes Hudson Labs Different From Competitors
- Company-specific, numeric-focused: Unlike generalist AI, we specialize in structured financial data, eliminating generic, non-actionable responses.
- Strict data retrieval: The model pulls data only from the prescribed source document, avoiding unreliable references to model memory.
- AI for extraction, not opinion: The Co-Analyst helps you track key financial metrics—such as same-store sales drivers for the last eight quarters—but it does not generate opinions on whether to buy a stock.
- Mathematical reasoning integration: Unlike traditional LLMs, which struggle with basic math, we handle numerical calculations separately, ensuring accuracy.
- Relevance ranking & noise suppression: Our AI prioritizes the most relevant financial information, reducing irrelevant or misleading outputs.
- Tabular output by default: Investors prefer structured data. Our models format results in tables for easy analysis.
The Co-Analyst is Built for Investors
Hudson Labs offers:
- Guidance workflow: Extracts recurring, one-time, numeric, and semi-quantitative guidance, delivering complete and structured outputs. This is an area where many AI models fail or provide highly inconsistent results. Find a detailed comparison below.
- Compare pipeline: Matches overlapping KPIs to compare multiple companies, supporting both qualitative and quantitative analysis.
- Mathematical reasoning: Accurately processes financial metrics, including percentage changes and annualized calculations.
- Consistency & factuality: Unlike chatbots, the Co-Analyst offers consistency and reliability.
Additionally, investors use Hudson Labs to assess forensic risks in governance, related parties, and management integrity, leveraging AI to detect downside risks beyond keyword searches.
NVIDIA guidance: Hudson Labs Co-Analyst vs Competitors
Hudson Labs outperforms other AI tools in identifying both hard and soft guidance. Below we compare the results from Nvidia’s Q3 2025 call held on November 20th, 2024. The test was performed on December 18, 2024.
The Hudson Labs Co-Analyst extracted the complete numerical guidance, performed mathematical calculations to arrive at the upper and lower end of the revenue guidance, and included qualitative disclosure. Directly copy the table to Excel or Google Sheets.
*Notice that the Co-Analyst accurately calculates the lower and upper end of guidance from the source:
A well-known financial AI chatbot competitor provided figures that appeared correct but were in fact wrong almost half the time. We have omitted their name out of respect.
Perplexity.ai for finance did not extract the complete numerical guidance. While hallucination is not an issue here, the lack of specificity means that the result is unusable to the average investor.
Features Competitors Offer That Hudson Labs Does Not (Yet)
Some competitors provide:
- Non-US coverage for European and Asian markets, though some exclude small-cap US stocks. Hudson Labs covers the entire US market.
- Analyst estimates, including price targets and ratings (Coming soon to Hudson Labs).
- Non-public equity analysis for industries like private equity, investment banking, and real estate. Hudson Labs specializes in US public equities.
- External data connections for model training on user data.
Rare technical expertise
Hudson Labs was founded in 2019 as the first financial AI platform fully powered by LLMs. The company is led by Suhas Pai, author of Designing LLM Applications, privacy co-chair of the BLOOM project, and four-time chair of the Toronto Machine Learning Summit. Learn more about our team here.
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Interested in more tools to take your investment analysis workflow to a new level? Check out our other lists of various investment research tools in financial markets:
