Hudson Labs Technology
Precision at scale for public market research
The Hudson Labs Co-Analyst is AI designed for institutional workflows where accuracy is non-negotiable, relevance matters and long time-horizon context is crucial.
The Co-Analyst extracts detailed quantitative and qualitative financial data across multiple documents and long time periods, with nearly 100% accuracy and zero hallucination.
Long time horizon, high-precision financial tasks present a particular challenge for AI systems. As of December 23, 2025 GPT-5.2, GPT-5.1, and Opus 4.5 score 53–57% in accuracy on financial tasks involving retrieval and analysis of specific financial information according to Vals AI benchmark.
Most AI products focus on low value creative tasks where accuracy cannot be guaranteed and is not important.
Hudson Labs’ focus on high-precision information extraction, direct-from source and the resulting quality is unique. We don’t do everything but what we do, you can always trust.
“Hudson Labs' main value proposition is handling high-precision finance tasks where hallucination and accuracy errors can be very costly.”
— Buyside AI Reviews
The problem Hudson Labs solves
Most AI tools perform adequately on short inputs or single documents. Error rates increase materially when those systems are asked to work across:
- Multiple calls and securities filings
- Overlapping but non-identical source material
- Several years of disclosures
- Subtle changes in language over-time
This is a structural limitation of generalist AI systems — not a prompting issue.
Hudson Labs is built specifically for long-context financial research.
Core technical capabilities
Multi-period financial precision
Financial research requires consistency across time.
As of December 23, 2025 GPT-5.2, GPT-5.1, and Opus 4.5 score 53–57% in accuracy on financial tasks involving retrieval and analysis of specific financial information according to Vals AI benchmark.
Most competitive tools rely on these generalist models via ChatGPT-style wrappers and face similar accuracy limitations.
Hudson Labs maintains accuracy across multi-document, multi-period financial queries.
Guidance identification
Subtle guidance cues are frequently missed by generalist AI systems.
Common failure modes include:
- Missing forward-looking statements
- Confusing historical, current, and future tense
- Losing conditional guidance embedded in longer responses
Hudson Labs uses specialized models and task-specific logic to consistently surface forward-looking statements.
“Our old AI provider missed some pretty important guidance statements. It sounds like a little thing but those subtle cues drive our thesis evolution.”
— L/S Analyst, Generalist, $350M AUM
Materiality (relevance) ranking
Correct information is not always useful information.
As document volume increases, irrelevance becomes as costly as hallucination.
Hudson Labs applies proprietary relevance ranking to:
- Reduce noise
- Prioritize information tied directly to the research question
- Preserve full traceability to source
“I cross-referenced everything our PM said about the Salesforce call — it was all the same. You actually get the information you're looking for here. ”
— Technology Lead, Global Equities, $15B AUM
Verbatim, source-faithful quotes
Generalist AI systems frequently:
- Paraphrase while presenting text as quoted
- Surface language that does not exist in source material
Hudson Labs retrieves verbatim excerpts only, with direct linkage to the underlying document.
“If the quotes aren’t real, it completely defeats the purpose.”
— VP, Corporate Strategy, Fortune 500 Company
Why Hudson Labs works where others fail
AI systems architecture over AI models
Reliability is a systems problem
Building effective AI products today is far more about systems, pipelines and architecture than about the model.
Hudson Labs is designed for high-speed, high-accuracy, high-relevance information extraction.
This is achieved through:
- Context engineering optimized for long-horizon tasks
- Topic spans
- Unique pre-processing and meta-tagging
- Proprietary retrieval systems
- Task-specific system design
Rather than relying on a single general-purpose model, Hudson Labs is built as a production-grade research system.
Deep, internal AI expertise
Not AI-adjacent. AI-native.
Artificial intelligence development is difficult. Building reliable AI systems for high-stakes financial use is even harder.
Hudson Labs is one of the few AI-for-finance platforms with credible, in-house AI expertise, rather than outsourced research or thin wrappers around generalist models.
Our CTO and co-founder is the author of a bestselling O’Reilly textbook on Large Language Models, widely used by engineers and researchers building real-world AI systems.
This background informs how Hudson Labs approaches system design, reliability, and long-context reasoning.
LLM-powered software before it was standard
Hudson Labs launched the first fully LLM-powered financial services software application in 2021, when large language models were still largely experimental.
This early work surfaced many of the same challenges that continue to affect AI systems today, including. Many of the system-level solutions developed during that period — including proprietary relevance ranking — remain core components of the platform.
Design principles
Hudson Labs is guided by a small number of operating principles that shape product and system decisions:
- Replace grunt work, not critical thinking
- Narrow and effective systems outperform complex, error-prone ones
- Every output must have a direct and traceable connection to source material
These principles are reflected in how the platform handles accuracy, sourcing, and user trust.
Hudson Labs is built by a team that has spent years confronting the failure modes of large language systems directly — and designing around them.
