Why We're Building Nyquist Labs
The thesis is simple: one proprietary pattern recognition engine that works across every domain. Here's why multi-agent AI with anti-groupthink is the future of signal detection.
Full report coming soonDeveloping novel anti-groupthink mechanisms and domain-agnostic pattern recognition architectures. Applied research with commercial validation.
Autonomous scout agents ingesting from RSS, APIs, GDELT, Reddit, and YouTube transcripts.
Cross-source verification and sentiment analysis pipeline for multi-perspective validation.
Boss agent synthesis with board voting mechanism for multi-perspective consensus.
Developing novel anti-groupthink mechanisms in multi-agent systems to ensure independent reasoning and reduce correlated failure modes across heterogeneous model architectures.
Quantifying bias propagation across heterogeneous AI models to understand how systematic errors compound through multi-stage inference pipelines and agent hierarchies.
Creating domain-agnostic pattern recognition architectures that transfer across verticals without retraining, validated through deployment in finance, disaster response, and education.
Validating autonomous agent coordination in high-noise environments where signal-to-noise ratios are low and reliable ground truth is delayed or unavailable.
Nyquist Labs is a research institution developing proprietary multi-agent pattern recognition systems. Our core technology uses multiple competing AI agents with built-in anti-groupthink mechanisms to deliver insights no single model can achieve.
Multiple LLMs working in concert with anti-groupthink strategies, ensuring diverse perspectives on every signal detected.
Scout agents autonomously gather data from 10+ sources, identifying patterns before they become obvious to the broader research community.
Built-in bias detection and multi-perspective verification ensures every finding is validated before surfacing.
Our approach combines autonomous experimentation with rigorous validation across heterogeneous agent architectures.
PM Agent formulates testable hypotheses from ingested data patterns and cross-domain signal correlation.
Swarm agents execute experiments in parallel across isolated environments with controlled variables.
Multiple LLMs validate findings independently, with disagreements flagged for human review.
Board voting mechanism synthesizes outcomes using weighted consensus across heterogeneous models.
System learns from experimental outcomes, updating priors and refining agent coordination protocols.
Tracking our multi-year research program from foundational architecture to cross-domain validation and publication.
Research entity established, institutional infrastructure configured.
Autonomous scout agents ingesting from RSS, APIs, web scrapers, GDELT, Reddit, and YouTube transcripts.
Migrated to production-grade SQLite/PostgreSQL with SQLAlchemy ORM for research data persistence.
Multi-source fact-checking and cross-referencing system for research integrity.
Developing sentiment analysis capabilities across all ingested data streams.
Automated detection and flagging of low-confidence or biased signals.
Central agent synthesizing scout + validator outputs into actionable research findings.
Multi-perspective decision system with weighted consensus across heterogeneous models.
Controlled study: 50+ documented experiments with tracked accuracy and false positive rates.
Agricultural weather/risk prediction study validating cross-domain architecture transfer.
Engage 1-2 institutional research partners for external validation studies.
Prove architecture transfers across domains. Apply to education or corporate intelligence research.
SBIR, NSF, FEMA, DHS grant submissions backed by validated research results.
The same core pattern recognition architecture validated across multiple high-impact research domains.
Validating multi-agent pattern recognition in high-noise financial environments. Testing anti-groupthink mechanisms against historical market data.
Applying core architecture to agricultural risk prediction. Domain transfer validation study with institutional partners in insurance and supply chain sectors.
Investigating knowledge gap detection through multi-perspective agent analysis. Research into personalized learning pathway generation.
Multi-source sentiment synthesis research for enterprise intelligence. Investigating cross-signal aggregation in competitive analysis domains.
We propose a novel framework for mitigating correlated failure modes in multi-agent LLM systems through adversarial deliberation protocols and weighted dissent mechanisms.
A technical report describing our architecture for transferable pattern recognition across financial, environmental, and educational domains without model retraining.
Preliminary findings on coordination protocols for autonomous agent swarms operating in environments with low signal-to-noise ratios and delayed ground truth.
Documenting the development of a multi-agent intelligence framework from foundational research through domain validation.
The thesis is simple: one proprietary pattern recognition engine that works across every domain. Here's why multi-agent AI with anti-groupthink is the future of signal detection.
Full report coming soonHow we built scout agents that autonomously pull from 10+ data sources including RSS, APIs, GDELT, Reddit, and YouTube transcripts.
Full report coming soonSingle AI models have blind spots. Our multi-agent approach uses competing perspectives to catch what any individual model would miss.
Full report coming soonThe same engine that detects market signals can predict agricultural risk, optimize supply chains, and power adaptive education research.
Full report coming soonWhether you're a research institution, funding partner, or pilot program candidate — we'd love to connect.