From Lab to Enterprise
Our research architecture operates across three distinct layers, ensuring that theoretical breakthroughs become measurable business outcomes.
Foundational Research
Advancing the core capabilities of artificial intelligence.
Applied Research
Translating capabilities into domain-specific paradigms.
Production Systems
Operationalized research embedded into enterprise products.
Editorial Curation
Agentic Retrieval-Augmented Generation in High-Compliance Environments
An architectural deep-dive into maintaining strict data governance while deploying autonomous agents across siloed enterprise databases. We evaluate our novel routing mechanism against standard RAG baselines.
Dynamic Task Planning for ERP Automation
Mitigating Hallucinations via Multi-Agent Debate
The Economics of Small Specialized Models
Research Archive
Explore our complete library of publications, technical reports, and case studies.
Scaling Laws for Enterprise Retrieval-Augmented Generation
An empirical study on the relationship between embedding dimensions, chunk size, and retrieval accuracy across 50 enterprise datasets.
Federated Learning for Cross-Border Financial Compliance
How to train predictive risk models across multiple sovereign jurisdictions without transferring PII or violating GDPR.
Enterprise Task Benchmarks
Generic LLM benchmarks (MMLU, HumanEval) don't reflect real business workflows. We evaluate models on actual enterprise tasks.
Experiment Gallery
We publish our methodology, results, and limitations. Transparency in applied research builds trust in production systems.
Multi-Agent Debate for Hallucination Mitigation
Objective
Determine if a 3-agent debate structure (Generator, Critic, Resolver) reduces hallucinations in highly technical legal contract reviews compared to zero-shot generation.
Methodology
A 10,000-document subset of the EDGAR corpus was used. We deployed GPT-4o as the Generator and Claude 3.5 Sonnet as the Critic to avoid inherited model bias.
Dataset
Results & Evaluation
Hallucinations reduced by 87.4%. However, latency increased by 3.2x and token cost doubled.
Human-in-the-loop expert review on a 5% sample + automated fact-checking against a deterministic graph.
Limitations
The cost/latency tradeoff is unacceptable for real-time applications. The approach is only viable for asynchronous, high-stakes background processing.
Research Knowledge Graph
Explore the connective tissue of our organization. See exactly how an abstract research paper propagates into a measurable business outcome.
Technology Transfer
Watch how our foundational research directly matures into our core enterprise products.
Research That Builds Products
Our papers do not collect dust. We ensure every theoretical advance is aggressively evaluated, optimized, and pushed into the CerebroHive product suite.
Dynamic Task Planning for ERP Automation
Knowledge Hub
Powers semantic task routing.
AgentOS
Core reasoning engine.
Quantiva ERP
Automated action execution.
Research by Industry
We conduct targeted research to solve the specific bottlenecks holding back AI adoption in highly regulated sectors.
Healthcare & Life Sciences
AI research focused on HIPAA-compliant agentic workflows for clinical documentation.
Financial Services
Low-latency reasoning models for fraud detection and automated compliance auditing.
Manufacturing & Supply Chain
Multi-agent systems for predictive maintenance and dynamic inventory routing.
Building in the Open
We open-source our foundational tooling, datasets, and evaluation frameworks to accelerate the entire AI ecosystem.
cerebro-rag
The open-source core of our Enterprise RAG pipeline. Includes semantic chunkers, vector hybrid search, and citation grounding.
agent-eval-framework
A suite for evaluating autonomous agents on multi-step reasoning tasks without human intervention.
edgar-corpus-v2
A cleaned, chunked, and vectorized dataset of 5 years of SEC filings, optimized for LLM financial reasoning.
Build the Future.
Access the same tools our researchers use. From interactive API playgrounds to verified prompt libraries, everything you need to operationalize AI is here.
API Explorer
Interactive playground for CerebroOS endpoints.
SDK Documentation
Native libraries for Python, Node.js, and Go.
Reference Architectures
Production-grade templates for deploying agent swarms.
Prompt Library
Version-controlled, highly optimized system prompts.
Innovation Roadmap
How our research translates from theoretical concepts into validated enterprise platform integrations.
Current Research
- Agentic Retrieval-Augmented Generation
- Multi-Agent Planning Protocols
- Knowledge Graph Construction
Validation
- Deterministic Fact-Checking
- Cross-Domain Federation
- Edge Deployment Models
Enterprise Adoption
- AgentOS Scale out
- Quantiva ERP Auto-remediation
Platform Integration
- Autonomous Organizations
- World Simulation Models
Research Authors
The engineers and scientists building the CerebroHive platform.