Live AI Tools for Equity Research & Compliance Management

Introducing REEF: A Multi-Agentic RAG System for Real-Time Legal & Financial Reasoning

Financial firms are now leveraging live AI tools for equity research and compliance management to gain real-time insights and ensure regulatory adherence. In today’s era of enterprise-ready Generative AI, Retrieval-Augmented Generation (RAG) systems are rapidly becoming foundational components of intelligent applications.

REEF exemplifies the cutting-edge agentic RAG design at the intersection of real-time data processing and autonomous agent orchestration. Built on Pathway, the world’s fastest Python-accessible data processing engine for ETL and RAG, REEF delivers a robust, modular, and context-aware framework tailored for high-stakes legal and financial domains.

Pathway's architecture, designed for real-time AI pipelines over continuously evolving data streams, enables REEF to operate with minimal latency, adaptive reasoning, and dynamic retrieval precision. By integrating Pathway’s Document Store, REEF facilitates hybrid indexing, multi-hop retrieval, and on-the-fly metadata filtering. These features are augmented by autonomous agents capable of decision-making, failure resilience, positioning REEF as a production-grade solution that exemplifies Pathway’s vision for scalable, real-time AI infrastructure.

Code Repository – Complete Setup & Usage Guide

Access the complete source code in this GitHub repository:

SubarnoMaji/reef-chat-pathwayGitHub

Video Demonstration of Live AI Tool for Equity Research and Compliance Management

For a comprehensive walkthrough of REEF’s capabilities, please view our video demonstration:

The Architecture Behind REEF's Intelligence

REEF is an autonomous multi-agent RAG system designed for accurate knowledge extraction and investigative reasoning. At its core, it leverages Pathway's Document Store with a hybrid retrieval mechanism operating on a curated knowledge base. What sets REEF apart is its real-time integration capabilities with online legal and financial resources, creating a dynamic knowledge ecosystem that stays current and relevant.

The system employs hybrid search techniques, combining BM25 with USearch KNN, which experimental data proved to be the most effective retrieval methodology for domain-specific applications. This retrieval strategy is further enhanced through multi-hop retrieval, contextual retrieval preprocessing, and metadata-based filtering, all working in concert to deliver precise information extraction tailored to query intent.

Embedding and Chunking Strategies for Live AI Use Cases

REEF's embedding pipeline represents a carefully engineered component of the system. After extensively evaluating SOTA models ranging from 200M to 7B parameters on the MTEB Leaderboard and domain-specific benchmarks like FinMTEB, Stella-1.5B was selected as the primary embedding model. This choice was driven by Stella-1.5B's superior performance on financial datasets, making it particularly suitable for handling complex financial documents.

For document preprocessing, REEF employs:

1. The Recursive Character Text Splitter to generate 2000-character chunks with 400-character overlaps.
2. Contextual retrieval preprocessing using Haiku by Claude 3 to capture each chunk's position and essence relative to the global context, using prompt caching to reduce computation costs.

3. Metadata extraction to identify key entities and topics from subdocuments.

Metadata Filtering

This multi-layered approach ensures optimal document segmentation while maintaining semantic integrity throughout retrieval.

Advanced Retrieval Strategies for Equity Research & Compliance Management

Important tasks like legal compliance and financial analysis demand precise contextual understanding, which vanilla retrieval often fails to deliver. To address this, we leverage multihop retrieval—an advanced approach that iteratively refines queries, allowing the system to retrieve interdependent documents across multiple reasoning steps. This ensures deeper context alignment and significantly improves retrieval accuracy for complex, multi-layered queries.

Here’s the performance of Contextual Chunking and Metadata Filtering without Multihop:

For comparison, here’s the performance of Contextual Chunking and Metadata Filtering with Multihop, which achieves equal or better performance with a significantly smaller number of chunks retrieved.

The Three Pillars of REEF's Execution Framework

What truly distinguishes REEF is its tri-modal execution framework, offering different levels of autonomous reasoning depending on the query complexity:

1. MORAY: Multi-Agent Orchestrated Retrieval and DAG Synthesis

MORAY functions as an advanced orchestration framework structured around three core components: the Planner, the Scheduler, and the Joiner. Drawing inspiration from classical compiler architecture, it operates on a directed acyclic graph (DAG), representing distinct stages of the workflow.

The Planner generates execution strategies using the Chain of Thought methodology by analyzing available tools, their task definitions, and inter-task dependencies. The Scheduler then constructs and executes the DAG while prioritizing tasks with resolved dependencies. If circular dependencies are detected, the system redirects to the Joiner for replanning, ensuring an acyclic execution framework.

Error handling in MORAY is particularly robust, preserving outputs from completed tasks and passing them to the Joiner for replanning or final response generation. This approach preserves computational resources and delivers more coherent results even when certain subtasks fail.

2. SQUID: Self-Critical Query Understanding via Intelligent Delegation

SQUID stands out with its self-critical task analysis methodology. The Thought-Based Planner generates reasoning-driven thoughts that undergo validation through self-criticism, ensuring mistakes are identified and corrected before execution. This process simulates a step-by-step reasoning approach, giving the LLM adequate time to "think."

Task delegation happens through a network of Specialist Agents equipped with domain-specific tools for use cases like AI equity research or AI compliance management. Execution occurs in parallel under SQUID Manager supervision, maintaining both efficiency and accuracy. Post-execution, the system identifies visualization-appropriate data, sending it to the Graph Tool for graphical representation.

The integration with an analytics module enables real-time monitoring of token consumption, execution latency, and resource utilization—a crucial feature for production deployments where cost optimization matters.

3. CArP: Code Architect for Planning

CArP (Code Architect for Planning) processes queries without auto-routing, generating Python code by leveraging tool descriptions provided in docstring format. This approach presents a novel way to orchestrate complex workflows through code generation rather than explicit planning and execution.

When combined with the extensive set of tools provided by REEF, CArP enables seamless tool chaining through programmatic execution, minimizing communication overhead between components.

System Resilience Through Intelligent Design

REEF incorporates several engineering techniques to enhance system robustness:

1. Prompt Caching: Avoids redundant computation by referencing previously stored context instead of regenerating it, resulting in reduced computational costs and faster response times. In practice you can rely on (a) your model-provider’s own cache or (b) if running the pipeline on Pathway, enable durable on-disk/S3 caching with pw.udfs.DiskCache backed by Pathway Persistence for enterprise-grade fault-tolerance—both routes slash token cost and latency.
2. Error Resilience: Integrates robust guardrails and dynamic tool management, with replanning capabilities that formulate alternative execution plans when primary tools fail
3. Fallback Mechanisms: If Google Serper Search encounters issues, the system sequentially falls back to Tavily Search or JINA Search. Similarly, Yahoo Finance failures trigger the utilization of Polygon or Alpha Vantage APIs
4. Human-in-the-Loop Integration: The system can assess query ambiguity and prompt users for clarification when needed, ensuring accurate responses to complex or underspecified queries

Live AI in Action: Use Case Videos

Role of Live AI in Market Valuation Research:

Role of Live AI in Financial Research:

Domain-Specific Agent Ecosystem for Finance and Legal

REEF's versatility comes from its specialized agent ecosystem, with distinct agents handling different domains and tasks:

Legal Domain Agents

• IPR Agent: Identifies applicable IP rights for novel ideas, determining whether patent or trademark protection is appropriate and performing novelty searches using Google and USPTO databases
• Compliance Checker Agent: Automates legal and regulatory compliance evaluation by comparing document sections with relevant laws and statutes, offering both "Strict" and "Lenient" evaluation methods
• Court Listener Legal Agent: Retrieves information about acts or judgments using the CourtListener API, providing access to U.S. court-related legal information

Financial Domain Agents

• Valuation and Competitor Analysis Agent: Analyzes market opportunities, compares company valuations, and provides strategic recommendations by leveraging search tools and financial data APIs
• Equity Research Agent: Creates industry-standard equity research reports structured according to Bloomberg and CFA Institute guidelines, complete with investment rationale and target price analysis
• Technical Indicator Analyst: Combines various technical analysis tools (SMA, EMA, Stochastic Oscillator, RSI, ADX) using Alpha Vantage data to produce market insights
• Financial News Agent: Analyzes sentiment for stock tickers and retrieves categorized news articles using Alpha Vantage and Finhub APIs

General Purpose Agents

• Graphing Agent: Automatically generates visualizations from user queries and context, providing detailed reasoning for visualization choices
• Retriever Agent: Gathers information through configurable multi-hop retrieval, refining queries based on insights obtained during the retrieval process

Performance Metrics

The experimental evaluation of REEF reveals impressive performance characteristics:

1. Retrieval Accuracy: The combination of BM25 with USearch KNN achieved a cosine similarity of 0.497 and word overlap of 0.639, outperforming other retrieval configurations
2. Framework Performance: SQUID demonstrated superior accuracy on both CUAD (0.913) and Form 10-K (0.886) datasets compared to MORAY and CArP
3. Operational Efficiency: For simple queries, the system responds within seconds, while complex document generation completes in 3-5 minutes—representing a dramatic reduction from the weeks often required for manual analysis
4. Cost Efficiency: The system operates at remarkably low costs, with equity research reports generated for approximately $1.89, financial statement analyzes for $1.15, and market analyzes for just $0.03

Enterprise Use Cases & Time-to-Value ROI

REEF's capabilities translate into powerful real-world applications:

1. Financial Statement Analysis: Generates comprehensive analyzes of 10-K data within 4 minutes, compared to the typical 2-week timeframe for manual analysis
2. Equity Research Reports: Creates professional equity research reports in 5 minutes instead of the 3-5 weeks typically required by financial analysts
3. Compliance Checks: Examines contracts for clause violations in approximately 3 minutes, reducing a process that traditionally takes 1 to 3 days
4. IP Identification: Searches for existing patents or trademarks based on product descriptions, completing in about 1 minute what typically requires 2-5 weeks of legal research.
5. ESG Benchmarking: Compares company ESG performance with competitors within 3 minutes, drastically accelerating a process that usually spans 2 to 4 weeks
6. Market Valuation Analyst : Evaluates market valuation of companies with detailed reports within 2-3 minutes instead of 3-5 weeks typically required by financial analysts.
7. Legal Case Research : Analyze legal documents quickly—Simplifies legal case research processes, which usually take weeks to analyze.

The table below compares traditional manual workflows in legal and financial domains with REEF’s automated performance. It highlights the types of tasks handled, required inputs, system outputs, and the dramatic reduction in time and cost. This showcases REEF’s ability to transform complex compliance and analysis tasks into streamlined, scalable, and cost-effective processes powered by live AI.

Future Technical Directions

The modular architecture of REEF provides a foundation for further technical evolution:

1. Domain Expansion: The system can be extended to healthcare, education, and hospitality domains by integrating specialized agents and domain-specific knowledge bases
2. Enhanced Retrieval: Future improvements may include advanced retrieval methods such as FLARE, PlanXRAG, RAPTOR, and MetaRAG, which utilize hierarchical tree structures and metacognitive regulation techniques
3. Embedding Optimization: Fine-tuning embedding models with domain-specific datasets could further enhance comprehension and query accuracy in specialized fields
4. Advanced Parsing Strategies: Implementation of dependency, semantic, neural, and probabilistic parsing methods could enable even more precise data interpretation from complex documents

The Technical Innovation Advantage

REEF marks a major advancement in RAG technology by effectively overcoming several key limitations of traditional systems. One of its standout features is dynamic data integration, unlike conventional RAG setups that rely on static knowledge bases, REEF supports continuous data ingestion using Pathway’s Document Store and built-in connectors. Its multi-agent architecture enables autonomous operation, allowing the system to independently retrieve and analyze information with minimal human input.

Additionally, REEF achieves a high level of contextual understanding by combining metadata filtering with hybrid retrieval techniques, delivering retrieval accuracy on par with state-of-the-art solutions. Importantly, REEF also prioritizes responsible AI practices by integrating NVIDIA’s NeMo framework, which enforces guardrails to prevent the execution of inappropriate instructions, the generation of explicit content, and attempts to bypass compliance standards.

Testimonials from Leaders in Finance and Legal

Conclusion: A New Paradigm with Live AI in Information Systems

By integrating live AI tools for equity research and compliance management into their workflows, organizations can achieve faster analysis and more reliable compliance checks. This multi-agent RAG approach heralds a new paradigm in which finance and compliance professionals collaborate with AI in real-time.

REEF chat represents a technical watershed moment in the development of agentic RAG systems. By combining Pathway's powerful document-handling capabilities with a sophisticated multi-agent architecture, it delivers unprecedented performance in knowledge extraction and reasoning across legal and financial domains.

The system's ability to reduce complex analysis tasks from weeks to minutes while maintaining high accuracy demonstrates the transformative potential of well-engineered AI systems. As organizations continue to grapple with the challenge of extracting actionable insights from ever-growing document repositories, solutions like REEF point the way toward a future where intelligent systems can autonomously navigate complex information landscapes with human-like understanding and reasoning capabilities.

For developers and organizations looking to implement advanced RAG architectures, REEF provides a compelling blueprint that balances technical sophistication with practical utility, pushing the boundaries of what's possible in AI-powered information systems.

If you are interested in diving deeper into the topic, here are some good references to get started with Pathway:

• Pathway Developer Documentation
• Pathway's Ready-to-run App Templates
• End-to-end Real-time RAG app with Pathway
• Discord Community
• Pathway's LLM Tooling
• Power and Deploy RAG Agent Tools with Pathway

Beyond the key resources mentioned above, for more information on the concepts and frameworks that we have used, these are other options to get started:

1. Unstructured Documentation
2. Chunking Strategies
3. Anthropic’s Contextual Retrieval
4. LLM Compiler (arXiv Paper)
5. LangGraph Documentation

References

Authors

• Ashish Patwa
• Amrit Lal Singh
• Subarno Maji
• Bhavesh Pabnani
• Rwik Dey
• Udit Mohan Pradhan
• Raghuveer V