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BenchmarkIQ

by KPI Partners

 

 

Unlock Financial Document Intelligence with Agentic RAG 

 

 

 

 

Overview
 
BenchmarkIQ is a GenAI accelerator for financial document intelligence. Built on an enterprise-grade Agentic Retrieval-Augmented Generation (RAG) architecture, BenchmarkIQ processes financial documents such as SEC 10-K filings and financial reports to deliver accurate, citation-backed question answering, section summarization, and cross-document comparison. 

By combining structured ingestion, semantic chunking, hybrid retrieval, and GPT-powered synthesis, BenchmarkIQ helps teams extract reliable insights from complex financial documents while maintaining source attribution and confidence controls.
 
 

 

Thumbnail_ BenchmarkIQ 1

 

Business


Use Cases

1 Citation-backed question answering across SEC filings and financial reports
2 Section-level summarization, such as MD&A, Risk Factors, and financial notes
3 Cross-document and cross-company comparison
4 Financial research acceleration for analysts and business teams
5 Compliance and audit support through traceable document references
6 Executive-ready summaries from lengthy financial documents
7 Future integration with CRM systems and databases

Underlying Technology

User Interface
 Streamlit-based user interface for document interaction and Q&A.  
Backend Orchestration
LangGraph workflow engine for intent detection, query planning, retrieval, evaluation, refinement, summarization, and synthesis.  
LLM Layer
 Azure OpenAI GPT-4o for grounded answer generation.
Embedding Layer
Text-embedding-3-large for high-accuracy semantic representations optimized for financial and legal text.  
Vector Store
Azure AI Search for vector similarity search, metadata filtering, and semantic ranking. 
Document Processing
Azure Document Intelligence and Docling for semantic, document-aware chunking that preserves sections, tables, and logical structure. 
Hosting
Azure App Service for scalable, stateless deployment. 
Guardrails
Confidence scoring, source attribution, fallback handling, and optional LLM evaluation. 

Standard Enterprise

AI Engagement Model

 

AI Engagement Model

 

Industry-Wide Applicability  
 
  • Financial services teams reviewing SEC filings and annual reports   
  • Insurance and banking teams analyzing financial disclosures 
  • Equity research and investment teams comparing companies and filings 
  • Risk, compliance, and audit teams requiring traceable document intelligence 
  • Enterprises managing large volumes of financial, legal, or regulatory documents 
  • Business teams that need structured insights from unstructured PDF reports 
 
 

 

Group

 

Data Platform Migration for Modern Analytics

 

KPI DataBridge Suite is designed to help enterprises modernize their data and analytics infrastructure across:
 
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Explore Real-World
Data Platform Migration Case Studies

 


 

BI Platform Migration

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3 Snowflake Cost Optimization App
4 Snowflake Security Automation App
5 Snowflake Data Governance App
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1 EDW (Snowflake, Redshift, Oracle/ADWC, Teradata, and Netezza) to Microsoft Fabric Migration Accelerator

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