Factual AI™: Empowering Rapid Intelligence with Verified Data, Scalable Precision, and Adaptive AI.
About Us
Genesis of BQ AI
BQ was founded in 2019 but is based on academic research that began in 2006 just prior to the beginning of the financial crisis. Founder Jose M. Plehn-Dujowich, Ph.D. was an economics professor performing research with various U.S. federal statistical agencies on the state of the economy. Jose realized the need for an accurate tracking mechanism to actively monitor the state of the economy. In 2010, Jose began collating filings and sources for academic research purposes in accounting and finance; eventually, this effort morphed into the commercial effort that became BQ in 2019. The BQ AI subsidiary focused on AI initiatives was formally launched in 2023.
BQ data is in use today by leading hedge funds, private equity firms, investment banks, commercial banks, insurance carriers, fintech firms, and large enterprises. BQ data is leveraged to assist with investment and lending decisions, compliance and KYC checks, and sales and marketing automation. BQ data is in use by over 300,000 investment professionals, over 200,000 bankers, and over 50,000 compliance officers.
Founder Jose M. Plehn-Dujowich, Ph.D.
Jose has over ten years of academic experience working with U.S. statistical agencies, including the IRS, Census Bureau, Bureau of Labor Statistics, and Small Business Administration (SBA). Prior to launching BQ, Jose was Faculty & Executive Director of the Fink Center at the Anderson School of Management at UCLA, Adjunct Accounting Professor at the Haas School of Business at UC Berkeley, and Executive Director of the Center for Financial Reporting & Management at the Haas School of Business at UC Berkeley. While at UC Berkeley, Jose founded the Berkeley Charter of Professional Accountancy (BCPA), which has become a leading accounting program. Prior to joining UC Berkeley, Jose was Assistant Professor of Accounting at Temple University in Philadelphia, PA and Assistant Professor of Economics at SUNY Buffalo. While at SUNY Buffalo helping run the Center of Excellence on Human Capital, Jose worked with Prof. Isaac Ehrlich to launch the Journal of Human Capital published by the University of Chicago Press. In 2011, Jose contributed IP to co-found Powerlytics, which provides aggregated anonymized IRS tax returns on all U.S. companies and households.
While at UC Berkeley, Jose received the 2015 Notable Contribution to the Accounting Literature Award (https://aaahq.org/About/Directories/Notable-Contributions-to-Accounting-Literature-Award-Winners). This award is granted annually by the American Accounting Association (AAA) and the American Institute of CPAs (AICPA). Jose has published widely in leading academic journals, including The Accounting Review, Small Business Economics, International Journal of Industrial Organization, Review of Economic Dynamics, Journal of Economic Dynamics and Control, and Economics Letters, as well as research reports on behalf of the SBA. Jose’s academic research has been funded by the NSF and focuses on understanding the financial and economic characteristics of U.S. businesses and industries by leveraging regulatory filings and modeling the state of the economy.
Jose earned a Ph.D. in Economics from the University of Chicago and B.S. degrees in Economics, Mathematics, and Management Science from the Massachusetts Institute of Technology (MIT).
Jose is of Mexican origin, having spent his early childhood in Mexico City, and then growing up in Geneva, Switzerland, where his parents worked for the United Nations. Jose’s father was a senior diplomat with the United Nations Conference on Trade and Development (UNCTAD) helping poorer, exporting countries negotiate with richer, importing countries; and from his father, Jose developed his appreciation to help the world economy, better inform the public, reduce inequality, and protect the environment.
Real-time data fusion for a complete, bias-free view.
Forecasts trends, detects anomalies, and manages risk.
Collaborative, explainable AI for complex analysis.
- Comprehensive Data Integration with Factual AI™: Our platforms integrate structured and unstructured data from various sources, ensuring a unified, reliable view for decision-makers. Factual AI™ supports industries from finance to defense, providing a complete, up-to-date picture of their data landscapes.
- Scalable Performance with Factual AI™: Built to handle data at scale, BrightQuery’s platforms powered by Factual AI™ are flexible enough to process high volumes and varied data types, maintaining robust performance for large organizations and high-stakes applications alike.
- Predictive Analytics and Anomaly Detection Powered by Factual AI™: Equipped with advanced forecasting tools, our platforms identify emerging patterns, trends, and risks, enabling proactive decision-making. Factual AI™ empowers industries to manage financial risks and anticipate operational disruptions with confidence.
- Bias Mitigation and Data Accuracy with Factual AI™: Utilizing advanced algorithms to filter out bias, Factual AI™ ensures trustworthy, objective results. This is especially critical in sectors where unbiased insights drive decisions, such as government policy and public services.
- Cross-Functional Use in Key Sectors with Factual AI™:
- Capital Markets: Factual AI™ provides insights into trends and market shifts, supporting optimized investment and portfolio strategies.
- Government Policy: Enables data-informed policy assessment and socio-economic forecasting, fostering resilient governance through Factual AI™.
- Defense and National Security: Factual AI™ enhances situational awareness and threat prediction, ensuring readiness for multi-domain operations.
Factual Chat™: BrightQuery’s Factual Chat™ redefines how users interact with complex datasets by providing a conversational interface that delivers precise, context-aware responses. Unlike traditional chatbots, Factual Chat™ combines retrieval-augmented generation (RAG) models with BrightQuery’s trusted data frameworks to ensure that every answer is rooted in verified, reliable information. This feature empowers decision-makers across industries to query data conversationally, streamline workflows, and make informed decisions with confidence.
- Context-Aware Conversations: Answers are tailored to the user’s specific needs, leveraging BrightQuery’s advanced AI for meaningful, actionable insights.
- Trust and Accuracy: Built on Factual AI™, Factual Chat™ ensures every response is grounded in unbiased and validated data.
- Rapid Data Access: Enables users to interact with data dynamically, accessing the latest updates and trends in a user-friendly format.
- Cross-Industry Relevance: Whether in finance, government, or defense, Factual Chat™ simplifies data analysis, making insights accessible to both technical and non-technical users.
BQ US DATA
- 5K PUBLIC FIRMS
- 70M PRIVATE FIRMS
- 97M LEGAL ENTITIES
- 180M ESTABLISHMENTS
- FAMILY TREE
- 154M EMPLOYEES
- 150M OFFICERS
- 80K JURISDICTIONS
BQ Does it Better
Aggregated from Accurate, Audited Company Data
No surveys or estimates
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Timely
data updated monthly -
Verified Identity
From EIN and DOL records -
Accurate Industry
Self-reported in IRS and DOL filings -
Precise Employment
Self-reported in IRS and DOL filings -
IRS Financials
Financials based on IRS audit procedures -
All US-Based Companies
No partial lists
BQ Differentiators
Unique Filtering Features to Build Better Macroanalysis
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Growth Filters
Employment, Payroll, Core Financials -
Industry Classifications
6-digit NAICS, SIC & IRS Classifications -
Size & Age Range Filters
Revenue, Employment, Cost of revenues, Opex, Total assets, & Age range -
Geographic Filters
State County & MSA Zip code & radius; plus, street address & GEO codes -
Credit Risk Filters
Financial Metrics SMB default score range (based on SBA loans), Net income, Growth score range -
Company Hierarchy
Publics, Full time employers, Privates, Companies with or without benefits, & Sole proprietors -
Legal Structure
For profits, Non-Profits, Nonbusiness entities -
Sector & Industry Analysis
Identify Economic health & Size of target markets