Financial engineering · Private AI
Wizard Invest is building a specialized financial research platform that brings together structured U.S. company data, quantitative methods, and locally controlled or privately hosted AI.
The goal is practical: make advanced corporate and equity analysis more accessible, transparent, and reproducible.
01 / The platform
Advanced financial analysis requires more than access to a language model. Teams need consistent company data, meaningful comparables, documented calculations, and a reliable way to work with their own information.
Wizard Invest brings these elements into a connected research environment. Our approach combines financial engineering with AI-assisted workflows so users can follow the evidence, review the assumptions, and reproduce the analysis.
For financial firms, independent analysts, operating companies, and academic researchers.
Normalized corporate fundamentals, market information, and derived research variables form the foundation for consistent analysis of U.S. companies. Source references, reporting periods, and documented transformations make the underlying information easier to evaluate.
Compare companies using documented financial characteristics and research-driven peer definitions. Explore valuation, profitability, growth, leverage, and risk with methods that expose the inputs and assumptions behind each comparison.
Translate research ideas into explicit screening and portfolio rules. Evaluate their historical behavior using dated inputs, documented methods, and controls designed to address look-ahead and survivorship bias.
Connect authorized internal reports, budgets, and planning documents with relevant public-company information. The planned private research environment brings company-specific context into a source-linked analytical workflow.
Build review into the research process. The planned Continuous Atomic Financial Audit layer will test narrowly defined data, calculation, timing, and information-boundary checks, with uncertain cases routed for further verification.
02 / Platform design
The platform is designed to keep each analytical step connected to its evidence.
Define the companies, periods, comparisons, and assumptions relevant to the research.
Bring together authorized financial data and relevant documents, preserving sources and reporting periods.
Execute financial and statistical calculations through versioned analytical functions.
Present the findings with sources, assumptions, and a research path that can be inspected and repeated.
This separation makes the interface easier to use while keeping financial methodology visible.
03 / Private deployment
Organizations differ in their computing resources and information-control needs. Wizard Invest is developing two deployment paths around the same financial-data and quantitative foundation.
Your infrastructure
Run the analytical environment on your own infrastructure or private cloud. Keep proprietary documents within an environment you control, while connecting to authorized financial data and research functions.
Dedicated environment
Use a dedicated U.S.-hosted environment when maintaining local model infrastructure is not practical. The proposed design separates customer information and supports document transfer only with customer authorization.
Planned research interfaces, notebooks, and benchmarks will extend access to independent and academic work.
04 / Illustrative workflow
An operating company wants to understand its margins, leverage, and capital-allocation alternatives in relation to relevant public-company peers. Its team brings authorized internal reports and planning assumptions into a private research environment.
Wizard Invest's proposed workflow combines that context with a documented peer set, calculates comparable measures, and explains the findings with references to the underlying inputs. The team can inspect the peer choices, revisit assumptions, and repeat the analysis as its questions evolve.
How does our financial position compare with relevant peers?
Reports
Budgets
Planning assumptions
Fundamentals
Reporting periods
Peer definitions
Trace the inputs. Review the assumptions.
Repeat the research.
Scenario illustration — no customer data or investment results are shown.
05 / Research foundation
Wizard Invest builds on doctoral research in statistical relative valuation, endogenous peer classification, portfolio construction, and historical strategy evaluation. That work provides both the financial methodology and the practical experience needed to turn research questions into documented analytical workflows.
An existing research database includes selected corporate-financial and market histories extending more than twenty years. Coverage varies by company and period.
Research code supports data preparation, peer comparison, valuation, portfolio construction, and historical evaluation.
A demonstrated local-model environment and an internally tested software-audit component support the next stage of financial-platform integration.
Our development program will extend this foundation through documented methods, evaluation benchmarks, and reproducible research materials.
06 / Founder & research lead
John is a financial engineer and quantitative researcher with a Ph.D. and M.S. in Financial Engineering from Stevens Institute of Technology and a bachelor's degree in Information Systems from the University of São Paulo.
His work connects statistical relative valuation, corporate-data systems, portfolio research, and software development. He founded Wizard Invest LLC to develop a specialized analytical platform that brings those disciplines together for professional and research use.
Financial Engineering · Quantitative Research · Software & Data Systems
07 / Development
Established
Financial research, structured data, quantitative code, local-model execution, and an internally tested software-audit component.
Development focus
Connect the financial-data foundation, private-document retrieval, deterministic methods, and source-linked explanations.
Next milestones
Evaluate end-to-end workflows, develop private deployment options, gather structured professional feedback, and expand research documentation and interfaces.
Start a conversation
We welcome conversations with financial professionals, operating companies, and researchers interested in private AI, corporate analysis, and reproducible financial methods.
Tell us about your research workflow, the information you need to connect, and the questions your team is trying to answer.
Contact the founder [email protected]