Reach out to 10X CPA Inc. with any questions regarding how we can leverage CODEX CPA® for your GenAI CPA needs. We design and research ways to produce generalized niche area GEN AI models for Certified Public Accountants by Certified Public Accountants. This entails sections such as Financial Reporting, Management Accounting, Audit & Assurance, Advisory, and Taxation. We also discuss Auditable AI and ways to ensure data integrity, reliability, replication, and interpretability. We are developing streamlined Research and Development (R&D) services & build and implementation (B&I) for Certified Public Accounting (CPA) Firms. Firms interested in having us carry out research options, cost benefit model analysis, design, and build & Implement should contact us. If you need to discuss how to rely on AI models to prevent concept bias or increase investor confidence we specialize in brainstorming these subjects. We are targeting small to mid-sized CPA firms that need to make sense of all the data in the pipeline, data ingestions, and devise and design tailored solutions for business specific purposes with domain specific GenAI for CPAs. If you need to discuss feasibility of a specific project undertaking we can guide you through the process as 10X CPA Inc. has already started.
A brief overview of concepts covered in Generalized Artificial Intelligence (GenAI) or/and how it’s developed and can be used for sophisticated build cases for Certified Public Accounting (CPA) Firms. Theory building and views involve:
Symbolic AI for outlining processes //
Machine Learning for data engineering ingestion //
Deep Learning on documents or financials //
Probabilistic Methods for sampling and higher percentage coverage for selections //
Optimization Techniques for Financial Analysis and Controller Ratios //
Game Theory Modeling Dilemmas for decision making and firm competition //
General Equilibria for break-even analysis (Marginal Revenue = Marginal Cost) //
Economic Analysis (Supply & Demand, Average Total Costs, Average Variable Costs, & Average Costs) //
Graphing (For Data Science and Data Views) //
General Outline of how-to:
Data Collection (Source diversity, preprocessing, and quality filtering) //
Pretraining (Unsupervised learning, transformer arch., and scaling laws) //
Fine-tuning (Supervised fine-tuning, reinforced learning, and human feedback loops) //
Evaluation (Benchmarks, red teaming, and user feedback) //
Deployment and Monitoring (Guardrails and Iterative Updates) //
Research Direction (Alignments, Interpretability, Robustness, and multimodal learning) //
* General AI Models are built and researched with current UpToDate systems and all follow strict ethical, moral, & governance principles
