AI-Assisted Research Disclosure
Effective 2026-08-21 · Version 2026-08-21
Princeton Analytica reports are produced using a multi-stage pipeline that combines artificial intelligence models with deterministic calculation, current web research, and quality controls. This page explains how AI is used in that process, what its limitations are, and how you can raise questions about a specific report.
1. 1. Overview of the Pipeline
A typical report moves through several stages: intake and scoping (based on your order and any diagnostic answers or uploaded materials), research and source gathering, drafting and synthesis, deterministic calculation where applicable, fact-checking, and quality review, before final assembly and delivery as a PDF. Each stage is governed by human-designed controls and prompts rather than fully unsupervised AI operation.
2. 2. Where AI Is Used
AI models, including models accessed through Amazon Bedrock, are used to conduct and summarize web-based research, draft narrative sections of your report, identify and organize relevant sources, and support fact-checking and internal quality review steps.
3. 3. What Is Deterministic
Where a report includes numerical outputs, such as calculations, aggregations, or formula-based figures, those computations are performed using deterministic (non-AI) calculation logic rather than being generated freeform by an AI model. This is intended to reduce the risk of AI-generated arithmetic errors in quantitative sections.
4. 4. Source Citation and Registry Integrity
Our pipeline maintains a source registry that tracks the origin of factual claims and citations used in a report. This is intended to support traceability between statements in a report and the sources used to generate them, and to reduce the risk of fabricated or unverifiable citations.
5. 5. Fact-Checking
Before delivery, reports pass through an automated fact-checking stage designed to compare key claims against their cited sources and flag inconsistencies for correction. Fact-checking reduces, but does not eliminate, the risk of factual error.
6. 6. Labeling of Fact, Derived, Estimate, and Forecast Content
Where feasible, our reports distinguish between different types of content: facts drawn directly from a cited source, figures derived through calculation from underlying data, estimates based on incomplete or proxy data, and forward-looking forecasts or projections. This labeling is intended to help you understand the basis and confidence level of a given statement.
7. 7. Known Limitations
AI models can misinterpret source material, rely on outdated or incomplete information, or generate plausible-sounding but incorrect statements. Web research reflects information available at the time the report was produced and may not capture the most recent developments. Despite fact-checking and quality review, no report should be treated as guaranteed to be complete, accurate, or current.
8. 8. Human Oversight and Review
Our research pipeline, prompts, and quality controls are designed and configured by humans, and we maintain the ability to perform human review of reports, including in response to a customer-reported issue. Human review of an individual report prior to delivery is not guaranteed for every order but is available as part of our error-reporting and quality process described below.
9. 9. Reporting an Error
If you believe a delivered report contains an error, omission, or unsupported claim, contact support@princetonanalytica.com with the report name, order number, and a description of the issue. We will investigate and, where appropriate, issue a corrected report consistent with our Refund Policy.
10. 10. Not a Substitute for Professional Advice
Reports are informational business intelligence outputs and do not constitute legal, medical, tax, investment, or other regulated professional advice. You should not rely on a report as a substitute for consultation with a qualified professional before making decisions with legal, financial, medical, or regulatory consequences.