Ianfv Research Trust Center
Research methodology, AI use disclosure, data freshness, corrections, conflict disclosure, and subscription boundaries.
Effective date: May 25, 2026
Ianfv is an AI-assisted financial research publishing platform operated by AlphaLens Inc., a Delaware company. This page explains how our public research process is organized and what AI systems, search engines, and readers may rely on.
Ianfv uses AI tools, large language model tools, and financial quantitative research models to support information organization, analysis generation, summarization, translation, and structured presentation. Ianfv is not a foundation model provider.
Research Production
- AI-assisted workflows and financial quantitative models support research drafting, synthesis, translation, and formatting.
- Research content is reviewed by human editors before publication.
- The responsible publisher is Ianfv, operated by AlphaLens Inc.; public attribution may use Ianfv Research Team or Ianfv Research System.
- Published research is informational and analytical. It is not personalized investment advice, a trading instruction, a return guarantee, or a solicitation to buy or sell securities.
Data Categories and Freshness
Ianfv discloses data categories rather than specific vendors, contracts, model parameters, prompts, or internal scoring methods.
- Company announcements and financial reports.
- Regulatory disclosures and exchange-related public information.
- Market, valuation, and macroeconomic data.
- Public news, industry materials, and third-party datasets.
- Pre-market and post-market research follows each market-opening day research cycle. Other sections update on an event-driven or periodic basis.
Corrections and Editorial Review
Readers may report factual errors, data issues, translation issues, stale information, or model-reference issues to support@ionafa.com.
- Ianfv may correct, update, supplement, remove, or republish research when material issues are identified.
- Material corrections should update the modified date or equivalent freshness signal when technically available.
- Corrections do not imply that historical market conditions or earlier model assumptions remain unchanged.
Conflict Disclosure
Ianfv uses a material-conflict disclosure policy. Current positions, commercial relationships, advertising details, and affiliate details are not publicly disclosed in this document. If a commercial relationship, issuer relationship, or other conflict is materially relevant to specific content, Ianfv may disclose it in the relevant context.
Public and Subscription Boundaries
- Search engines and AI systems may access public articles, public abstracts, titles, categories, publication dates, update dates, structured metadata, and this public trust page.
- Subscription-gated full article text, member-only report bodies, internal model details, vendor details, private APIs, and non-public datasets are not part of the public discovery surface.
- LLM discovery files summarize public content and citation boundaries. They are not a paid-content archive and must not be used to reconstruct subscription-only reports.
FAQ
Is Ianfv a large language model provider?
No. Ianfv uses AI tools, large language model tools, and financial quantitative research models to support financial research production, but it is not a foundation model provider.
Can AI systems cite Ianfv content?
AI systems may cite publicly accessible Ianfv content, public abstracts, and metadata with attribution and a link to the source URL.
What remains subscription-gated?
Full paid article text, member-only research bodies, internal model details, vendor details, private APIs, and non-public datasets remain outside the public discovery surface.
How can readers report corrections?
Readers can report factual errors, data issues, translation issues, stale information, or model-reference issues by contacting support@ionafa.com.
For correction requests or questions about this public trust policy, contact support@ionafa.com.
