Editorial Integrity & Evidence Policy

This Editorial Integrity & Evidence Policy applies to all content on our platform, including but not limited to:

• Company and product profiles
• Rankings, comparisons, and "best of" lists
• Feature and pricing tables
• Badges, scores, and structured data feeds
• Commentary, summaries, and explanatory text generated or edited by our team

Our objective is to deliver accurate, transparent, and independently compiled information accessible to both human users and machine systems.

Editorial and data teams operate with full independence from business operations:

• Business development, sales, or partnership activities do not influence entity inclusion, rankings, or claim validation.
• Inclusion and exclusion decisions are based exclusively on documented criteria and evidence standards.
• Internal or external pressure to influence editorial decisions constitutes a policy violation and will be rejected.

Neutrality is maintained through mandatory conflict of interest protocols:

• Contributors and decision-makers must disclose financial, advisory, or employment relationships with entities in the database.
• Individuals with conflicts of interest are excluded from decisions regarding presentation, ranking, or scoring of related entities.
• Material conflicts of interest may be disclosed within relevant pages or datasets.

Database construction and maintenance methodologies are fully documented. Each dataset or ranking includes the following documentation:

Inclusion criteria

Specification of entities and categories considered, with rationale for inclusion parameters.

Data sources

Origin identification for all data (official websites, regulatory filings, verified third-party providers).

Normalization & deduplication rules

Procedures for data cleaning, merging, and normalization (duplicate brand mapping, product line consolidation).

Scoring & ranking logic

For scored or ranked content (including "Top X" lists and Yes.inc scores), documentation specifies:

• Evaluation factors (evidence quality, clarity, coverage, reliability)
• Factor weighting and combination methodologies
• Limitations and caveats

Update and review cadence

Scheduled intervals for dataset review, refresh, and re-crawling.

Methodologies are designed for reproducibility and inspection by both human analysts and machine systems.

Data is published in structured, machine-readable formats optimized for both human and automated consumption.

Structured data formats (JSON-LD or equivalent) include:

• Entity identification (company, product, feature)
• Associated claims and assertions
• Source citations for each claim
• Verification timestamps

Provenance documentation:
Complete traceability of information origin, observation date, and applied transformations.

This structure enables search engines, language models, and other systems to evaluate data trustworthiness and trace all statements to source documentation.

Data accuracy is maintained through systematic review and correction protocols:

• Regular data verification through scheduled re-crawling and event-triggered updates (product launches, regulatory changes, mergers).
• Error correction procedures for internally identified or externally reported issues:
  • Investigation conducted against established evidence standards
  • Prompt correction of verified errors
  • Documentation of material changes via timestamp updates and change logs

Correction requests and supplementary evidence submissions from companies and users are processed using uniform, evidence-based criteria applied to all entities.

Database integrity is maintained through exclusion of the following content types:

• Unsubstantiated marketing claims (e.g., "world-leading", "best-in-class" without measurable support)
• Unreleased or aspirational features
• Non-commercial content (corporate information pages, career listings, brand slogans)
• Information contradicting primary sources or lacking verifiable evidence

Ambiguous content of uncertain verifiability is excluded or accompanied by explicit disclaimers.

Data processing employs integrated automation and human oversight:

• Automated systems facilitate data discovery, extraction, and structuring at scale.
• Human review provides critical evaluation, contextual analysis, and quality assurance for:
  • Ambiguous claim interpretation
  • Inclusion and exclusion criteria application
  • Conflict of interest assessment
  • Methodology modification approval

Automated systems cannot override core editorial principles, non-sponsorship standards, or evidence requirements without human authorization.

Sustained trustworthiness is maintained through formal governance procedures:

• Recurring internal review of methodologies, sources, and datasets.
• Internal or external audits may be conducted to assess:
  • Conflict-of-interest protocols
  • Non-sponsorship compliance
  • Evidence and sourcing standards
• Material issues identified through governance processes are addressed through:
  • Corrective action implementation
  • Methodology updates
  • Documentation revisions and, where appropriate, public disclosure

This Editorial Integrity & Evidence Policy may be updated to reflect:

• Expansion of product categories and data types
• Regulatory changes, industry standards, or best practice evolution
• Methodology or governance enhancements

Material policy revisions will be documented with updated effective dates. Continued platform and data usage constitutes acceptance of the current policy version.