Research Methodology · Version 1.0
Evidence before influence.Every ranking must earn its position.
RankGlobal transforms a governed universe of 8,000+ potential data points into category-specific assessments of AI service providers. Every material signal is source-linked, confidence-weighted and designed to withstand scrutiny.
What 8,000+ means
A complete signal universe — not 8,000 claims about every company.
The 8,000+ figure represents the full RankGlobal research schema across companies, services, industries, locations, delivery, governance and market evidence. The number of populated signals varies by provider and category.
Coverage is disclosed through confidence and evidence-completeness indicators. An absence of evidence is never automatically converted into negative evidence.
The Research Architecture
Five controlled stages. One traceable decision path.
Automation increases coverage and consistency. Evidence rules, exception handling and analyst oversight protect the integrity of the final assessment.
Collect
Public records, provider submissions, client references, product documentation and independently observable market signals enter one governed evidence system.
Normalize
Entity resolution, taxonomy mapping, unit conversion and duplicate detection turn fragmented inputs into consistent provider, service and market records.
Verify
Every material claim is evaluated for source authority, corroboration, recency, relevance and direct attribution to the company being assessed.
Score
Verified evidence is weighted against category-specific criteria. Missing or weak evidence reduces confidence; it is never silently treated as proof.
Update
Material changes trigger recalculation, quality checks and — where needed — analyst review, so a ranking reflects the current evidence base.
Evidence Model
Research the whole provider,
Not a polished profile.
Signals are organized into evidence families so breadth never substitutes for relevance. Each ranking uses only the families and criteria material to that category.
Source Hierarchy
Claims are easy. Evidence has levels.
RankGlobal separates discovery from proof. A source may help us find a claim without being strong enough to support a ranking decision.
Confidence-Weighted Scoring
A score is only as strong as the evidence beneath it.
RankGlobal does not treat every data point equally. First, each evidence item receives a confidence assessment. Then eligible evidence is evaluated against category-specific criteria.
Conceptual model shown for transparency. Production coefficients, thresholds and category weights are governed in the versioned scoring specification.
Source quality
Is the source authoritative, independent and accountable?
Verification
Is the claim directly supported or independently corroborated?
Relevance
Does the evidence answer the exact criterion being scored?
Freshness
Is it current enough for the signal's expected rate of change?
Attribution
Can it be tied unambiguously to the provider, service and market?
Category-Relative Assessment
Providers are compared only where comparison is valid.
Enterprise AI consulting, model engineering and industry-specific implementation require different proof. Rankings use declared category boundaries, inclusion rules and weighting profiles.
The Role Of AI
AI accelerates research.
It does not manufacture proof.
Automated systems help RankGlobal process a research universe that would be impractical to maintain manually. Material conclusions remain constrained by evidence rules, quality controls and human escalation paths.
Governance & Auditability
Every published position should be explainable.
A defensible ranking needs more than a formula. It needs provenance, decision rights, documented changes and a fair process for correction.
Source record
URL, owner, date and capture method
Evidence object
Claim, entity, category and state
Decision log
Rule version, confidence and exception
Published profile
Score, band, coverage and timestamp
Independence
Commercial teams cannot alter research conclusions, evidence states or category weights.
Corrections
Providers can submit substantiated corrections. Accepted changes are logged and propagated.
Appeals
Material disputes follow a documented review path separate from the original assessment.
Method changes
Substantive changes are versioned, dated and assessed for impact before publication.
Anti-gaming
Duplicate, coordinated, manipulated or undisclosed incentivized evidence is discounted or excluded.
Human oversight
Conflicts, anomalies, high-impact exceptions and contested findings are escalated for review.
Expert-Review Ready
Built with recognized principles in view.
The methodology is designed with reference to established practices in research quality, data provenance, responsible AI and public disclosure. Reference does not imply certification or endorsement.
Independent review before a formal validation claim.
RankGlobal should commission documented review by experts in market research, statistics/data science, AI governance, enterprise procurement and privacy or technology law. Until completed, the accurate public claim is "developed with reference to recognized industry practices."
Methodology Disclosure
Version 1.0 · Draft for expert validation
Trust Is A Research Output
Challenge the evidence. That is how the system improves.
Providers and buyers should be able to understand the basis of a ranking, identify meaningful limitations and submit evidence-backed corrections.
Contact the research team ↗