Last Updated: July 31, 2026
The Digital Today’s core objective is to map, measure, and benchmark how digital brands, software ecosystems, and enterprises perform across both traditional search and generative AI retrieval. Our research desk operates under one transparent process — we don’t form an opinion and then look for data to justify it.
The seven-phase research pipeline. Every ranking, audit, and benchmark we publish moves through the same sequence: Research Planning & Hypothesis Mapping → Candidate & Vendor Discovery → Public Evidence & Dataset Collection → Deep Technical & Infrastructure Audits → Evidence-Based Scoring → Independent Editorial Analysis & Drafting → Peer Review & Publication. Writing happens strictly at the end. If evidence collected during the audit phase contradicts our initial hypothesis, the data stands and the editorial narrative adjusts to match it — not the other way around.
What we evaluate. Individual reports weigh criteria differently depending on the subject, but our overarching framework rests on ten evaluation areas, grouped into three categories:
Entity Clarity — Technical SEO & Infrastructure (server performance, page speed, architecture robustness); Structured Data & Semantics (schema, knowledge-graph alignment, machine-readability); Entity Strength (how cleanly a brand’s identity survives across AI inference chains).
Semantic Authority — AI Visibility & Mindshare (organic share of voice across generative answer engines); Content Quality & Architecture (depth, topical authority, semantic density); Website Operational Quality (UX, responsiveness, accessibility); Editorial Authority & Trust (author credentials, citation standards, verifiable experience).
Cross-Source Trust — Cross-Source Citation Probability (how consistently a brand is corroborated across independent sources); AI Search Recommendation Performance (how AI systems actually recommend or omit a brand in practice); Verifiable Client & Market Proof (case studies, execution data, third-party review evidence).
The exact weighting and scoring for a specific report is always published within that report — full methodological transparency, report by report.
What “evidence-based” means here. Our scoring favors three things: it’s explainable (you can see exactly why a score landed where it did, not an opaque “vibe check”); it’s reproducible wherever practical, using public evidence and accessible data so an independent researcher using the same inputs could reach the same conclusion; and it’s consistent within a series over time, so readers can track real change rather than methodology drift.
Independence. Our research cannot be bought, influenced, or co-opted. No vendor, enterprise, or agency can pay for inclusion, a higher score, or a better ranking. For how we manage our founder’s related ventures without compromising this, see our Disclosure Policy.
This will change, transparently. AI-mediated discovery is moving quickly, and our frameworks will evolve with it. When we refine our evaluation criteria, we’ll declare the change here, on the record.
