Research
Original methodology and frameworks from ibs Fulcro's AI search and technology teams, published as primary research, not marketing content, organised into AI Search Visibility, Privacy & Measurement, and Original Data.
The ibs Fulcro AI Search Visibility Methodology: A Canonical Reference
This is the real, complete method behind every AI Search Visibility number we publish, prompt sampling, the brand-anchored vs. unprompted distinction, how we score coverage and position, and where this approach still has real, honest limitations.
Read the methodology →Original ResearchHow to Estimate AEO Prompt Volume: A Calibrated Method
Keyword research tools measure typed queries submitted to search engines. They do not measure prompts submitted to AI assistants. This is the four-stage method ibs Fulcro uses to size Answer Engine Optimisation demand from first-party product data, calibrated against measured search volume, without relying on per-prompt data that no commercial provider currently publishes.
Read the methodology →Original ResearchThe Owned-Citation Gap: What Five AI Visibility Scans Reveal About Who AI Actually Recommends
ibs Fulcro's AI search analysts ran five independent Velivo Radar scans across five unrelated categories, cement, oral care, health drinks, paint, and whisky, tracking how often ChatGPT and related AI engines actually recommend real, named brands versus their competitors. The same structural pattern showed up in every single one.
Read the methodology →Original Research"Average Position" Is Quietly Broken as an AI Visibility Metric
Almost every AI visibility report leads with average position when mentioned. It's an intuitive number and it's quietly wrong, in a specific, provable way. Here's the failure mode, and the fix.
Read the methodology →How India's DPDP Act Breaks GA4 and Remarketing, and What Actually Fixes It
Under the DPDP Act, 2023 and the DPDP Rules, 2025, running GA4 scripts or firing Meta and Google remarketing pixels before a user gives explicit, affirmative consent is non-compliant, full stop. Here's exactly why, on what legal basis, and the real technical setup that keeps a brand's marketing measurable without breaking the law.
Read the methodology →Original ResearchHow to Enable Google Consent Mode v2, Step by Step, and What Happens to Meta and Google Remarketing
A real, technical walkthrough for setting up Consent Mode v2 through Google Tag Manager, built for Indian marketing teams working toward DPDP compliance. Plus two direct questions answered honestly: can this actually be implemented in India, and what genuinely happens to remarketing when a user says no.
Read the methodology →Original ResearchA Three-Layer Verification Model for GA4 Under DPDP, and the Real Cost to Remarketing
Consent-gated GA4 doesn't just lose some data, it loses it unevenly, and every brand ends up guessing how much. Here's a real, proposed framework for closing that gap, plus a direct, honest look at what consent decline actually costs D2C brands: remarketing, custom audiences, and a real, structural rise in acquisition cost.
Read the methodology →Original ResearchThe Cookie Consent Standard We Build to Under DPDP: A Complete Reference
This is the real, complete standard we hold every project to, not a summary of one. The non-negotiables, a reference banner pattern, working code, reusable copy, and a QA checklist a non-developer can run before any project ships.
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