An evidence-first platform that measures how frequently a brand is discovered, mentioned, recommended, ranked and cited across AI answer engines — then converts every visibility gap into a specific, evidence-backed action.
Buyers no longer just type keywords into Google. They ask AI assistants for shortlists, recommendations and experts. Traditional SEO can track rankings, backlinks and traffic — but it goes silent on what actually happens inside an AI answer.
The system runs a disciplined loop for each frozen prompt — capturing raw evidence before any number is ever calculated.
A mention, a recommendation, a rank and a citation are not the same outcome — so each signal is measured separately before being combined.
From frozen prompts to human-validated evidence and persistent optimization workflows.
The same frozen prompt runs across OpenAI and Gemini for reliable comparison — with an architecture built to add providers as independent lanes.
24 frozen prompts organised by lane, prompt type, buyer intent, funnel stage, priority and version — spanning discovery, branded, comparison and category.
Every execution creates a new record; responses are never overwritten. Prompt version, model, timestamp and snapshots are preserved so measurements stay reproducible.
A separate layer identifies mentions, recommendations, explicit rank, sentiment, competitors and citations — without ever modifying the original response.
An evidence view places the full untouched AI response beside the structured extraction, so every analysis can be verified against the exact source answer.
The Prompt Explorer lays out mention, recommendation, rank and citations per engine — with combined citation domains and full evidence drill-down.
Monitors 15 relevant PMM, GTM and growth practitioners — five frozen per benchmark cycle for consistency, with newly discovered competitors recorded automatically.
For every cited source it captures the full and normalised URL, domain, engine, entity supported, owned vs third-party status and first/latest appearance.
Each prompt carries a finding, cross-engine evidence, winning competitors, source gap, diagnosis, a recommended intervention, a content outline and a retest plan.
Every initiative moves through Needs Evidence → Planned → In Progress → Published → Retesting → Complete, turning reporting into an ongoing optimization loop.
Each metric is defined precisely — and calculated only from valid, evidence-backed responses.
Share of successful responses where Deepak or an approved alias appears.
Prioritises high-intent prompts — P1×3, P2×2, P3×1.
Active recommendations across all responses and among mentions.
Uses only explicit ordered rankings — never inferred from prose.
Relative visibility using weighted position points across the cohort.
Owned-domain citations as a share of all tracked citation domains.
How often deepakruchandani.com enters AI evidence chains.
Provider reliability, measured separately from brand visibility.
The system never reacts to noise. When a result can't be reproduced or hasn't been tested enough, it says so instead of manufacturing a finding.
The winning answer covers a topic where Deepak lacks visible authoritative content.
Owner · Content & SEOCompetitors are backed by publications, communities, podcasts or analyst sources.
Owner · PR & PartnershipsDeepak appears, but tied to the wrong audience, category, problem or expertise.
Owner · Product MarketingResponses repeatedly carry negative or limiting language linked to evidence.
Owner · Product & PMMThe result can't be reproduced consistently. Don't react — repeat the test first.
Action · Re-run before actingThe prompt isn't tested enough. A hypothesis may be offered — never as a finding.
Action · Test before concludingDeepak is absent from the available sample responses.
A potential content and authority gap.
Publish an original, research-led AEO authority page with a transparent visibility methodology, clear mention and recommendation rules, citation measurement, a reproducible benchmark, B2B SaaS examples and a downloadable framework.
Once the page is discoverable, rerun the exact frozen prompt across the same engines — preserving prompt wording, model mix, competitor cohort, alias config and scoring version. Only compare like-for-like periods.
Demo and live data stay strictly isolated, no score exists without supporting evidence, and credentials never leave the server.
Demo responses never touch live metrics, rankings or citations.
No visibility score exists without supporting response data.
Failed calls still create records — reported, but excluded from visibility maths.
OpenAI & Gemini keys never reach the browser, bundles, logs or screens.
The dashboard is publicly explorable for demos — visitors can review prompts, sample responses, citations, competitors and recommendations. Only the authorised owner can enter Live mode, execute engine requests, consume API credits and change workflow statuses, protecting the connected API balance.
Does AI independently discover us?
Does AI understand our positioning correctly?
Does AI recommend us for high-intent questions?
Which competitors are winning visibility instead?
What evidence or authority asset should we create next?
The same architecture adapts to B2B SaaS companies, AI startups, consulting businesses, founders, executives, professional service firms, category creators and personal brands.
Rather than treating AEO as another content-generation activity, I approached it as an intelligence, measurement and decision-making problem.
Strategic marketing thinking connected to hands-on AI execution.
Explore the demo console to walk the evidence architecture — from frozen prompts to human-validated extractions and prompt-level recommendations.