Classic SEO gets you ranked on Google. GEO gets your product recommended inside the answer — by ChatGPT, Perplexity, Claude, Gemini and AI Overviews. DaaS already structures your docs into skills; GEO exposes them on every surface a generative engine crawls, parses, and quotes.
The machine-readable surfaces generative engines look for. All live and served by DaaS.
Each score measures how well a product is set up to be discovered and cited. Click a card for the factor breakdown, recommendations, and live structured data.
Every product gets a crawlable /p/{product} page and one
/p/{product}/{intent} page per how-to, each carrying schema.org JSON-LD
(SoftwareApplication, FAQPage, HowTo) — the formats LLMs trust for facts.
A single /llms.txt tells any AI engine what you offer and where
the authoritative content lives — the emerging standard for AI discoverability.
A crawler-aware robots.txt explicitly welcomes GPTBot, ClaudeBot,
PerplexityBot, Google-Extended and more — the #1 fix for being invisible to AI answers.
Agents route any question through /api/route and get the single best
skill back — so the model cites your docs, not a guess.
Most GEO tools stop at "we generated the surfaces." DaaS closes the loop: it
generates its own brand-free test questions from the skill graph, runs them through
/api/route — which is both the thing we optimize and the ruler we measure
with — then diagnoses, rewrites, and re-measures. Click any scored product to see its live routing
diagnosis.
Brand-free questions are synthesised straight from each intent's task and trigger keywords — never naming the product, so we measure organic surfacing, not a brand lookup. The structured skill graph makes this step free; everyone else hand-writes prompts.
Each query runs through our own router. A miss is decomposed into a coverage gap (no content exists) vs a surfacing gap (right product, wrong intent) vs wrong product — the diagnosis that tells us exactly what to fix.
The same brand-free queries are run against real engines, then an LLM judges whether the product was mentioned or cited — the true "are we in the answer" signal, with a citation gap list of queries where a competitor surfaced and we didn't.
The GEO-paper-proven levers (answer-first, statistics, citations, inline definitions) are applied to the weakest skills — guarded by a key-point-recall check so visibility is never bought with lost accuracy. No facts, endpoints or numbers may be dropped.
ChatGPT, Perplexity, Google AIO and Claude weight the signals differently. Each product gets a per-engine sub-score so you can see where you're already citable and where you're invisible.
Beyond being cited, every product ships an /api/mcp/{product}.py server and a
markdown twin at /p/{product}.md — so an agent can call DaaS directly as the authoritative
answer source, not just quote it.
# Ask DaaS for the best skill, the way an agent would
curl -s /api/route -H 'Content-Type: application/json' \
-d '{"query": "how do I deploy an SSL certificate?"}'