Be the answer, not the link.
Optival — optimal retrieval — measures how a brand is cited, named, and represented when ChatGPT, Perplexity, and Google AI Overviews compose an answer, then diagnoses the gap and ships the fix.
What Optival does
Owned-content GEO. Passive citation visibility — not agent operability.
Optival — short for optimal retrieval — is a commercial Generative Engine Optimization tool. GEO is the generative-era counterpart to SEO: where search optimization ranks a page in a list of blue links, Optival works to be the answer an engine composes and the source it credits.
Generative engines now answer many customer questions directly, drawing on a small set of sources they frequently name. A brand absent from that answer loses the customer’s attention before the customer ever reaches the site — and usually cannot see that it is happening, or why. That is the problem, stated plainly: lost visibility inside the answer, with neither the measurement to detect it nor the diagnosis to fix it.
Optival supplies both. Its scope is deliberate — owned content only: the pages, copy, and structured data a brand controls and can change directly. The diagnosis is the pitch, the meta is the proof of work, and the benchmarked lift is the headline.
Three atomic engines
Each a black box with one responsibility, wired audit → diagnosis → generation.
Audit
Input is one domain. Auto-derives the query set from keyword analysis, personas, and intent buckets, auto-discovers competitors from whoever appears in the answers, and samples every engine repeatedly to measure AI Share of Voice with citation depth.
Diagnosis
Consumes the audit and identifies the root causes of the gaps against the owned-content levers, emitting a prioritized account of what is holding the prospect back.
Generation
Turns the diagnosis into the meta: optimized titles and descriptions, schema markup (FAQ, HowTo, QAPage), and an llms.txt — emitted as a schema-governed YAML document. The proof of work, never published to a live site.
Anchored in research
Two papers anchor the method and the commercial thesis.
GEO: Generative Engine Optimization
Defines visibility for a single source two ways — a position-adjusted word count and a subjective impression score. Quotations, statistics, cited sources, fluency, and an authoritative tone lift visibility; keyword stuffing reduces it. Crucially, lower-ranked sources gain the most — the method favors smaller players.
Generative Engine Optimization: How to Dominate AI Search
Controlled experiments show AI search favors earned, third-party authoritative sources over brand-owned content, with engines differing in freshness and a big-brand bias against niche players. This defines the off-site frontier Optival deliberately defers past v0.
Roadmap
v0 diagnoses and prepares the fix; v1 applies it and proves the lift.
Diagnose & prepare the fix
Internal, pre-sales. Three engines — audit, diagnosis, generation — produce the diagnosis (the pitch) and the YAML meta (the proof of work) for a named prospect. Owned content only. The meta is never applied.
Apply & prove the lift
A fourth content engine ingests the meta and applies it to the live product — Optival becomes client-facing. The audit re-runs and benchmark comparisons turn the before-and-after difference into proven citation lift: the headline.
Earned & off-site authority
Once owned-content optimization works end to end, Optival extends into earned, off-site authority — the third-party presence the research shows engines weigh heavily — as suggestions, tactics, and strategies.
Core team
Small by design.
Aman Agrawal
Building Optival — optimal retrieval.