We turn AI search evidence into growth decisions.
Aivius helps teams observe how AI engines answer buyer questions, diagnose credible gaps, prioritize experiments, and measure what changes.
Visibility is only useful when it changes a decision.
Teams do not need another isolated score. They need to know which buyer questions matter, which brands are actually recommended, which sources are cited, where the evidence is weak, and what can be tested next.
Aivius exists to connect those steps. We combine live snapshots, fixed-protocol benchmarks, human-reviewed audits, and repeat measurement in one growth workflow.
We are initially focused on ChatGPT, Perplexity, and Gemini when their providers are configured and available. We disclose partial completion, preserve measurement boundaries, and do not convert missing data into invented competitor scores.
The long-term product is the system of record for AI search growth learning: what was observed, what the team changed, what was measured again, and what the evidence supports doing next.
What "growth intelligence" means to us
A traceable connection between a buyer question, an observed AI answer, a defensible diagnosis, a scoped experiment, and a repeat measurement.
- ✓ Focused initially on ChatGPT, Perplexity, and Gemini
- ✓ Tied to real prompts in your niche
- ✓ Benchmarkable against competitors
- ✓ Actionable through content fixes
Built around a simple measurement problem.
AI answers are stochastic, platform availability changes, and a single query cannot represent a market. Yet many visibility products compress all of that uncertainty into one authoritative-looking score.
Aivius began by treating the measurement protocol as part of the product: confirm the brand and category, use matched prompts, retain platform status, distinguish recommendation from citation, and publish limitations beside the result.
The public benchmark added a second layer—fixed datasets that make category observations reviewable. The Growth Audit adds human diagnosis, while ongoing Growth work connects validated gaps to experiments and repeat measurement.
Aivius remains an early-stage product. Current capabilities, roadmap items, and human-delivered services are labeled separately so buyers can evaluate what exists today.
What we believe, and how we operate.
Four principles that shape every product decision, support reply, and roadmap item at Aivius.
White-hat GEO
We do not help brands manipulate, spam, or poison AI models. Our playbooks focus on earning citations through better content, clearer structure, and verifiable expertise — the same principles that made SEO sustainable, applied to generative search.
Transparent scope
We publish what each offer includes, label samples and estimates, and confirm custom execution scope before invoicing. Limits and evidence boundaries should be understandable before a customer commits.
Provider-aware measurement
Our live snapshot targets ChatGPT, Perplexity, and Gemini when their providers are configured and available. We report partial completion and provider failures instead of converting missing data into a zero score.
Decision usefulness
Every output should help a team choose a next test: which buyer question matters, what evidence is missing, what to change, and how to measure the result without overstating certainty.
Evidence first, then human judgment.
Aivius combines repeatable measurement with a human-reviewed path from observed gaps to prioritized experiments.
Observed evidence
We preserve prompts, provider status, mentions, citations, and timestamps so conclusions can be traced back to what was actually measured.
Human review
The paid Growth Audit turns a directional snapshot into a reviewed diagnosis, with assumptions, confidence, priorities, and limitations stated explicitly.
Measured iteration
Execution work is scoped around testable changes and repeat measurement. We report movement and uncertainty rather than promise rankings, citations, traffic, or revenue.
Published methods, visible limitations.
Our benchmark and research pages publish question sets, collection dates, observed results, and methodological limits wherever the underlying evidence supports them.
Product capability, research evidence, and planned work are labeled separately. Review the published benchmark to see that standard in practice.
Built to support decisions, not decorative scores.
Aivius connects directional observation, human-reviewed diagnosis, prioritized execution, and repeat measurement in one explicit workflow.
Provider-aware results
Live snapshots target ChatGPT, Perplexity, and Gemini when available. Partial completion, quota issues, and provider failures stay visible instead of becoming misleading zero scores.
White-hat only
We refuse to help brands manipulate AI models. No prompt flooding, no citation fraud, no data poisoning. Our Terms of Service explicitly ban these tactics. If you want a shortcut, Aivius is not the right tool — and we are comfortable with that.
Priorities, not just monitoring
A snapshot is only an entry point. The reviewed audit tests which buyer questions and evidence gaps deserve attention, then turns them into scoped experiments.
Useful evidence requires honest limits.
AI answers vary by prompt, provider, date, model, and sampling method. We keep those conditions visible so teams can use the evidence without confusing it for a universal market measurement.
A sample is not market share
A result describes the questions and runs in its protocol—not every possible buyer journey.
Missing is not zero
Provider errors, missing configuration, and quota limits remain distinct from a successful answer with no mention.
Movement is not causation
Repeated measurement can show change, but attribution requires a controlled interpretation of competing causes.
Aivius does not guarantee rankings, citations, traffic, or revenue.
Talk to the team behind Aivius
Whether you want a product demo, a GEO audit, or just want to swap notes on AI search — we read every message.
Media inquiries
Working on a story about AI search, generative engine optimization, or brand visibility? Contact us for available methodology notes, published data, or commentary.