Solutions

Eye for AI Research

Run repeatable experiments across answer engines. Compare outputs, sources, and drift over time—so you can study model behavior with evidence.

Outcomes teams care about

Eye turns model behavior into visibility, citations, and concrete next steps you can validate.

Repeatable evaluations

Run the same prompt set across platforms and time windows for consistent comparisons.

Attribution through sources

Track which domains show up in citations and how retrieval changes across models.

Behavioral drift monitoring

Detect changes in narratives, sentiment, and ranking stability over time.

Exportable datasets

Pull structured results for reporting, analysis, and downstream research workflows.

Use a small set of repeatable workflows that keep the depth while staying easy to act on.

Design a prompt suite

Capture questions, personas, and constraints—then run across selected engines.

Compare outputs side-by-side

See differences in mentions, position, and citations across platforms.

Track drift & stability

Measure how answers change across runs to understand volatility.

Inspect sources and domains

Study which domains are cited and how that correlates with recommendations.

Run Prompt Lab conversations

Iterate in a conversational flow while still capturing structured stats.

Export results

Download structured outputs for analysis or collaboration.

Drill down from charts and tables into evidence, sources, and changes across runs.

Platform comparison

Compare outputs across engines on the same prompts for controlled evaluation.

LLM mentions and outcomes

Quantify mention rates and ranking behavior across platforms.

Cited domains (variant)

Track citation dynamics across runs to understand retrieval and authority shifts.

Source concentration

See which sources dominate answers—and how that changes over time.

Create a project, add prompts, and start measuring visibility across answer engines in minutes.