See how your AI performs —
before real users do.
The software primitive is changing in real time. Testing the new surface with the old tools leaves you flying blind — because the user is no longer choosing from a menu; they're collaborating with a system that has its own state.
AI products are non-deterministic, multi-turn, and increasingly autonomous. Traditional QA wasn't built for this. Most teams ship with hope instead of evidence.
Watch how evaluation compounds — each stage builds on the last. A benchmark is one dot. VIVID evaluates the whole universe.
Others evaluate a point. We evaluate the world.
Two kinds of agents run against your system so you see how it actually behaves in production — not just whether it answers correctly.
Personas with personality, memory, and goals. They return across sessions and judge your AI the way real people will.
Autonomous attackers that adapt, chain, and push toward the weakest point — before customers or auditors find it.
Virtual users that return across sessions, build memory, and surface the trust erosion single-turn tests never catch.
Most "persona evaluation" today is a system prompt that asks the model to perform a role. VIVID's Persona-Simulating Agent (PSA) is different in kind — a stateful agent whose behavior is derived from psychological traits, not improvised from a paragraph of text.
"You are Sarah, a 34-year-old wedding photographer in Brooklyn. Personality: friendly, detail-oriented, slightly impatient. When the user asks about photo editing, respond as Sarah would. Keep your answers concise and use casual language."
Big Five is not a label on top of the persona — it determines how the persona acts. Frustration intensity, patience, conversation style, evaluation leniency all derive mathematically from trait values.
Across 32 PSAs · 960 sessions · 15 tasks · 5 domains · 2 model pairs, two scaling laws coexist within the same panel. Optimizing for measurement ("is this system good?") and optimizing for coverage ("what are this system's problems?") require different panel sizes.
A persona claim is only as good as the evidence behind it. We ran three tests — a Turing-style human comparison, a controlled ablation against simple prompting, and a Big-Five behavioral validation — to see whether structured PSAs are doing what we say they're doing.
A high-Openness persona weights creativity more heavily on image tasks; a high-Conscientiousness persona prioritizes technical accuracy. The same persona behaves consistently across text, image, audio, video, and computer use — because behavior derives from traits, not from modality-specific prompts.
Quality ceilings require expert-written scenarios. Real-world coverage requires adaptive generation. We do both — curated scenarios as the floor, LLM adaptation at execution time for the long tail.
Scenario: A US retail bank launches a bilingual AI agent for Spanish-speaking customers — a demographic of 42M+ US adults with over $2T in purchasing power. English compliance passed. Could the Spanish experience hold up? Below: what a Compass evaluation would surface across a 5-session customer journey.
Two modes, one platform. Assessment runs fast parallel attacks with pass/fail triage; Swarm deploys agents that chain attacks toward worst-case outcomes. Every run produces a standardized S–D safety grade with compliance-mapped evidence.
Existing categories answer "did the model give a correct response?" VIVID answers "how does this system behave when real users and real attackers meet it?"
The shift from app UX to agent UX is already here. VIVID puts a panel of personas with memory, personality, and goals between your build and your user — across text, image, audio, video, and computer-use. Delivered as a single API — runs, personas, and structured evidence wireable into your CI pipeline, your moderated-study briefing flow, or your regression dashboard.