Ship AI that actually works when real users use it

You validate an AI agent by running it against a diverse population of realistic users and scenarios, scoring every interaction across the dimensions that define “working” for your business, and reviewing the failures before customers do.

Validation is fundamentally different from evaluation. Evals score a model on a fixed benchmark. Validation answers a product question: does this agent behave correctly when it meets the real world? For agents – which are multi-turn, tool-using, and context-dependent – that means testing whole conversations and outcomes, not single prompts.

Arato is purpose-built for AI agent validation. For each release we:

• Map the agent’s real user base into 100–200 synthetic personas – novices, power users, frustrated users, adversarial users
• Generate realistic multi-turn scenarios, including the messy, off-script, and edge-case paths real customers take
• Run the agent against your staging or production endpoint with no code access or SDK required
• Score every conversation across accuracy, safety, brand voice, task completion, and tool-use correctness
• Surface the exact transcripts where the agent broke, ranked by severity

The output is a readiness report you can take into a launch review – quantified evidence that your agent works, and a prioritized list of the failures to fix before it ships.

Evals measure whether a model is performing on a fixed test set. Arato measures whether a product is working for the customers who will actually use it.

An eval platform answers “is the model better than last week?” Arato answers “will this launch survive contact with real users?” The two are complementary – evals belong inside the engineering loop, Arato belongs inside the launch-readiness review – but only the second question determines whether a release ships safely.

You know your AI is working when you have measured evidence that it behaves correctly for the customers and scenarios that actually matter – not just for the prompts your team happened to try.

Most teams answer this question with a mix of vibes-based testing, a handful of internal prompts, and model-level evals that score accuracy on a fixed dataset. None of those tell you how the product behaves in the messy, multi-turn, off-script ways real users will use it.

Arato gives you that evidence directly. For each release, Arato:

• Generates 100–200 synthetic users that mirror your real customer base – novices, power users, frustrated users, adversarial users
• Runs them through realistic, multi-turn conversations against your staging or production endpoint
• Scores every interaction across accuracy, safety, brand voice, and edge-case behavior
• Surfaces the exact transcripts where your AI broke, with severity ranked so you know what to fix first

The output is a readiness score you can take into a launch review and a list of concrete failures to fix – the difference between hoping your AI is working and knowing it is.

No. Arato does not require code access, SDK installation, or changes to a build pipeline.

Arato tests an AI product from the outside in – pointed at a staging URL, production endpoint, or sandboxed API key. Engineering teams stay focused on shipping; product and trust & safety leaders get a readiness report without booking sprint capacity.

A first Arato simulation typically runs 1–3 weeks from kickoff to delivered readiness report.

The bulk of the timeline is scoping – defining the personas, scenarios, and risk dimensions that matter for the specific product. Once a scenario library is established, subsequent simulations on the same product can run in days, making it practical to gate every major release.

A first Arato simulation is free. Ongoing engagements are priced per release or as an annual program based on simulation volume and product complexity.

The free first run is intentional: it lets teams see exactly what their AI product is currently shipping without seeing – before there’s a procurement conversation. Pricing for follow-on work is shared after the first readiness report is delivered.

Arato tests any customer-facing generative AI surface where wrong, unsafe, or off-brand responses carry real business risk.

• Chatbots, copilots, and support agents
• RAG-based search and knowledge assistants
• AI-powered onboarding, coaching, and recommendation flows
• Voice agents and multi-turn conversational products
• Internal AI tools where accuracy or compliance is regulated

An Arato readiness report quantifies how an AI product behaves across 100–200 simulated scenarios and flags the specific failures a launch review needs to see.

• An overall readiness score with confidence intervals
• Per-persona and per-scenario pass/fail breakdowns
• Critical-risk transcripts – the exact failures, in context
• Brand-voice, safety, accuracy, and edge-case dimensions scored separately
• A prioritized remediation list the engineering team can act on