About ARIA Evaluator
ARIA Evaluator gives engineering, security, and governance teams one operating layer for AI agent testing, release controls, compliance evidence, and post-deployment oversight.
Evaluation dimensions
Global deployment regions
Agent platforms supported
Founded
Our story
ARIA was not conceived as a product category — it was built to solve a specific operational problem that existing tooling consistently failed to address.
In 2024, enterprise adoption of conversational AI agents accelerated sharply across financial services, healthcare, and contact-centre operations. The infrastructure for deploying these systems — Amazon Connect, Lex, Azure Bot Service, and a growing set of custom LLM-backed endpoints — was mature and well-supported. The infrastructure for verifying that they behaved safely, fairly, and in compliance with regulatory expectations was not.
The teams building and operating these agents were using a patchwork of approaches: unit tests against isolated intents, manual QA sessions, occasional red-team exercises, and post-incident review. Each of these has value in isolation, but none of them produces the continuous, structured, auditable signal that production operations and compliance teams actually need.
The earliest version of ARIA was an internal evaluation framework built specifically for assessing Amazon Connect agents in a financial services context. The key architectural decision made early was to use an LLM as the judge, scoring multi-turn transcripts against a structured rubric rather than matching against expected outputs. That decision turned out to be the right one: it scaled naturally from functional testing to adversarial, bias, and compliance evaluation without requiring separate testing infrastructure for each category.
ARIA launched as a fully managed SaaS platform in 2026. The product is opinionated: evaluation should be continuous, evidence should be auditable, and safety controls should be on by default. Those positions come directly from the operational experience that built it.
Our thesis
Most AI incidents in production are not catastrophic failures — they are subtle ones. An escalation path that almost works. A guardrail that holds under direct attack but yields under social engineering framing. A bias that only surfaces at turn seven of a multi-step conversation. Standard QA tooling misses all of these because it was never designed for conversational, multi-turn, stochastic systems.
Agent behaviour drifts. Prompts change. LLM providers update models without notice. A score that was true at release can become false within weeks. Teams that treat evaluation as a one-time gate before launch are operating on stale signal. ARIA is designed around the premise that evaluation is continuous — run at every release, on a schedule, and in response to operational signals.
Regulated-industry teams cannot submit intent to an auditor. FCA Consumer Duty, HIPAA, and financial services AI governance frameworks all require demonstrable, traceable evidence that the system behaves correctly in the scenarios that matter. ARIA produces that evidence: structured scores, judge reasoning, and immutable run history that auditors can review.
How we build
These are not values — they are design constraints. Every product decision in ARIA is tested against them.
Security and compliance controls are first-class features, not add-ons. Every ARIA run evaluates guardrail compliance, prompt injection resistance, and escalation behaviour as standard — teams do not have to configure safety in.
A test that cannot be reproduced is a guess. ARIA runs are deterministic: same scenario, same adapter configuration, same judge model produces comparable results across releases. This is what makes regression testing and baseline comparison meaningful.
Every score ARIA produces comes with judge reasoning you can read, quote, and export. The goal is not a pass/fail number — it is an auditable record that explains why the system scored the way it did, trace by trace.
ARIA is built for the people who own the reliability of AI systems in production: platform engineers, security architects, risk leads, and the compliance teams that sign off on their work. The product is designed around their workflows, not a researcher's notebook.
Governance frameworks matter only if product teams can act on them. ARIA translates policy intent — FCA Consumer Duty, HIPAA safeguard requirements, bias standards — into discrete, scorable dimensions that both engineers and compliance teams can reason about.
Pre-release evaluation is necessary but not sufficient. Scheduled regression runs, baseline comparison, and scheduled red-team packs give teams the ability to detect drift between releases — not just at the gate.
Timeline
The earliest version of ARIA was built as an internal tool for evaluating Amazon Connect and Lex agents in a financial services context. The initial focus was adversarial testing — prompt injection, social engineering, and guardrail verification. A custom LLM-judge architecture scored transcripts across a structured rubric instead of relying on keyword matching or manual review.
The dimension framework expanded to 15 evaluation criteria covering response quality, task completion, safety and security, customer experience, and escalation compliance. Azure Bot Service and OpenAPI adapters were added, making ARIA platform-agnostic. The first version of the bias and fairness dimension was introduced after observing inconsistent treatment patterns in demographic testing.
ARIA was rebuilt around a dedicated-tenant model — every workspace runs in isolated infrastructure. The control plane introduced tenant lifecycle management, region selection, role-based access, and scheduled evaluation runs. The human review queue allowed compliance and risk teams to participate in the evaluation process alongside engineers.
Escalation Appropriateness and Vulnerability Detection dimensions were formalised against FCA Consumer Duty requirements. The audit log, run history, and report export capabilities were hardened to produce evidence packages suitable for regulatory review. The first structured compliance playbooks were documented.
ARIA launched as a fully managed SaaS platform with global region support, CloudFront delivery, ECS Fargate isolation, and Secrets Manager credential handling. The website and self-serve onboarding were built. Security controls were aligned to SOC 2 and ISO 27001 frameworks, with certification underway.
What ARIA is
What ARIA is not
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The Free plan supports 5 evaluation runs with the full 15-dimension judge. No infrastructure to configure. Results in minutes.