Most AI testing tools were built by ML engineers for ML engineers. TryGrounded AI is different — it was designed for QA testers who need structured, evidence-backed verdicts on AI output quality. Here's our hands-on review.
The challenge facing QA teams in 2026 is not a shortage of AI testing opinions — it's a shortage of AI testing tools that actually fit into a QA workflow. Most hallucination detection approaches require ML engineering skills, model API access, or expensive infrastructure. TryGrounded AI takes a radically different approach: paste the AI response, get a structured verdict in under 60 seconds. No API key. No integration. No model access required.
KiwiQA built TryGrounded AI after years of fielding the same question from clients: 'How do we know our AI isn't making things up?' The honest answer — before Grounded existed — was that most organisations had no systematic answer. They relied on manual review, which doesn't scale, or accepted that customers would report problems. Neither is an acceptable quality standard for AI systems operating in healthcare, legal, financial or government contexts.
Grounded is an 8-layer hallucination testing platform. It takes three inputs — a question, an AI response, and an optional reference document — and runs up to nine independent validation checks, returning a GR reliability score (0–100), a PASS / WARN / FAIL verdict, and a downloadable PDF report. The whole process takes under 60 seconds.
The eight checks are: consistency (does the AI give the same factual answer when asked the same question different ways?), document grounding (is every claim supported by the reference document?), confidence audit (does the AI's expressed certainty match the actual evidence?), model consensus (does GPT-4o agree with the answer?), semantic drift (did the response stay on topic throughout?), domain rules (validated against 21+ verified facts across 12 industries, no LLM involved), custom rules (your own verified facts, fully auditable), and RAG validation (claim-level source attribution). Up to 50% of the score is non-LLM — making it fully explainable and audit-ready.
Up to 50% of the GR score comes from non-LLM checks — domain rules, custom rule sets, and multi-model consensus. Fully auditable. Fully explainable. No black box.
The most valuable structural contribution Grounded makes to AI QA practice is the GR rating system — a five-tier reliability classification modelled on the logic of NCAP safety ratings for cars. GR-1 (0–41) is Critical — do not deploy. GR-2 (42–59) is High Risk — multiple checks failed, fix required. GR-3 (60–75) is Conditional — partial reliability, review findings before shipping. GR-4 (76–87) is Reliable — ready for deployment with monitoring. GR-5 (88–100) is Verified — all checks passed, safe to use as regression baseline.
For QA teams, this system solves a fundamental problem: how do you communicate AI quality to stakeholders who don't understand ML metrics? A GR-4 rating is immediately interpretable by a product manager, a compliance officer, or an executive. It provides a release gate threshold (block deployments below GR-4), a client report deliverable, and a regression tracking metric across model versions — all things that a raw accuracy score or a list of prompt failures cannot provide.
Grounded offers three testing modes. Response Audit is the core workflow — single question, single AI response, optional reference document, 8-layer GR score in under 60 seconds. This is the mode most useful for pre-release spot-checks, exploratory AI testing, and client deliverables. Batch Audit accepts a CSV or JSON file with up to 50 rows (question + AI response + optional reference document per row), producing per-row GR scores, a suite average, a pass rate, and a PDF report — essential for regression testing across a full question set. Conversation mode analyses a complete multi-turn AI transcript, providing per-turn GR scores, cross-turn contradiction detection, semantic drift analysis across the conversation, and a summary check.
Test analysts get an 8-layer verdict in 60 seconds with a client-ready PDF — replacing hours of manual response review. ML and AI engineers can track GR scores before and after model updates, prompt changes, or fine-tuning iterations, knowing immediately what changed and where. Product managers gain a reproducible GR score and PDF evidence on every feature shipped, answering the 'how do we know the AI is accurate?' question that previously had no structured answer. Compliance and risk teams get timestamped GR reports as evidence of due diligence before AI content is published or acted upon. For regulated industries — healthcare, legal, financial services, government — where a hallucination isn't a bug but a liability, Grounded provides the audit trail that internal and external auditors now expect.
Grounded offers 50 free runs to evaluate the platform — enough to run 2–3 meaningful tests and understand the GR rating system before committing. The Starter plan at $29/month provides 500 runs, all 8 validation checks, batch audit, custom rule sets, PDF reports and 90-day history. The Team plan at $149/month provides 5,000 runs with API access, CI/CD integration, unlimited batch audit, regression alerts and 1-year history. An Enterprise tier with branded client PDFs, custom GR thresholds and SSO is planned for Q3 2026.
The practical integration pattern for QA teams is straightforward. During development: run Grounded spot-checks on AI responses during exploratory testing, using domain rules and custom rule sets to validate against known facts in your domain. Pre-release: run a batch audit across your full regression question set and use the GR score as a release gate — any build scoring below GR-4 requires investigation before deployment. Post-release: use Scheduled Regression Monitoring to automatically detect GR score drops when a model update, prompt change, or data update is pushed. When something goes wrong: Grounded's timestamped PDF reports provide the audit trail needed for internal review, client communication, and regulatory reporting.
KiwiQA's AI testing practice incorporates Grounded into client engagements as the structured hallucination testing layer of the K-ASCI framework. Clients in healthcare, legal and financial services particularly value the evidence documentation Grounded produces — it directly supports the conformity assessments required under the EU AI Act for high-risk AI systems and aligns with ACSC guidance on AI governance in regulated Australian sectors. For teams that want expert-guided AI QA rather than a self-service tool, KiwiQA's consulting practice supports implementation alongside Grounded's platform capabilities.
TryGrounded AI fills a gap that has existed in the QA toolchain since GenAI entered production: a structured, evidence-backed, workflow-compatible method for testing AI output quality that doesn't require ML engineering skills to operate. The GR rating system gives QA teams a language for communicating AI quality to stakeholders. The model-agnostic architecture means it works across any AI product without integration overhead. The PDF report output makes it usable for compliance, client deliverables, and internal governance. For QA teams shipping AI products, it's the closest thing to a mandatory addition to the toolkit. Start with 50 free runs at TryGrounded.ai — no credit card required.