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Research

Zynoviq Intelligence

Research on professional AI capability, evaluation and failure. What we learn about how AI handles professional work — on public models and synthetic cases, with our methods disclosed — so teams building AI can make better decisions.

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What we research

AI reliability →How often AI is confidently wrong on professional questions, and whether it signals doubt.Professional benchmarks →What makes a benchmark represent professional reality, and how it stays current.Domain intelligence →SAP · GST · Finance · Procurement · Supply chain — where AI meets real business rules.AI agents →Evaluating agents that act: workflow decisions, tool use and actions, not just answers.Red team →Ambiguity, exceptions and contradictions — how professional scenarios make AI fail.Human intelligence →Capability versus credentials, and why expert disagreement is valuable evaluation data.

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How AI fails at professional work →The failure patterns we test for, from fluent-but-wrong answers to premature agent actions, each with a synthetic example.LibraryIndia GST AI Report →How publicly available AI models handle Indian GST, tested twice a year with the method disclosed.ReportResearch and method notes →Our published research, research principles and the topics we are working on.ResearchArticles →Plain-English explainers on AI, Indian tax and how to test AI before it acts.ArticlesGlossary →Ground truth, adjudication, input tax credit and other terms, in plain words.ReferenceEvents and webinars →Talks for finance leaders, SAP teams and professionals.EventsFrequently asked questions →Short answers for buyers, AI labs and professionals.FAQ

Coming next

Planned articleThemeStatus
Why fluent AI answers can still be professionally wrongAI reliabilityComing
What makes a good enterprise AI benchmarkProfessional benchmarksComing
SAP AI: testing software is not the same as testing business correctnessDomain intelligenceComing
How to evaluate an AI procurement agentAI agentsComing
Why expert disagreement is valuable evaluation dataHuman intelligenceComing
Credentials are not capabilityHuman intelligenceComing

Titles may change before publication. Subscribe on the articles page to hear when they are published.

How we publish

  • Public models only in published comparisons — never a customer's system.
  • Methods disclosed, with honest limits.
  • Separate case sets from any customer's private tests.
  • No league tables of customers.
  • Corrections published if we find an error in our work.

Zynoviq Intelligence — FAQ

Do you publish results on customers' AI systems?+

Never. Published research covers only publicly available AI models, or methods and synthetic examples. We do not comment on any customer's system or results.

How do you choose research topics?+

From what we see in evaluation work: the kinds of professional reasoning AI finds hardest, the failures that recur across domains, and the questions buyers ask when deciding whether an AI system can be trusted with real work.

Are your methods published?+

Yes. Methods are disclosed with their limits, so others can check and challenge them. Research and method notes.

Can universities or institutes collaborate with you?+

Yes, on methods for evaluating AI in professional and regulated work. Contact us.

What if you find an error in published research?+

We publish a correction.

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