Solutions
Make AI systems reliable and secure enough for production
AI agents and applications behave differently from traditional software. They can give different answers to the same question, change behaviour when the model is updated, and be manipulated through their inputs. We test them systematically for reliability, security and safe behaviour, so you can release with evidence rather than hope.
Why traditional QA is not enough
| Traditional software | AI systems |
|---|---|
| Traditional softwareTested against fixed expected outputs | AI systemsCan give different answers to the same question |
| Traditional softwareChanges when its code changes | AI systemsChanges when the model, prompt or data changes |
| Traditional softwareInputs are validated, then processed | AI systemsCan be manipulated through its inputs |
| Traditional softwareCalls between systems are fixed in code | AI systemsChooses tools and actions itself |
AI systems need systematic evaluation on top of traditional quality engineering, and they need it again every time something changes.
What we test
Does it work?
- Agent evaluation: does the agent achieve the intended outcome?
- LLM and functional evaluation: correct behaviour across expected scenarios.
- Hallucination and factuality testing where accuracy matters.
- Regression evaluation after model, prompt, workflow or code changes.
Does it use its tools correctly?
- Tool-use validation: the right tool, the right parameters, the right order.
- Permission and boundary testing: can it act outside its authority?
Is it secure?
- Prompt-injection and adversarial testing.
- Data-leakage and access testing.
- AI security testing of tools, integrations and outputs.
Can people oversee it?
- Human-in-the-loop validation: are escalation and approval paths working?
- Observability and traceability: can you reconstruct what the system did and why?
- Compliance evidence support: records of inputs, outputs, decisions and approvals.
Also: QA for AI-generated software and agentic development, testing code written with AI assistance and the pipelines that produce it.
Embedded or independent
Embedded assurance
Built into the AI systems we deliver for you.
Independent assurance
Testing of AI systems built by your team or other vendors, by a separate SYGNISYS assurance team. If we built the system, we tell you before we assess it.
Findings your risk team can use
We map findings to frameworks your risk and security teams already use, such as:
- NIST AI Risk Management Framework
- ISO/IEC 42001
- OWASP guidance for LLM and agentic applications
Oversight designed around consequences
We design human oversight to match the cost of an error, using four patterns. Every assurance report states which pattern each step uses and whether it is working.
- Human executes, AI assists
- Human directs, AI executes
- AI executes, human reviews
- AI executes, human handles exceptions
Our assessments and reports support your risk and compliance work. They are not a certification, audit opinion or legal advice.
Assurance doesn’t end at release
Models, prompts and data change. We re-run your evaluation suite on every significant change and monitor production behaviour, so regressions are caught before your users find them.
Start here
- Assess your agent
Agent Assurance Assessment
For teams building or deploying AI agents. We test reliability, tool use, permissions, security and human oversight, then give you a findings report and remediation plan.
Frequently asked questions
What is AI assurance?
AI assurance is systematic testing of AI agents and LLM applications for reliability, security and safe behaviour, so you can release them with evidence rather than hope.
Why is traditional QA not enough for AI?
AI systems can give different answers to the same question, change behaviour when the model or prompt changes, be manipulated through their inputs and choose tools themselves. They need systematic evaluation on top of quality engineering, and again after every change.
What do you test?
Whether it works (agent and functional evaluation, hallucination, regression), whether it uses its tools correctly (right tool, parameters and permissions), whether it is secure (prompt injection, data leakage) and whether people can oversee it (escalation, approvals, traceability).
Can you test an AI system another vendor built?
Yes. Independent assurance is carried out by a separate SYGNISYS assurance team. If we built the system ourselves, we tell you before we assess it.
Is an assurance report a certification?
No. We map findings to frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001 and OWASP guidance for LLM applications, to support your risk and compliance work. It is not a certification, audit opinion or legal advice.