AI Risk Due Diligence
Find the risks hidden inside your AI workflows.
Hlinor reviews AI assistants, copilots, LLM workflows and automated decision systems to identify governance gaps, human-review weaknesses, audit-log blind spots and operational risks.
Designed for B2B SaaS, fintech, HR, legal, insurance, compliance and customer support workflows.
One concrete scenario
A support agent reads every incoming customer email, drafts replies and can issue refunds up to a limit. Here is what the review checks, question by question.
Sees
What the agent sees.
Which inboxes, tickets, order records and customer histories it can read. Whether personal or payment data enters its context that does not need to be there.
Which inboxes, tickets, order records and customer histories it can read. Whether personal or payment data enters its context that does not need to be there.
Decides
What it decides.
Which replies it sends on its own and which refunds it can approve. Where the limit sits. Whether a confident wrong answer reaches the customer unreviewed.
Which replies it sends on its own and which refunds it can approve. Where the limit sits. Whether a confident wrong answer reaches the customer unreviewed.
Human
Where the human is.
Which cases go to a person before sending: angry customers, large refunds, legal or medical topics. Whether the human sees the agent's sources or only its answer.
Which cases go to a person before sending: angry customers, large refunds, legal or medical topics. Whether the human sees the agent's sources or only its answer.
Logged
What is logged.
Whether every automated reply and refund leaves a record: the input, the decision, the data used, the model version. Whether an incident can be reconstructed a month later.
Whether every automated reply and refund leaves a record: the input, the decision, the data used, the model version. Whether an incident can be reconstructed a month later.
Failure
What happens on failure.
What the agent does when it is unsure, when the model provider is down or when a customer disputes an automated decision. Who finds out, and how fast.
What the agent does when it is unsure, when the model provider is down or when a customer disputes an automated decision. Who finds out, and how fast.
What you receive
- A map of what the agent sees and what it decides.
- A list of human-review gaps, ranked by customer impact.
- A logging and audit-trail review: what an incident would look like a month later.
- A failure-mode plan: what to limit, monitor, route to humans or redesign.
Good fit
- B2B SaaS using AI assistants, copilots or decision support.
- Customer support automation with real customer impact.
- Fintech, HR, legal, insurance, compliance or health workflows.
- Companies preparing investor, partner or enterprise-customer conversations.
Using AI in a sensitive workflow?
Start with a risk review before customers, investors or regulators ask harder questions.