I build AI and voice systems with fintech-grade reliability, where deterministic code, not the model, makes the decisions that matter.

Collections calls are heavily regulated. A wrong balance, a discount the company never authorised, or a promise it can’t keep each create real liability, and a model left to its own judgement will produce all three.

• Split the system in two: the model runs the conversation, and a separate rule engine decides what counts as an acceptable repayment arrangement. The model can only quote figures the rules give it, so it cannot invent or approve anything on its own.
• Made the rules weigh every legal repayment option against what the caller says they can afford and choose the best one, rather than follow a fixed script.
• Built the legal requirements in directly: confirm who you are speaking to before discussing the debt, give the required disclosures, stop when asked, and never threaten or pressure. Every completed call is checked automatically for compliance.

• The agent cannot state an amount it was not given, and nothing that breaks the rules can be recorded even if it misspeaks.
• It was tested against dozens of difficult callers, including people disputing the debt or asking it to stop.
• It is live and handling real calls, from both a browser and a phone number.

An AI can draft a genuinely useful summary of a patient’s history. But it should not be trusted to decide whether a figure is accurate, which way a result is trending, or how serious it is.

• The model decides what to highlight and writes the summary. A separate check then verifies every statement against the patient’s actual record: the numbers have to match, the trend direction is recalculated, and the risk level comes from the record, not the model.
• Anything that fails a check is set aside in a clearly marked list with the reason, instead of being shown as if it were fact.
• The clinician makes every decision, and nothing can be signed off until each item has been reviewed.

• Planted errors, such as an altered number or a reversed trend, are caught every time, with no false alarms on the correct cases.
• The clinician can always see which claims were verified and which were rejected, and why.
• It runs on synthetic data only, with no real patient information involved.