Conversations are the work, so they deserve to be reviewed
Argus exists because quality teams across BPOs, contact centers, sales floors, banks, hospitals, and academies all face the same problem. They can only listen to a tiny fraction of the conversations their organization runs every day. We built an AI QA agent that listens to the rest, scores against the rubric the team already uses, and surfaces the calls a human actually needs to review.
Most QA programs are built around sampling. We think that made sense before the technology was here. Now it is just accepting the gap.
A typical contact center reviews two to three percent of calls. A typical sales team reviews even fewer. The coaching that happens on the back of those reviews is informed by maybe twenty conversations a month, picked semi randomly. The other ninety eight percent of conversations are simply unseen.
The cost of that gap is invisible until something goes wrong. A compliance miss surfaces in a regulator letter. An escalation surfaces in a churned customer. A great call from a top performer is never copied, because nobody heard it. Argus exists to close that gap with evidence, not theater.
Four principles that shape the product
Every score traces back to a transcript timestamp. No black boxes, no 'the AI said so'.
Argus exists to coach, not to catch. Anyone scored can dispute any finding and see exactly what the evaluator saw.
We are not trying to replace your QA team. We are trying to give them back their afternoons.
We disclose how our evaluations are produced, where they can be wrong, and what your team still has to decide.
We are clearer about what Argus is by saying what it is not
We do not score anyone without their organization knowing. We do not sell call recordings. We do not let anyone else train models on your data.
We do not track keystrokes, screen time, or attendance. We score the work product, which is the conversation.
We surface the calls worth a human look. The judgment about coaching, escalation, or discipline stays with your team.
Every finding cites a timestamp and a transcript quote. If you disagree with the AI, the dispute workflow takes about thirty seconds.