AI Call QA for a High-Volume Outbound Call Center

Most call centers assure quality by listening to a sample: typically 1 to 3 percent of calls. Everything else goes unheard. For a high-volume outbound call center, we built a platform that reviews every call instead.

The problem

Human QA does not scale. A team that samples a few calls per agent per week cannot catch systematic compliance drift, cannot coach from evidence, and cannot answer a simple question: what actually happened on the other 97 percent of calls?

What we built

An end-to-end AI review pipeline. Raw call recordings go in; what comes out, for every single call, is:

• a speaker-separated transcript,
• a score for each compliance criterion, marked pass, fail, or not applicable, each backed by a quoted line from the call as evidence,
• an overall compliance percentage,
• a concrete coaching tip for the agent,
• structured data extracted from the conversation itself.

Results land in a filterable dashboard built to span tens of thousands of calls, so QA leads move from spot-checking to querying: by agent, by campaign, by criterion, by score.

How it works

A multimodal model (Google Gemini) evaluates the audio against the center's own compliance rubric, so scoring reflects the client's actual standards rather than generic sentiment. Behind it sits a production task pipeline: scheduled ingestion, bounded concurrency, automatic retry on transient failures, and a self-migrating database schema, so the system absorbs new recordings continuously without an operator babysitting it.

What it proves

Compliance review stops being a sampling exercise and becomes a complete, auditable record. Every score traces back to a quoted moment in a real call, which makes disputes short and coaching specific. This is the pattern we bring to any operation drowning in recorded conversations: turn the archive into structured, queryable evidence.