AI agent evaluation in Atlanta.

The technical reality of interaction-AI evaluation in Atlanta.

Questions from Atlanta buyers.

Our automated QA scores disagree with our human QA team. Which one is wrong?

Usually both, in different places, and the alignment work is where the value lives. The method: run automated and human QA on the same sample of interactions (a few hundred calls or chats), measure agreement per rubric dimension, and investigate the disagreements in three buckets. Rubric ambiguity: dimensions where humans also disagree with each other (empathy scores are notorious); the fix is rubric clarification with anchored examples, which improves both scorers. Judge miscalibration: dimensions where humans agree and the model diverges (sarcasm read as rudeness, policy-correct refusals scored as unhelpful); the fix is judge-prompt tuning against the human standard until agreement is demonstrated. Human drift: dimensions where the model is consistent and individual reviewers diverge from the documented rubric (the senior reviewer's personal standards); the fix is a calibration session your QA leadership runs. The end state is automated scoring with per-dimension agreement numbers attached, covering every interaction, with humans auditing samples and owning the standard. The interim discovery is usually that the rubric itself needed the workout.

How do we measure whether live agent-assist actually helps, beyond asking agents if they like it?

With paired operational metrics and suggestion-level telemetry, because liking and using are different facts. The instrumentation: every suggestion logs whether it was accepted, edited, or ignored (acceptance rate by intent class is the first honest signal; an assist ignored 80 percent of the time is decoration with a latency cost), and operational outcomes get compared across matched conditions: handle time, hold time, transfer rate, first-contact resolution, and QA scores for assisted versus unassisted interactions of the same intent mix, ideally via staged rollout so the comparison has a control group. The subtleties that matter: acceptance without edit can mean trust or rubber-stamping (we sample-audit accepted suggestions for correctness), handle-time improvements concentrate in specific intents (the per-intent breakdown tells you where to expand and where to retire the assist), and new-agent versus veteran splits often diverge sharply (assists frequently help ramp time more than veteran throughput, which changes the ROI story). The deliverable is an assist scorecard your ops leadership reads quarterly.

What accuracy should we expect from dispute and chargeback classification, and how is it verified?

On well-built systems: high-90s on clear-cut reason codes, mid-80s to low-90s on the genuinely ambiguous middle, and the verification design matters more than the headline. The harness: a labeled set built from adjudicated disputes (cases where the final outcome is known, which makes labels ground truth rather than opinion), stratified across reason codes, card networks, and the ugly variants (partial shipments, subscription cancellations, friendly fraud patterns), scored for classification accuracy plus the operationally expensive errors: misroutes that send a winnable dispute down the wrong evidence path, and deadline-relevant misreads. Confidence calibration gets verified so thresholds mean what they claim, and the auto-route versus review split gets set against your evidence-assembly costs and win-rate data. Ongoing, classifications get scored against final adjudications monthly (the outcome data arrives on its own schedule), which catches drift against network rule changes, the chronic hazard in this domain since the networks revise reason codes and evidence requirements regularly.

What does an evaluation program return in dollars for a contact-center operation?

Three quantifiable lines, and an honest accounting names all of them. QA coverage economics: replacing sampled human QA (typically 2 to 5 percent of interactions) with aligned automated scoring on 100 percent plus human auditing of samples usually nets out positive immediately at center scale, while surfacing the coaching signals sampling missed. Regression prevention: a prompt or model change that degrades containment or compliance behavior costs real money per day undetected (your volumes make this arithmetic vivid); smoke gates and drift alarms convert that from a monthly risk into a caught-in-CI event, and one prevented incident typically pays for the program. Decision quality: measured per-intent assist performance and verified containment numbers redirect investment from where AI demos well to where it performs, which is the quiet compounding return. What we do not claim: evaluation does not improve the AI by itself; it tells you precisely where improvement money goes and proves whether it arrived. The ROI report we deliver prices all three lines in your volumes and rates.