ENSURING QUALITY AT SCALE FOR A HYPERGROWING FOOD-TECH PLATFORM
AI-DRIVEN QA FRAMEWORK THAT IMPROVED ACCURACY, SPEED & CONSISTENCY
- Background
- Problem
- Solutions
- Accomplishments
A rapidly scaling food-tech unicorn expanded customer support operations across multiple cities and partner vendors.
Daily ticket volumes grew into thousands, covering voice, chat, email, and social channels.
Manual QA could not keep up with expansion—audits were inconsistent, delayed, and fragmented across teams.
The company needed an automated, scalable, and bias-free QA system that could support hypergrowth without compromising customer experience.
Manual audits covered less than 2–3% of total interactions, creating blind spots in quality control.
Major inconsistencies between internal teams and outsourced partners led to fluctuating QA accuracy.
Coaching and feedback cycles were slow—often delayed by 7–10 days, impacting agent performance.
New-hire onboarding was inefficient, with no structured, measurable system to certify agent readiness at scale.
- Implemented Amanstra’s AI-powered QA system to automate interaction monitoring and evaluate 100% of selected samples daily.
- Built standardized scorecards, evaluation rubrics, and automated feedback workflows to deliver real-time coaching insights.
- Set up unified dashboards tracking accuracy, error categories, agent performance trends, and partner-wise quality metrics.
- Introduced an automated onboarding & certification module enabling structured ramp-up and performance tracking for all new hires.
QA coverage increased from 3% to 25–30%, significantly improving visibility across all partner operations.
Reduced QA variance by 40–50%, improving consistency in how interactions were evaluated across locations and vendors.
Cut feedback cycle time from 7–10 days to under 24 hours, accelerating coaching impact and agent improvement.
Enabled faster onboarding with structured certification—reducing ramp-up time for new agents by 30–35%.
Helped maintain high QA scores across thousands of agents even as the support team scaled rapidly during peak growth phases.
