How a Global Health & Wearable Technology Brand Is Automating QA for 46,000 Monthly Interactions, Including Their AI Chatbot

Why a Global Health Wearable Brand Chose AmplifAI Before Going Live

This is a case study about a decision, not a deployment. A leading global health and wearable technology brand spent four and a half months evaluating performance and quality platforms before signing with AmplifAI in late 2025. They have not yet rolled out the platform across their 110-person internal Member Service Representative team. They will, soon. This is the story of why they chose to.

The brand makes wearable devices and a subscription health platform that continuously monitors sleep, strain, stress, and heart health for millions of members worldwide. Their internal Member Service team handles 22,000 conversations a month across email and voice channels. An AI-powered chatbot handles another 24,000. That is 46,000 monthly customer interactions, half of them handled by AI and the other half by humans, all of them holding the brand's relationship with paying members.

The question that drove the purchase was simple. How do you scale quality across both?

Two Channels, One QA Gap, and an AI Chatbot in the Middle

Before AmplifAI, quality assurance at the brand looked like it does at most contact operations of this size. Manual reviewers sampled a handful of conversations per agent per month, scored them against a rubric, and fed the results back into one-on-ones. That model has two well-known ceilings. The sample is small enough that an agent might go a quarter without a representative score on their hardest call types. And the AI chatbot, which now handles more interactions than the agents do, falls entirely outside the scoring framework.

The team was clear about what they wanted on both fronts.

On the human side, they wanted Auto QA coverage across 100 percent of email and voice interactions, not a sample. Every conversation scored against the same form. Every coaching opportunity surfaced without a reviewer having to find it.

On the AI side, they wanted to apply the same scorecard to the chatbot. Treat the bot like an agent. Run the same form against its conversations. Find the gaps. Coach the prompt and the underlying knowledge base the same way a supervisor would coach a human on a hard call type.

That second requirement is what most quality platforms cannot do, and it is one of the reasons the evaluation took as long as it did.

The Supervisor Bottleneck Was Forty-Five Minutes Long

Supervisor efficiency was the other half of the business case. The brand runs bi-weekly one-on-one coaching with all 110 Member Service Representatives. That is 220 coaching sessions a month. Each one was taking a supervisor between 30 and 45 minutes to prepare for: pulling QA scores, finding the relevant call clips, opening the workforce management system to check schedule and adherence, opening the CRM to check ticket history, opening a spreadsheet to check trend over time, and assembling the whole thing into a coachable conversation.

The math on that bottleneck is unforgiving. At 40 minutes of prep per session times 220 sessions a month, supervisors were spending roughly 147 hours every month assembling coaching materials. That is most of a supervisor's monthly capacity, and it is the part of the job that produces no direct customer or agent value.

AmplifAI's Performance Enablement was modeled to bring that 40 minutes down to 5 to 10 minutes by consolidating every signal a supervisor needs into a single coaching prep view. Same scores, same clips, same adherence, same ticket history, in one place, with the AI surfacing the patterns worth talking about. The projected savings, conservatively, is 30 minutes per session, or 110 supervisor hours every single month. Those hours are intended to land back where they should: in live coaching on the floor.

What Wholistic Actually Means When You Have a Chatbot to QA

When the brand's leadership team summarized their reason for choosing AmplifAI, three words came up first: wholistic solution. That is a word a lot of vendors claim. In this case it meant something specific.

Most platforms in the evaluation could automate QA. Several could speed up coaching prep. A few could gamify performance for agents. None could do all of it on one platform, against one data model, for both human and AI conversations. The brand's leadership did not want four point solutions to integrate. They wanted one operating model. Coaching, QA, performance visibility, and gamification all running off the same signal.

The chatbot mattered most here. The brand's AI agent handles a slightly higher volume than the humans do, and that share is going to grow. If the QA platform could not score the bot, the brand would either need a second platform to do it or accept that the larger half of their customer interactions would go uncoached. Neither was acceptable. AmplifAI's auto-QA forms apply the same scoring criteria to AI-handled conversations as to human conversations, which is what made the wholistic claim real instead of marketing.

The Security Review That Took Months and Why It Mattered

Health data is regulated. Member conversations include personal health information. The brand's security and legal teams ran one of the most thorough Data Processing Agreement and security reviews AmplifAI has been through. It was not a checkbox exercise. Redaction logic was scrutinized line by line, including where in the pipeline it happens and what AmplifAI sees before it happens. The Business Associate Agreement was negotiated word by word.

From August through October of 2025, the deal was effectively in the hands of the security and legal teams while the operations team waited. That length of review is, paradoxically, the point. The brand was choosing a partner they trust with the most sensitive part of their member relationship. The thoroughness of the review was a feature, not a bug, and AmplifAI's willingness to go through it without rushing it was one of the three reasons the brand cited for choosing the platform: partnership, relationship, and trust.

The Business Case: 110 Supervisor Hours Returned Every Month

The business case the brand wrote internally was deliberately conservative. They did not model agent conversion lift, AHT reduction, or member satisfaction improvement, even though those are the typical outcomes of better coaching. They modeled one number: supervisor time.

Bi-weekly coaching across 110 MSRs equals 220 sessions per month. Average prep time falls from 40 minutes to 10 minutes. Thirty minutes saved per session, multiplied across 220 sessions, equals 110 supervisor hours back every single month.

That number alone funds the platform. Anything that lands on top, conversion lift, retention improvement, faster speed-to-proficiency for new hires, AI chatbot quality improvements, is upside the brand expects to see but did not need to count to justify the purchase.

The pricing model was equally clean. Annual license of $85,800. One-time implementation of $25,000. Per-form pricing for additional QA forms as the operation matures. No professional services arrangements, no AI tax, no opaque consumption pricing. The brand's finance team approved it without friction once the security team finished.

Scaling Beyond 110 to a BPO Network of 300

The 110-MSR internal team is the starting point, not the destination. The brand also runs its operation through a network of business process outsourcing partners totaling roughly 190 additional seats across three vendors. The plan is to extend the same performance and QA model into that BPO footprint, giving every leader, internal and partner, the same dashboard, the same coaching workflow, and the same QA standard.

That is the long-term reason a wholistic platform mattered. Stitching together point solutions to cover a 110-person internal team is hard. Stitching them together across an additional 190 BPO seats, with three different vendor environments, is functionally impossible. One platform, one data model, one coaching language, was the only architecture that scaled to where the brand intends to go.

The deployment work begins next. The case study to write twelve months from now will be the results case study. This is the case study about why the decision was the right one to make.