Use case

Agent assist and AI-driven QA.

From the field, AI native workflow redesign of agent assist and qa process within Contact Center Customer Service function.

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Convolving expertise

A senior Convolving delivery team partnered with the customer service function for one sprint. Operators from our expert network – with forty combined years inside contact-center operations and quality programmes – reviewed the redesign at each checkpoint. Forward-deployed engineers built inside the team's CRM, KB, and QA-platform stack. One flat fee, artifact out, no retainer creep.

Situation

Today QA scores roughly two percent of interactions by hand. Agent-assist is a search bar over the knowledge base.

Coaches sample a handful of calls per agent per month. Most agents see feedback when they miss a target, not while they handle the call. Cresta, Salesforce Service Cloud Einstein, and Zendesk QA converge on real-time macro suggestion and full-coverage scoring; the legacy stack samples and lags.

QA coverage ~2% Sampled by hand
Coaching latency Weeks After the call ended
Macro hit rate Variable Agent searches by hand
Tone consistency Drifty Brand voice varies by agent

Click any node to see the activities and tools behind it. Open the canvas in fullscreen for the horizontal view.

Complication

Largest obstacles and inefficiencies.

Two percent QA coverage misses ninety-eight.

Coaches read a handful of calls per agent per month. Most interactions never enter the feedback loop.

Coaching arrives weeks after the call.

By the time an agent hears feedback, the call is forgotten. Behaviour change happens slowly, if at all.

Tone drifts under volume.

Brand voice varies by agent and by shift. Drift compounds across hundreds of agents and millions of interactions.

Resolution

The AI-native cycle.

Same five steps. Click any node to see what the redesign does in that step.

QA coverage 100% From sampled to full
Coaching latency Live In-call assist, post-call review
Macro hit rate Uniform Right macro, every interaction
Tone consistency Tight Brand voice scored continuously
Key changes

What the redesign actually shifts.

Coverage

  • QA moves from a two-percent sample to one hundred percent of interactions.
  • Tone and adherence score continuously, not quarterly.
  • Coaching becomes a loop, not a sample.

Coaching latency

  • Agents see assist in the call, not feedback weeks later.
  • Drafted coaching briefs land weekly, not quarterly.
  • Behaviour change compounds inside the cycle.

Macro discipline

  • Right macro surfaces in the conversation, not a search bar.
  • Stale macros retire automatically.
  • Policy updates propagate into assist on push.

Audit and control

  • Every flag cites the conversation moment.
  • Every macro logs version and policy alignment.
  • Quality leaders read the same trail as ops.

Deploy this in your team.

The redesign above ships as a step-by-step playbook. Assist prompt library, QA rubric, macro learning pipeline, coaching brief template, and the rollout cadence we use on engagements.

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