Use case

Customer service conversational resolution.

From the field, AI native workflow redesign of conversational resolution process within Contact Center Customer Service function.

Get the playbook
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 conversational-AI deployment – reviewed the redesign at each checkpoint. Forward-deployed engineers built inside the team's CRM, knowledge base, and contact-center stack. One flat fee, artifact out, no retainer creep.

Situation

Today contact volume arrives across chat, email, and voice. Tier-1 agents resolve in fifteen minutes on chat, longer on voice. Repeat contacts run high.

Knowledge bases age. Macros stop tracking the live policy. Edge cases route to senior agents who already carry the worst tickets. Klarna handled two and a third million conversations a year on AI agents – two-thirds of chat volume, the work of seven hundred FTE – with resolution time falling from fifteen to two minutes and repeat contacts down twenty-five percent. Klarna later partially reversed for complex cases; the design lesson is that containment ceiling and escalation matter as much as the bot.

Resolution time ~15 min Tier-1 chat handle time
Containment Low Most chats need a human
Repeat contacts High Same issue, same buyer, twice
Senior agent load Heavy Edge cases land on the few experts

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.

Fifteen minutes per chat at the tier-1 ceiling.

Even strong agents cannot compress further; the bottleneck is context-pull and macro-find, not the customer's question.

Containment ceiling is design, not magic.

Klarna later partially reversed for complex cases. Brand risk on hallucinated policy answers is the failure mode without explicit escalation paths.

Senior agents carry the worst tickets.

Escalations land on the same five experts. Burnout and turnover destroy tribal knowledge faster than the KB rebuilds.

Resolution

The AI-native cycle.

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

Resolution time ~2 min ▼ ~85% on chat
Containment ~⅔ Klarna-band on tier-1 volume
Repeat contacts ▼ ~25% Better first-contact resolution
Senior agent load Light AI handles standard, humans rule on hard cases
Key changes

What the redesign actually shifts.

Resolution compression

  • Chat resolution moves from fifteen minutes toward two.
  • Containment lands in the Klarna band on tier-1 volume.
  • Repeat contacts fall roughly twenty-five percent.

Escalation by design

  • Hard cases route to humans with context and drafted next step.
  • Brand-sensitive scenarios route by policy, not guess.
  • Senior agents stop carrying every escalation.

Quality discipline

  • QA scores one hundred percent of interactions, not two.
  • Flags cite the conversation moment.
  • Coaching becomes a loop, not a sample.

Audit and control

  • Every action logs identity, policy band, and model version.
  • Containment ceiling is a design parameter, not a target.
  • Service owners read the same trail as audit.

Deploy this in your team.

The redesign above ships as a step-by-step playbook. KB ingestion spec, policy and escalation map, prompt library, QA rubric, and the rollout cadence we use on engagements.

Get the playbook Or book a coffee