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Something new at Sierra

Predict repeat contacts after conversations appear complete

You’re the ai / ml engineer. Your team is in the room. Printed Oct 19, 2026.

A finished conversation is pulling against evidence that the customer is actually finished.

Support leaders need fewer repeat contacts, while the first prediction must avoid treating every return as a failed resolution.

Who you’d be doing this for

“The dashboard says we finished the case, then my queue gets the same customer back two days later.”

Dalili Wanjala · VP of Customer Care

Owns automation performance and repeat-contact volume for a multi-channel consumer service operation.

What is at stake

Seven-day repeat contact reaches 24% in SMS long-horizon cases despite similar conversation-end rates. You must weigh a useful early-risk signal against incomplete links between channels and actual unresolved needs.

Why it isn’t already fixed

Every obvious fix costs something else. That’s the part you’d have to decide.

  • apparent completion vs durable resolution
  • broad reuse vs defensible labels
  • early pilot speed vs cross-channel evidence
  • prediction usefulness vs customer-facing safety

Why Sierra

Long-horizon agents operate across voice, SMS, email, and chat, where a resolution may only prove itself after the next customer contact.

Written with these in mind

Applied ML product builderEvaluation-focused ML engineerCustomer-outcome modeler

Not your kind of problem? 28 more at Sierra, or browse every organization.

This is the setup. The work is inside.

Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.