Back to home
Function · Customer Success

Artellis builds a tool that reads the signals and flags the risk weeks early, with the save-play written for you.

The pain

Why this one is worth fixing

You notice an account is unhappy when they are already halfway out the door. The signals were there: logins down, tickets grumpier, exec sponsor gone quiet. But nobody joined the dots until the renewal call was ugly.

What Artellis builds

A weekly read of the signals across usage, tickets and comms, with the risk flagged early and the save-play written for the account owner.

How it works
  1. 01
    Reads usage, tickets and comms
    Pulls the trend, not just the number: logins, feature use, ticket tone, exec engagement.
  2. 02
    Flags the risk with a reason
    ‘Renewal at risk: logins down 40%, no exec engagement since March.’ No black boxes.
  3. 03
    Drafts the save-play
    A specific next step and a briefing note the account owner can walk into a call with.
Example artefact
What the output actually looks like.
Acme Ltd · Health 62 · Renewal 18 days
Save-play, drafted
Signal: Weekly active users down 40% since March. No exec sponsor engaged since Q1.
Play: Book a 30-min check-in with COO this week. Lead with the two reports she used most last year.
Ask: Confirm renewal terms and one product ask worth pushing this quarter.
You edit and send.
Where it plugs in
Your product analyticsCRM (HubSpot / Salesforce)Zendesk / IntercomYour NPS / survey tool
Guardrails
  • Human-in-the-loop: nothing sends, signs or spends without you approving it.
  • Your data stays yours, hosted in your tenant or a dedicated Artellis workspace, never used to train public models.
  • Week-one build: something small and useful is live in the first week, before we scope anything bigger.
FAQ
How long until we see something working?
A first working version of the customer success tool inside week one. Refinement and rollout happen after you have seen it work against your real inputs.
What do you need from us to start?
About 45 minutes to walk through the current flow, sample inputs (emails, files, screenshots, whatever you actually work from) and one person on your side who owns the outcome.
What happens if the AI gets it wrong?
Every draft goes to a human before it leaves the building. When you correct it, the tool learns your standard so the next one lands closer to right.
Bring us this workflow
One workflow. One week. Something working.