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08reference cases

What changes, and what stays yours.

Four worked scenarios showing how a workflow changes when a digital employee takes over the repetitive layer, and what stays firmly human. With a real client, these are not scenarios: we take a baseline before we build, and you watch the numbers move in your own dashboard.

Four digital employees are running at clients today. Their numbers are not here yet, because we only publish with a name and permission. What you see below are worked-out scenarios showing what changes inside a workflow. Want to speak to one of those four clients, ask in the intake call.

CASE 01 / e-commerce company, customer support triage

before

  • Every customer request read and sorted manually
  • Urgent issues buried under routine questions
  • Response quality depending on who picked it up
  • Backlogs after every campaign or busy weekend

after

  • Digital employee classifies and prioritizes each request
  • Draft responses ready for one-click human approval
  • Edge cases routed to the right specialist with context
  • Peaks absorbed without weekend overtime
Fasterhandling, without a rushed reply
< 1 hrfirst response, set by you
0unapproved replies sent

CASE 02 / operations-heavy SME, product stock planning

before

  • Stock checked by hand across sheets and systems
  • Reorders triggered by emergencies, not signals
  • Stock-outs discovered through customer complaints
  • Planning hours lost to data collection, not decisions

after

  • Digital employee monitors product and demand data daily
  • Stock risks flagged against real lead times
  • Reorder proposals prepared for planner approval
  • A steady weekly planning rhythm instead of firefighting
Fewermanual stock checks
Earlierrisk detection, not after the fact
100%orders human-approved

CASE 03 / marketing team, SEO operations

before

  • Keyword clustering done manually in spreadsheets
  • Content tasks created ad hoc, without clear priority
  • Ranking opportunities spotted weeks late
  • Strategy time consumed by data preparation

after

  • Digital employee clusters SEO data into scored opportunities
  • Prioritized, briefed content tasks land in the PM tool
  • Ranking decay flagged the week it starts
  • Editors decide and create, the pipeline runs itself
Soonerfrom idea to published
weeklyrefreshed pipeline
0unreviewed publications

CASE 04 / B2B service provider, data triage & reporting

before

  • Operational data scattered across five systems
  • Weekly report built by hand every Friday afternoon
  • Numbers already outdated by Monday's meeting
  • Deviations noticed only when they became problems

after

  • Digital employee collects and reconciles data on schedule
  • Structured summary delivered before the Monday stand-up
  • Deviations highlighted with the underlying detail
  • Management decides on facts, not reconstruction
Lessadmin, every week
every mondayreport on time
1 sourceof operational truth

The cases above are anonymized examples: they show the shape of the change, not measured client outcomes. Validated, client-approved results replace them as they are published.

→common questions

About the results.

Why are there no names and numbers here?+

They are not anonymised, they are written. We have clients running, but we publish their numbers only with their name and their approval underneath. Until that exists we would rather show an honest scenario than an anonymised number you cannot check. Speaking to a real client is possible, ask in the intake call.

How do you measure the impact?+

We agree the numbers before we build, hours returned, response time, error rate, throughput, and we take a baseline first, so there is something to compare against. From day one you see them yourself in your dashboard, updated as the work happens. Not a figure we hand you once a month.

Can we talk to a reference?+

Yes, on request we connect you with a client in a comparable situation, once both sides agree. Ask during the introduction call.