Case Studies

What the machine looks like when it works

Representative outcomes from the kinds of engagements we run. Different stack, same physics: clean data in, automated motion out.

Who This Is For

Different seat, different question

These decisions get made by a group, and the group does not share a question. Here is what each seat gets out of the first two weeks, before anyone commits to a rebuild.

If you are the Your question is What you get in the first 2 weeks
CRO
Can I defend this forecast
Pipeline definitions audited, stage conversion rebuilt from raw data
CMO
What actually drove this deal
Attribution coverage measured, gaps named, model recommended
CFO
What is this stack costing per outcome
Tool spend mapped to usage, consolidation candidates ranked
VP RevOps
Where do I start
Prioritised written findings you can take to your own leadership
CEO / Founder
Why isn't AI showing up in revenue
Honest readiness read, including "not yet"

Series B SaaS, 140 employees

4 hrs → 90 sec

Speed-to-lead

Series A SaaS, PLG motion

~40% → 95%

Attribution coverage

Growth-stage, 60-rep sales org

10 hrs/wk → 0 hrs

Manual enrichment time per rep

Series C, multi-product

3 weeks → Same day

Board reporting cycle

Series B, outbound-led

1.2% → 4.8%

Cold email reply rate

Post-migration, HubSpot to Salesforce

6 mo backlog → 90 days

Migration and cleanup

Representative engagement outcomes. Specifics vary by stack, data quality, and team. We'll tell you what's realistic on the first call.