Proof, outside pharma
Four AI Builds for One Industrial Manufacturer
This work is outside pharma. It’s here because it shows what we build when a team wants AI working on its own data.
1. Finding the accounts nobody was watching
Six years of orders sat in the company’s ERP, with no easy way to see who was slipping away. We built a system that classifies every account by buying trajectory and refreshes each quarter.
- 804 accounts classified
- 264 lapsed accounts found, 33% of the base
- The top lapsed account had gone from $67K a year to zero
- 199 cross-sell openings surfaced
2. Giving the sales team a plan
Every account was being treated the same way. We sorted the base into clear segments and wrote a playbook for each one.
- 804 accounts sorted into 6 segments
- 6 sales playbooks
- 152 net-new prospects, each matched to specific products
3. Replacing spreadsheet reports with a live dashboard
Reports that took two to three days by hand became live dashboards, with four views built for the roles that use them.
4. Choosing the right trade shows
We mapped 80 trade shows and scored them down to 11 core events. For the top conference, we profiled 985 attendees, identified 580 priority prospects, flagged 32 existing customers and wrote a conversation opener for every attendee.
Want AI working on your team’s own data?
Tell me one job your team does every week. In 30 minutes we’ll work out whether AI can take it on inside your rules.