Case studies

Proof, not promises.

Five pieces of client work, written up with the numbers we can share. Each one started the same way: a leadership team that needed a decision made about something it could not see clearly. What follows is what we found, what we built, and what changed.

Clients are described by sector rather than named, at their preference.

It's obvious Mind Mechanics and their partners know what they are talking about. Building AI agents through them will save us millions of dollars.

VP of Operations, Large healthcare company

I've spoken with many professionals in this space who only provide high-level recommendations. I appreciate the deeper insight and thoughtfulness Mind Mechanics puts into their analyses. Their insights on how to position my company's brand continue to stick with me.

CEO, AI-driven ecommerce company, Switzerland

I enjoy learning from Mind Mechanics about how AI is being applied across different industries. It sparks a lot of ideas for me.

Senior Director, Oracle

Ecommerce

US market entry for a Swiss ecommerce company

Problem: A Swiss ecommerce company had watched profitability slow in Europe and needed a revenue strategy for the US market, with limited funding and almost no exposure to American buyers.
Solution: We ran the company's own meeting and call recordings through AI-assisted analysis to surface what buyers were actually saying, built the go-to-market strategy on what came back, then tested it live with a ten-week US paid pilot.
Outcome: A go-to-market strategy the CEO still cites in decisions today
32 US leads delivered at roughly EUR 220 each, confirmed against the client's own records
One messaging angle proved out over three, settling an internal argument with live data

Healthcare

Operational visibility for a global medical device maker

Problem: A global medical device manufacturer sensed its operational costs were too high but could not locate them. Travel and expense monitoring was manual, report generation was slow, and inconsistent data made enterprise-wide comparison impossible.
Solution: We built an AI proof of concept in the area of maximum impact, plus a demo dashboard for the COO that pulls data from across the enterprise into one place for analysis and decision making.
Outcome: A single view across the whole enterprise for the first time
Cost savings through AI automation of manual monitoring work
A prioritized list of AI projects ranked by bottom-line impact

SaaS

Rebuilding a CRM a sales team could trust

Problem: A subscription software company could not trust its own pipeline numbers. Ten thousand customer records, 120 workflows and 46 forms had gone unaudited for two years, and sales was working from stale data.
Solution: We connected AI directly to the CRM with custom connectors for reads and bulk writes, plus browser automation where the API stops, then ran dry run, sales sign-off, batch write and recount on every change.
Outcome: Open deals corrected from 4,240 to 289, every change reversible
4,002 records re-routed with zero failures
836 real churns surfaced where the CRM had shown 55

SaaS

Finding the channel that actually made customers

Problem: Leadership needed to know which channels were producing customers before scaling spend against growth targets. The CRM, the ad platforms and the sales team each reported a different answer.
Solution: We traced every demo from first touch to closed deal, coding 132 demos against sales notes and 218 CRM chat threads, with parallel agents checking 45 downloads against a public registry.
Outcome: Referrals identified as the strongest channel, and invisible to the CRM
Three of four headline numbers corrected
Conversion tracking fixed so demos could be counted

Insurance

AI agents for commercial insurance quoting

Problem: A commercial insurance agency spent up to three hours quoting one customer, re-keying identical intake data into a dozen carrier portals. Most carriers offer no API and several enforce two-factor login.
Solution: We built schema-driven intake, a rules preflight, and one browser agent per carrier running in parallel, with two-factor pause and resume, human approval before bind, and a full audit trail.
Outcome: Three hours of re-keying designed down to minutes
A working prototype backed by 90 automated tests
Routine steps running as code at zero token cost

The first step

Every one of these started with a question.

Eight questions about how your business runs, and you get back a shortlist of where AI would move a real number. Yours to keep, with us or without us.

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