Service margin, made visible.
Your ERP knows what you sold. It doesn't know what is happening at the plant today. ZopaAI connects your service history and quote log with external signals such as investment, ownership changes, hiring, expansion, and industry activity to score every account across the machine lifecycle.
Mechanical engineering
The installed base, turned into a clear sales plan
For many machine builders, a large part of the customer relationship happens after the first sale, through service, spare parts and retrofits. The signals that point to the next opportunity, like a new plant, an ownership change or a drop in parts consumption, are often spread across systems or not captured at all. ZopaAI links your installed base, service history and quote logs with external signals on investment, expansion and competitor activity, and scores every account across the whole machine lifecycle. Your team sees which contracts may be at risk, which machines are coming up for replacement and where the next sale is most likely to come from. Service and new business can then be planned together rather than side by side.
ZopaAI stands for reliable, scientifically verified AI for use in companies, in order to avoid the problem of AI hallucinations.
Newspaper, Frankfurt
Eight building blocks, usable on their own, together a steering system
All blocks run on the same data foundation and the same score logic. You can start with one of them without having to rebuild the others later.
Pick your role, the modules used day to day in that role are highlighted.
Account scoring & prioritisation
A/B/C classes come out of last year's revenue and tell you who bought, not who is about to — and the scored list that replaces them is still too long for the week. ZopaAI scores every account on expected twelve-month potential from fleet, service behaviour and external investment signals, then cuts it into a ranked work list per rep, sized to the capacity actually available.
- One score per account, with the reasons shown
- Ranked work list per rep, sized to real capacity
- Separate scores for end customer, dealer, service partner
“Account 3042, potential 81/100. Drivers: 12 machines past 8 years, capex announcement in Q3, two maintenance roles posted. Rank 4 of 9 on the Central list this month.”
Illustrative wording. Figures and names are not from a real client project.
Retrofit & replacement timing
The replacement conversation usually starts when the customer asks — by which point a competitor has already quoted. ZopaAI estimates the replacement window per machine from age, service intensity and the customer's own investment behaviour, and puts it on a calendar your field team can plan against.
- Replacement window per machine, by quarter
- Rising service cost flagged as a replacement signal
- Customer capex cycle matched against your quote timing
“18 machines enter the replacement window within three quarters. 5 of them sit at sites with an announced capacity expansion.”
Illustrative wording. Figures and names are not from a real client project.
Service contract retention
A service contract lapses quietly, and by the renewal date a third-party provider has often been on site for a year. ZopaAI flags contracts at risk months ahead, using service frequency, parts drift and signs that another provider is already working the machine, and ranks the effort by contract value at risk.
- Risk flag per contract, months before renewal
- Third-party presence detected from parts drift
- Renewal effort prioritised by contract value at risk
“9 contracts flagged at risk, combined annual value €780k. In 4 cases parts consumption dropped while machine hours stayed flat.”
Illustrative wording. Figures and names are not from a real client project.
New customer discovery
Trade fair lists and association directories give you names, not reasons, so the field cannot tell a prospect from a placeholder. ZopaAI identifies plants outside your base with a fleet and process profile matching your best customers, and ranks them by how close they are to a decision rather than by company size.
- Look-alike plants, matched on fleet and process profile
- Ranked by timing signal, not by company size
- Competitor installations flagged where visible
“63 plants in DACH match your top-quartile customer profile. 11 show an active investment signal within the last two quarters.”
Illustrative wording. Figures and names are not from a real client project.
Cross-sell & upsell
Spare parts and service sell themselves at the big accounts, while the long tail carries the margin and never gets worked. ZopaAI compares each site's parts and service consumption against comparable fleets, flags what is missing, and attaches an expected order value so the list can be worked top-down.
- Consumption gap per site, measured against peer fleets
- Expected order value, so the list can be prioritised
- Long-tail accounts surfaced without manual review
“47 sites consume below peer level for wear parts. The top 12 carry an expected gap of €410k over twelve months.”
Illustrative wording. Figures and names are not from a real client project.
Region overview & territory planning
Territories were cut years ago along postcodes, and nobody knows where the potential actually sits or whether headcount matches it. ZopaAI maps potential, coverage and travel effort per territory, so headcount and budget can be set against potential instead of history — and re-cuts can be modelled before they are implemented.
- Potential and coverage per territory, side by side
- Travel effort weighed against expected effect
- Cut scenarios modelled before they are implemented
“Territory South carries 1.8× the potential of Territory North at the same headcount. Rebalancing three postcodes shifts 340 accounts without adding travel time.”
Illustrative wording. Figures and names are not from a real client project.
Next-best-action recommendations
Knowing an account matters is not the same as knowing what to do with it on Monday. ZopaAI turns each scored account into one concrete next step — the occasion, the channel and the timing — so the field opens the conversation with a reason rather than a check-in call.
- One concrete next step per account, not a status label
- Occasion named: what changed and why now
- Channel and timing assigned: visit, inside sales or digital
“Account 3042: quote for a wear-part package before end of Q2. Occasion: two machines past the service interval while output stayed flat. Channel: on-site, combined with the Hanover route.”
Illustrative wording. Figures and names are not from a real client project.
Competitive analysis
You find out you are up against a competitor when the quote is already on the table, and you rarely learn where they have quietly taken over part of your base. ZopaAI detects competitor presence per site from parts drift, service gaps and public installation signals, and shows where your share of a customer's fleet is shrinking.
- Competitor presence per site, not per market report
- Share of fleet trend: where your base is eroding
- Displacement candidates ranked by recoverable volume
“At 23 sites competitor equipment has entered the fleet since 2024. In 7 cases your parts volume fell by more than 40 % while machine hours stayed flat.”
Illustrative wording. Figures and names are not from a real client project.
What changed at a mid-sized machine builder in 12 months.
Scope of the case study
- Client
- Machine builder, special-purpose machines and series plant (anonymised)
- Territory
- DACH and Eastern Europearound 2,400 machines in the field18 sales staff
- Implemented
- Installed-base analysisprioritisationcross-sellterritory planning
- Period
- 12 months, rolled out in two waves, measured against control territories
Full case study as a PDF
Two pages on initial situation, data foundation, methodology, results. Get access to the link.
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Presented anonymised. Figures from the client's own project controlling.
We were still pushing new machines into accounts where the service business was already gone. Now we defend the service accounts first. The machine sale follows from that, not the other way round.
Reference from an ongoing project in mechanical engineering
Questions we hear first
Short answers before your first call.
Yes. Service history is one of the strongest predictors of retrofit and replacement, so it is one of the first sources we connect. Where machine runtime data is available, it goes in as well — but nothing depends on it.
Choose your industry and time.We will match you with an expert.
Not another sales pitch, but an honest discussion focusing on your needs. 30 minutes, no sensitive data, no risk, no cost.
- Industry lead, not a generalist
- Valuable discussion, honest assessment.
- Focused on discovering potentials.