After-Sales & Preventive Maintenance (PM) Automation. 200+ installed machines tracked in Excel, reactively. AI activates them: automated PM alerts, proactive spare parts outreach, service scheduling. After-sales team recovers 15–20 hours/week. First results within weeks of kickoff.
Module 2
Quote & Proposal Generation. Only engineers can write specs today. Requests come via email and WhatsApp, each one manually assembled from catalogs across 13+ OEM brands. AI agent reads the request, matches products from the catalog, and drafts a proposal for review. Back-office stops being the bottleneck.
Timeline: ~8–12 weeks total, with early results expected within the first weeks of engagement. Start with Module 1 as a standalone Quick Win. No obligation to continue until you see the results.
How We Move Forward
Next Steps
Simple and low-friction. Three steps — the first one takes a WhatsApp message.
Step 1
Confirm the direction — WhatsApp is fine
No call needed
If this proposal reflects where Eurostar wants to go, just say so. A message is enough. We are not asking for a signed contract — just alignment on the direction before investing more time on both sides.
Step 2
Discovery retainer — $2,000 — meet the owners, go deep
5–6 hours of discovery work
This is a paid engagement — $2,000 — and it is where the real work begins. We meet the owners of each process (after-sales manager, technical lead), review the actual files, and do a structured deep-dive with clear goals and a defined time investment.
We come with a framework. Eurostar comes with open access to the people and the data. Together we map exactly what exists, what is missing, and what is possible.
One ask on your side: let your team know this is a serious project. The quality of what we build depends directly on how openly they engage. When people understand the stakes, the sessions deliver.
What we cover: Machine database audit, after-sales workflow mapping, quote generation process review, OEM catalog inventory, data quality assessment.
What Eurostar gets: A clear picture of the opportunity, validated by the people who live it every day.
Step 3
Final proposals for both modules
Output of Step 2
Based on what we find in the discovery, we deliver two fully scoped proposals: one for Module 1 (Preventive Maintenance Automation) and one for Module 2 (Quote & Proposal Generation). Each with exact deliverables, timeline, and investment.
This is the point where Eurostar decides how far to go — one module, both, or neither. No pressure either way. The discovery retainer stands on its own regardless.
We can do this. The pieces are already in place — the data exists, the team knows the process, and the opportunities are clear. The only thing left is to start.
Context
Executive Summary
Eurostar Machinery has 40 years of hard-won expertise locked inside people’s heads, Excel files, and WhatsApp conversations. That’s a competitive moat — and a ceiling. The two opportunities below don’t require rebuilding what works. They connect it, surface it, and make it run without Ferdinando needing to be in the room.
Through one discovery call, two areas emerged as the highest-impact, lowest-disruption opportunities:
After-sales & preventive maintenance — 200+ machines tracked in a spreadsheet, revenue left on the table every month
Quote & proposal generation — only engineers can write specs, every request manually assembled from 13+ OEM brand catalogs
The fastest path to value is Module 1: a 4-week after-sales automation that activates the installed base Eurostar already has — without adding a single new client.
Discovery
The Pain — In Your Numbers
These are the figures Ferdinando shared directly during our discovery conversation:
200+ installed machines across Mexico and LatAm, all tracked manually in Excel by one person. No automated alerts when a machine approaches its maintenance threshold.
12 months to get a new technician field-ready: 3 months in Italy, then paired with a senior tech on every installation. Knowledge lives in people, not systems.
3 AI users out of 48 employees. Only Ferdinando and the after-sales manager have paid ChatGPT. The rest of the company is operating at pre-AI speed.
Salesforce and SAP paid for but barely used. Salesforce may be cancelled. The real operating system is WhatsApp, Excel, and institutional memory.
Quote generation bottleneck. Only engineers can write proposals because they require technical specs from 13+ European brand catalogs. Requests come in via email and WhatsApp, each one manually assembled. If the engineer is in the field, the quote waits.
Admin team invisible to the CEO. Ferdinando is not in the office daily — and there is no system to tell him what is moving and what is stalled.
“No me interesa lo que hagan los técnicos porque siempre resuelven. Es más los tiempos muertos de admin.”
— Ferdinando Carfagno, on where the real inefficiency lives
200+ installed machines across Mexico and LatAm. All tracked in Excel by one after-sales manager. Service follow-ups happen when someone remembers, not when the machine needs it. Preventive maintenance kits (8,000 and 16,000 hours) are manually monitored — meaning PM contracts slip, spare parts revenue goes uncaptured, and clients only call when something breaks.
This is recurring revenue Eurostar has already earned. It just is not being collected.
What We Will Build
An automated after-sales engine built on top of the Excel data you already have:
PM alert system: When a machine approaches its 8,000 or 16,000-hour threshold, the after-sales manager is automatically notified — and the client receives a proactive outreach. No more manual calendar-checking.
Proactive spare parts outreach: If a client has not purchased parts in 6+ months, an automated follow-up triggers, personalized by machine type and OEM brand.
Service scheduling flow: Incoming maintenance requests automatically routed and logged — no more coordinating via WhatsApp and hoping nothing falls through.
CEO dashboard: Ferdinando sees the full PM pipeline at a glance, from anywhere, without calling anyone.
Expected Impact
15–20 hours/week recovered for the after-sales team. PM contract renewal rate up 20–30%. Spare parts revenue captured proactively instead of reactively. 200+ machines activated from day one — no new clients required.
Effort Estimate
4 weeks. Requires access to the Excel machine database and 2–3 working sessions with the after-sales manager. No new software licenses. No change management for the technical team.
2
Module 2: Quote & Proposal Generation
4–6 weeks development
The Problem
When a client needs a quote for equipment, spare parts, or a service package, the request goes to an engineer. That engineer manually looks up specs across catalogs from 13+ European OEM brands, checks availability, calculates pricing, and assembles a proposal. If the engineer is on-site or traveling, the quote waits.
Requests arrive via email and WhatsApp with no standard format. Some are a product code, some are a photo, some are a description. Each one requires a person who knows the full catalog to translate the request into a spec and a price.
What We Will Build
An AI agent that takes the manual work out of quote generation:
Request parsing: Incoming emails and WhatsApp messages are read automatically. The system extracts what the client needs: product codes, quantities, machine model, urgency.
Catalog matching: The agent searches across all 13+ OEM brand catalogs to find the right products, compatible alternatives, and current pricing. No more flipping through PDFs or calling Italy.
Draft proposal generation: A formatted proposal is auto-generated with the matched products, pricing, and lead times. The back-office reviews and sends, instead of building from scratch.
Complex case routing: When a request involves custom specs, non-standard configurations, or requires engineer judgment, the system flags it and routes to the right person with context pre-loaded.
Expected Impact
Quote turnaround from days to hours. Engineers freed from routine specs to focus on complex projects. Back-office team able to handle standard quotes independently. No more lost requests sitting in someone’s WhatsApp.
Effort Estimate
4–6 weeks. Requires access to OEM product catalogs (digital format), 10–20 sample quote request emails, current pricing structure, and 2–3 working sessions with the commercial team. We start with the highest-volume brand to validate, then expand.
Schedule
Timeline Overview
Module
Focus
Dev Effort
Timeline
1. After-Sales & PM
After-Sales Automation
4 weeks
Weeks 1–4
2. Quote Generation
Commercial Automation
4–6 weeks
Weeks 5–10
Module 1 begins immediately as a standalone Quick Win. Module 2 begins once Module 1 is live and delivering results.
Total engagement: approximately 8–10 weeks from kickoff to full rollout of both modules.
Process
How We Work
Each module follows the same cycle:
Phase 1
Discover
1 week. Working sessions with the right people to document the current process, edge cases, and what success looks like.
Phase 2
Build
2–5 weeks. Development with weekly demos to the department champion. No surprises at delivery.
Phase 3
Pilot
1–2 weeks. Limited rollout with a small user group. Feedback, fixes, calibration.
Phase 4
Roll Out
1 week. Full deployment with training and handoff documentation.
Each module includes 30 days of post-rollout support and optimization.
Two Critical Dependencies
The after-sales manager is the key person for Module 1. She owns the data and knows the process. We need 2–3 hours of her time in Week 1 to validate the machine database and confirm the PM logic. Everything in Module 1 flows from that session.
Module 2 requires access to the commercial team and product catalogs. The person who handles quote requests today is key. We need 2–3 hours of their time in the first week to walk us through how requests arrive, how they look up products, and what a finished quote looks like. Plus digital access to OEM catalogs and current pricing.
Pricing
Investment
Pricing will be discussed in a follow-up conversation once Module 1 scope is confirmed. We can structure the engagement as fixed-price per module, a monthly retainer, or a hybrid — whichever model works best for Eurostar.
Our recommendation: start with Module 1 as a fixed-price Quick Win. Four weeks, contained scope, measurable ROI. If the results are there, the conversation about Module 2 is easy. If they are not, you have not committed to more.
Each module’s investment covers: discovery, development, pilot, rollout, training, documentation, and 30 days post-rollout support.
Roadmap
What Comes After These Two Modules
Three areas are ready to explore once Modules 1 and 2 are running:
Technician Companion App
12-month onboarding cycle, knowledge trapped in people’s heads and WhatsApp. AI-indexed manuals across 13+ OEM brands, troubleshooting guides, service history per machine, daily learning modules. Cuts onboarding from 12 months to 6. Natural expansion once Module 1 service data is flowing.
Inbound Lead Qualification
Leads arrive via WhatsApp, email, and phone with no CRM and no structured tracking. If the volume justifies it, an AI agent can qualify, route, and log inbound inquiries automatically. We will assess volume during discovery to determine if this is worth scoping.
Spare Parts Demand Forecasting
107 European suppliers, long lead times from Italy. With service history data from Module 1 and quote patterns from Module 2, we can start predicting which parts will be needed and when — before the client calls.
Other areas for future consideration: LatAm geographic expansion (PM automation and quote gen work across geographies from day one) and client self-service portal.