Predeep
Predeep is a cloud maintenance management platform from Italian vendor AIM. It combines scheduled maintenance with machine-learning failure prediction, connecting to existing Industry 4.0 machines, IoT sensors and databases to cut unplanned downtime.
What is Predeep?
Predeep is the maintenance management platform of Artificial Intelligence Monitoring s.r.l. (AIM), a Naples company registered on 2 October 2020 and listed the same day in Italy's special register of innovative startups. Its pitch is blunt: eliminate the downtime companies suffer through intelligent, simple, scheduled maintenance.
What distinguishes the product is that it joins two things the market usually sells separately. On one side it is a maintenance management system — work orders, planning, calendar, history, reporting. On the other it is a predictive layer: machine-learning algorithms compute a Health Indicator and estimate the risk that a given failure will affect a given component, so an intervention can be scheduled before a breakdown rather than after one. Both the homepage and the company page describe it as cloud-based software.
Seven capabilities are named. Predictive maintenance with the Health Indicator and its risk algorithms. Maintenance management and planning, from raising an intervention through to reporting, with a calendar view. Machine fleet exploration, which organises assets into a clear hierarchy attached to the production line, the department and the plant. Modular intervention management, which breaks a main job into sub-tasks to expose the critical phases. Complete reporting on key indicators and costs. Intervention history per machine. And automatic alerts when failure risk rises.
The platform is designed to sit on top of what a plant already has: it interfaces with Industry 4.0 machines, IoT sensors and databases, collecting, storing and organising the data the installations already produce. Press coverage cited by the vendor describes the approach as hardware independent, connecting to sensors already in place.
The site publishes three figures, presented as a real-world average across its customers: 45% less downtime, 30% lower maintenance costs and 15% better energy efficiency. Four sectors are addressed individually — manufacturing, machinery production, utilities and automotive — and two user profiles are named: production managers, who get the real-time dashboard, and maintenance staff and technical partners, who get the calendar and the reports. Development is co-financed by the European Regional Development Fund through the Campania 2021-2027 programme, under the project PREDEEP — Predictive Maintenance.
What it does
- Predict the risk that a specific failure will hit a specific component, before it happens
- Plan and track the whole maintenance process, from raising a work order to the closing report
- Watch the real-time health of every machine on an interactive dashboard
- Receive automatic alerts when the risk of failure becomes high
- Organise the machine fleet into a hierarchy tied to production line, department and plant
- Break a major intervention into sub-tasks to isolate the critical phases
- Produce reports on key indicators, costs and intervention performance
When to use Predeep / When not to
A quick filter to help you decide if Predeep is the right fit.
When to use Predeep
- Production managers on industrial sites where an unplanned machine stoppage is expensive, and who want a real-time health view of every asset
- Maintenance planners and technicians who currently juggle a spreadsheet, a paper calendar and a separate diagnostics tool
- Machinery manufacturers who want one shared reference point for their technicians and a structured after-sales service
- Utilities running critical water or energy infrastructure, where even a short interruption has a service and financial cost
- Plants already fitted with sensors that want to exploit the data they collect without replacing their instrumentation
When not to use Predeep
- Buyers who want to compare prices before talking to anyone: nothing is published and the only entry point is a demo request
- Teams looking for a free plan or a self-service trial, neither of which the vendor announces
- Organisations that need to be productive in days: the platform lands in about two weeks, but full predictive coverage takes three to six months
- Non-Italian-speaking teams, since the website, the documentation and the sales process are Italian only
- Engineering teams that need a public API, an SDK or a mobile app to embed maintenance data elsewhere: none exists
How to use Predeep
A typical end-to-end flow, from setup to results.
- Request a demo through the contact form: there is no self-service sign-up
- Attend the live demo session the vendor schedules with its team
- Step 1, kickoff: a workshop on your maintenance processes, technology stack, stakeholders and objectives
- Step 2, data acquisition: a workshop to connect the platform to your existing data, or to present the acquisition system to install if the data is not sufficient
- Step 3, model training: the algorithms are trained on your company's own data
- Step 4, platform delivery: a first fully configured version arrives in roughly two weeks
- Step 5, predictive model integration: full predictive coverage is completed over three to six months
- Day to day, monitor asset health on the dashboard and act on the automatic alerts
- Plan, reschedule and close interventions in the maintenance calendar, splitting big jobs into sub-tasks
- Pull reports on indicators, costs and intervention performance to steer the next cycle
Pros & Cons
Pros
- Planning and prediction live in one tool, where the market usually sells the CMMS and the predictive layer separately
- Connects to existing sensors, Industry 4.0 machines and databases rather than imposing proprietary hardware
- Genuinely modular: the predictive module can be switched on alone and plugged into a maintenance system already in place
- The rollout is written down and dated, with a first configured version delivered in about two weeks
- Per-customer isolated environments, encrypted data and encrypted communications, with extra security layers on request
- On-premise deployment is possible, which is unusual for a small cloud vendor
- Development backed by a named, verifiable European regional grant, which gives the roadmap some public accountability
Cons
- No published price of any kind: you cannot size the investment without going through a salesperson
- Neither a free plan nor a free trial is announced
- No terms and conditions, no legal notice and no data processing agreement are published, so no contractual commitment can be checked in advance
- The only legal document online is a privacy policy that covers the marketing site, not the platform or its machine data
- No list of subprocessors is published, and no hosting location for customer data is disclosed anywhere
- No security certification is claimed — neither ISO 27001 nor SOC 2 — and the headline performance figures are self-declared averages with no named customer behind them
- Italian only, with no public API, no documentation and no mobile app, from a very small and young vendor
Pricing & Plans
No price is published. Across the whole eight-page site there is no pricing page, no plan table and no figure of any kind, and the vendor announces neither a permanent free plan nor a free trial. Predeep is sold through contact: the only call to action is a demo request, and the offer is presented as tailored to each sector and each customer, which is consistent with the absence of a public rate card. Prospective buyers should also budget for the rollout itself, which involves workshops, possibly the installation of a data acquisition system, and a three to six month integration period.
Data, GDPR & hosting
A consolidated view of how Predeep handles your data.
GDPR overview
The vendor is an Italian company and therefore established inside the EU, so no Article 27 representative is required and none is designated. Privacy and cookie policies are hosted on iubenda and linked from the footer of every page. The privacy policy lists the usual GDPR rights — access, rectification, erasure, restriction, objection, portability, withdrawal of consent and complaint to the supervisory authority — and names the controller with a certified email address. No data protection officer is appointed. The list of processors is available on request rather than published. Processing locations are declared for the website's third-party services only: the United States for the Google services, Ireland for LinkedIn. Two limits matter. The site never claims to be GDPR compliant in so many words, and the policy covers the marketing site rather than the platform, so any GDPR fact stated here is revocable for Predeep itself.
Who owns the data?
The vendor publishes no terms and conditions, no service contract and no data processing agreement, so there is no public clause stating who owns the data a customer feeds into the platform. The only legal document online is a privacy policy hosted on iubenda, and it explicitly covers this Application — the aimonitoring.net marketing site, not the Predeep platform. It names Artificial Intelligence Monitoring, Via Traccia a Poggioreale 541, Naples, as data controller, reachable at a certified Italian mailbox. On the product side the site says only that each customer gets a dedicated environment, fully separated from every other and open exclusively to the users the company itself designates. Ownership of machine data should be settled in writing before signing.
Reuse rights
There is no contract to read, so nothing describes what a customer may do with the data or the outputs the platform produces. What the vendor does describe is its own use: at step three of the rollout the algorithms are trained on the company's data, for that company. No pooled or cross-customer use of machine data is ever claimed, and no shared model is mentioned. The privacy policy documents only the marketing site — contact form, mailing list, Google Analytics 4, Google Tag Manager, several Google Ads services and a LinkedIn widget. The two opt-outs it offers are advertising and analytics opt-outs, not a way to keep machine data out of model training, which the vendor never addresses.
Data retention & training
Hosting summary
The vendor publishes nothing about where the data of Predeep customers is hosted. There is no security page, no trust centre, no subprocessor list and no hosting jurisdiction statement anywhere on the site. The only processing locations that appear in the privacy policy belong to the marketing website's third-party services: the United States for Google Analytics 4, Google Tag Manager, Google Ads and Google Fonts, and Ireland for the LinkedIn widget. These say nothing about the platform itself and should not be read as the product's hosting. The policy otherwise uses a generic formula, stating that data is processed at the controller's operating premises and anywhere else the parties involved are located. The only hosting-related commitment the vendor does make concerns isolation rather than geography: each customer is given a dedicated environment, fully separated from the others and accessible only to the users the company designates, with encrypted data and encrypted communications. Jurisdiction, data centre location and subprocessors all have to be asked for directly.
Things to keep in mind
Risks and trade-offs to weigh before adopting Predeep.
- The product name and the vendor name differ: Predeep is the platform, AIMonitoring or AIM is the company. That is normal, but it makes the tool easy to search for under the wrong name
- With no contract, no terms and no data processing agreement published, everything that matters legally has to be negotiated privately, and a buyer without procurement support may end up accepting whatever is offered
- Nothing is published about where customer machine data is hosted or who processes it, which is a real gap for a plant whose production data is commercially sensitive
- The 45%, 30% and 15% improvement figures are the vendor's own averages, with no named customer, no period and no published study: treat them as a claim, not a benchmark
- The site's endorsements come from journalists and one contact at a utility, not from contractual customer references, and several are displayed with empty star ratings
- Predictive models are only as good as the data behind them, and they reach full performance after three months: acting on early alerts as if they were certainties, or letting the dashboard replace an experienced technician's judgement, is the practical risk
- The vendor is small and young, with a single employee in the last published accounts, so continuity of service and long-term support deserve an explicit contractual answer
Setup & Integrations
Technical difficulty
Moderate, and vendor-led rather than self-service. The rollout runs in five stages: two workshops, one on your maintenance processes and technology stack and one to connect your data, then model training on your own data, delivery of a first configured platform in about two weeks, and full integration of the predictive models over three to six months. The prerequisite is data rather than skill: Industry 4.0 machines, IoT sensors or databases already in place, with an acquisition system installed if what exists is not enough. No historical data is needed, and no developer work is required.
Deployment
Behind Predeep
Social
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
Do I need historical data to get started?
Can Predeep be installed on-premise?
We already have an ERP. Does Predeep integrate with it?
We already run a maintenance system and only want the predictive part. Is that possible?
How is my data protected?
How much does Predeep cost?
Is there a mobile app or a public API?
Who is behind Predeep?
Is the product available in English?
How long does it take to be fully operational?
Should you pick Predeep?
Predeep is a real product from an identifiable vendor. Artificial Intelligence Monitoring s.r.l. is registered and active in Naples, its VAT number resolves to the name and address printed on the site, and the development of the platform is backed by a named European regional grant with a verifiable project code. The proposition is clear and well bounded: bring maintenance planning and failure prediction together on the machines and sensors a plant already owns, rather than selling a new instrumentation layer.
The weakness is commercial opacity rather than technical substance. There is no price, no plan, no terms and conditions, no legal notice, no data processing agreement, no list of subprocessors and no statement of where customer data is hosted. The single legal document online is a privacy policy that explicitly covers the marketing website, not the platform. No security certification is claimed, and the headline figures of 45% less downtime and 30% lower maintenance costs are self-declared averages with no named customer or published study behind them.
That combination points to a specific reader. Predeep suits an Italian industrial site that is already instrumented, has a production or maintenance manager willing to sponsor the project, and can absorb a three to six month ramp-up. For that buyer the modularity is genuinely useful, since the predictive module can be switched on alone alongside an existing maintenance system.
The sensible approach is to treat the demo as a due diligence session rather than a sales pitch, and to ask in writing for everything the website does not publish: the price and its billing unit, the contract, where the data will be hosted, who the subprocessors are, and what happens to the machine data and the trained models if the relationship ends.
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