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Solution

Enterprise AI: built for your company, on your own servers

Enterprise AI is a set of decision systems that process business data with advanced mathematical and statistical methods to predict and optimise business outcomes. With a Data-First, AI-First approach, ORELSIS builds end-to-end AI solutions for production, customer engagement and business intelligence. Where needed, models run in the field with Edge AI, as close to the data as possible.

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OREL AIOn-premise · data never leaves
Which equipment is at risk this week?

Based on the last 72 hours of vibration, temperature and current data, three assets stand out. The highest risk is the P-102 brine pump: bearing temperature and vibration are rising together.

Failure risk — next 7 days%
P-102Brine feed pump82
K-201Chlorine compressor46
F-115Filter press31
Suggested actionReplace P-102 bearingAdd to Thursday 06:00 planned stop · ~6 h downtime avoided
Schedule
Sources: OREL IMS · SCADA · 72 h
Ask OREL AI…
Capabilities

AI that starts with your data and ends in a decision

01

More efficient production

Generates forecasts from demand, capacity and downtime data to improve planning and resource use.

02

Effective customer engagement

Segments customer behaviour, anticipates needs and gives sales and support teams timely recommendations.

03

Dynamic decision-making

Recalculates scenarios as conditions change and presents decision-makers with reasoned options.

04

Business intelligence

Brings scattered data into a single dashboard, tracks KPIs and reports deviations automatically.

05

Edge AI

Runs models on field devices for low-latency inference and less dependence on the network.

06

Analytical assessment

Tests hypotheses with advanced mathematical and statistical techniques and makes results measurable.

Guide

Enterprise AI: what you need to know

What is enterprise AI?

Enterprise AI is an AI system that works on a company's own data, connects to its own processes and follows its own security rules. Unlike general-purpose chat services, it reads documents, ERP, production, quality, maintenance and finance data together, answers according to user permissions and keeps data inside the company.

ORELSIS installs enterprise AI on-premise under the name OREL AI. The model runs on your servers and your data stays there; if required, it can even run on a network fully isolated from the internet.

Why should a company run its own AI?

  • Data security: trade secrets, recipes, costs and customer data are never sent to a third-party service.
  • Compliance: personal data is not transferred abroad; access is permission-based and every query is logged.
  • Accuracy: because the model draws on your documents and systems, its answers reflect how your company actually works.
  • Process integration: answers become work orders, maintenance plans or quality records, not just suggestions.
  • Cost control: a predictable infrastructure cost instead of variable per-user and per-query cloud fees.

How do you deploy AI in a company?

A successful enterprise AI project starts with data and the right use case, not with the model. The ORELSIS deployment approach has four steps: building a data inventory and security framework, choosing a measurable use case, installing the model on-premise and validating it on your data, and finally connecting it to ERP, MRP, quality and maintenance processes.

The first use case is usually a document and procedure assistant, predictive maintenance or demand forecasting — their data is ready and their impact is measurable, so they deliver value quickly.

Enterprise AI use cases in manufacturing

  • Predictive maintenance: catching failure precursors in vibration, temperature and current data.
  • Demand and plan forecasting: reading orders, capacity and stock together to plan further ahead.
  • Root-cause analysis: linking quality deviations to shifts, lots and equipment.
  • Energy optimisation: tracking consumption per tonne and flagging lines that are off target.
  • Knowledge assistant: answering from procedures, instructions and technical documents according to each user's permissions.

Public AI services vs. OREL AI

Public AI serviceOREL AI (on-premise)
Where data livesThe provider's serversYour own servers
Cross-border transferNeeds assessmentData never leaves the company
Answers from company dataLimitedFrom documents, ERP and shop-floor data
Permissions & audit trailGenericRole-based, every query logged
Process integrationManualLinked to work orders, maintenance, quality
Air-gapped operationNoYes, fully isolated if needed
Our approach

How we deliver it

  1. 01

    Prepare the data

    We first inventory your data sources, measure their quality and build a single governed data layer with OREL DM.

  2. 02

    Choose the use case

    We prioritise use cases that are tied to your business goals, measurable and backed by available data, and agree on success criteria up front.

  3. 03

    Build the model

    We develop forecasting, optimisation and classification models with statistical methods and machine learning, and validate them on real data.

  4. 04

    Embed it in operations

    We deploy the model in the cloud, on-premise or at the edge, monitor its performance and retrain it on a regular schedule.

Related products

Products behind this solution

Production & Planning

OREL MRP

Plan production, track the shop floor, improve continuously.

Quality Management System

OREL QMS

Automate your quality processes and regulatory compliance.

Customer Relationship Management

OREL CRM

Manage customer, lead and partner relationships on one screen.

Data Management Platform

OREL DM

Unify your data and turn it into value with AI.

FAQ

Frequently asked questions

It depends on the use case and how ready the data is. During discovery we assess your data and give a clear timeline and scope for the first use case. Use cases such as a document assistant go live faster than those that need data integration.

Because OREL AI runs on-premise, personal and commercial data is not transferred abroad. Access is role-based, every query is logged and retention follows your policy. We complete the final compliance assessment together with your legal team.

Requirements depend on model size and the number of users. During discovery we review your infrastructure and, if needed, design and install GPU servers and the network architecture with you.

Public services are powerful, but they cannot see your company data or connect to your processes, and your data goes to the provider. Enterprise AI works on your own documents and systems, answers according to user permissions and keeps data in-house.

We first make the data foundation solid, then place AI at the centre of your business processes. Reliable models cannot be built without clean, governed data. This order makes it easier to move pilots into production.

It depends on the use case. During discovery we assess your existing data and state clearly which use cases are feasible today and what data still needs to be collected.

Both are possible. Together we choose a cloud, on-premise or Edge AI architecture, based on latency, data privacy and connectivity requirements.

Let's digitalise your operations together.

In a 30-minute call, we'll listen to your needs and show you the right OREL products live.

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