What is an AI Agent, how does it work?

An AI agent is an intelligent software component that can make autonomous decisions towards defined goals, perceive and analyze environment, and automatically execute appropriate actions. The fundamental difference from traditional software: it doesn't wait for commands, it moves goal-oriented. It scales from a single agent to multi-agent systems that work in coordination with each other. It integrates with ERP, CRM, email and databases to run tasks 24/7.

What is MCP (Model Context Protocol)?

MCP is an open protocol that enables AI models to access enterprise data sources — databases, files, APIs and applications — in a secure and standardized way. Developed by Anthropic, MCP allows AI systems to work through a single standard without requiring separate integration for each data source. Corius uses the MCP protocol as the data connection layer in AI agent projects.

What is Unified Data Access (UDA)?

UDA is a data layer architecture that provides uniform interface access to data across different systems — ERP, CRM, file systems, APIs. With UDA, AI models and agents pull data in the same language and protocol, regardless of the data source. In the Acta Legal project, it was used to process both UDF and PDF sources from a single channel.

What is the difference between a machine learning model and a rule-based system?

Rule-based systems operate with human-defined fixed if-then logic; updating them under variable conditions requires time and cost. ML models, on the other hand, learn patterns from historical data to create an adaptive decision tree. To determine the failure risk of a turbine in the energy sector by simultaneously evaluating thousands of sensor parameters is practically impossible with a rule-based approach; an ML model does this with R²=0.9997 accuracy.

How is data security and GDPR compliance ensured in AI projects?

Corius designs all AI projects in compliance with GDPR requirements. Personal data processing processes are documented, data minimization principle is adopted, and access controls are applied in layers. For organizations preferring on-premise deployment, local model solutions are offered where data does not leave the company infrastructure. Masking sensitive data is our standard process in projects using LLMs.

Is integration with my existing ERP, CRM or enterprise software possible?

Yes. The AI solutions we develop have a modular and API-first architecture. We provide seamless integration not only with major enterprise systems like SAP, Salesforce, Microsoft Dynamics, but also with custom-developed platforms. Integration time and complexity are minimized with the MCP protocol and UDA layer.

Are AI solutions suitable for small and medium-sized enterprises?

Yes. The majority of Corius projects were conducted in organizations of 10-200 people. Acta Legal, a 13-person law firm, saved 2,000 hours annually; Mine Colours increased FTR to 93% with 8 production engineers. Scale doesn't determine success — the right use case and data quality do.

How much data is needed to start an AI project?

It depends on the project type. While 12-24 months of historical data is usually sufficient for ML models, labeled dataset needs may arise in NLP solutions. In the Elsan Energy EDMAP project, existing laboratory data was sufficient; Mine Colours was trained with spectrophotometer historical records. In the first step, we analyze your data infrastructure together.

How is the ROI and starting cost of AI integration evaluated?

ROI calculation in Corius projects is based on four components: cost of saved human hours, cost reduction from error and waste decrease, revenue potential from capacity increase, and competitive value of speed advantage. In our reference projects, investment return was realized between 5 to 8 months. Starting cost varies by project scope; it's clarified together in the discovery phase.

AI Solutions | AI Agents, ML Models, Automation

Enterprise AI Solutions

Corius develops autonomous AI agents, predictive analysis models and AI-integrated custom software platforms for enterprise companies. From law firms to energy companies, from plastic manufacturers to electrical contracting firms — proven results with data in every sector.

AI Agent and ML Solutions in Manufacturing Sector

Reduce waste costs and increase FTR rates with ML models that catch quality deviations on production lines before they occur, and AI agents that autonomously manage maintenance processes. In the Mine Colours project, FTR increased from 68% to 93%.

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Predictive Maintenance Model

ML model that predicts failure risk weeks in advance from machine sensor data. Minimizes unplanned downtime.

Quality Control AI Agent

Agent system that monitors production parameters in real-time, automatically detects quality deviation and instantly alerts the relevant operator.

Production Planning Optimization

Model that calculates the most efficient production schedule by jointly evaluating demand forecast, machine capacity and raw material stock.

Waste and Scrap Minimization

Recommendation engine that analyzes process variables to suggest the parameter set providing lowest waste rate and presents it to the operator.

EDMAP and Prediction Models in Energy Sector

Increase operational efficiency with ML models that predict failures from turbine and equipment data, energy consumption forecasts and AI agents that digitize field coordination. Proven results in Elsan and Enart projects.

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EDMAP — Material Selection Model

ML-based decision support system that analyzes turbine blade data and environmental conditions to determine the most durable material combination.

Energy Consumption Forecast

Model that makes high-accuracy consumption forecast with historical consumption, weather and production data. Optimizes energy procurement strategy.

Field Coordination Agent

Autonomous agent system that automatically creates work orders, directs field teams and tracks intervention process end-to-end.

Predictive Maintenance System

Early warning model that continuously monitors data from equipment sensors, detects anomalies and proactively alerts the maintenance team.

Personalization and Demand Forecast in E-Commerce

Increase conversion rate and customer lifetime value with recommendation engines that analyze customer behavior, stock management models that see demand in advance, and AI agents that autonomously execute order processes.

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Personalization Engine

ML model that processes user navigation, purchase and favorite data to offer personalized product and content recommendations to each customer.

Demand Forecast Model

Multi-variable demand forecast covering seasonality, campaign effects and market trends. Minimizes overstock and stockout losses.

Customer Service Agent

Intelligent support agent that autonomously responds 24/7 to order tracking, return requests and product questions, forwarding complex situations to humans.

Dynamic Pricing

System that calculates and updates optimal price point by analyzing competitor prices, stock level and demand intensity in real-time.

Route Optimization and Supply Chain Visibility in Logistics

Reduce delivery cost and time with ML models that do dynamic route optimization, AI agents that autonomously manage warehouse operations and analytics platforms that provide full visibility across the supply chain.

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Dynamic Route Optimization

ML-based route planning engine that jointly optimizes vehicle capacity, traffic, time windows and fuel cost.

Demand Forecast and Stock Management

Predictive analytics system that proactively optimizes warehouse stock levels by modeling historical order data and external factors.

Warehouse Operation Agent

Smart agent that autonomously coordinates order picking, shelf assignment and shipment preparation processes, supporting human operators.

Supply Chain Visibility

Analytics platform that tracks the entire chain from supplier to end customer, detects delay risks in advance and suggests alternatives.

Process Optimization and Security Compliance in Chemical Sector

Increase quality and efficiency with ML models that analyze formulation databases, systems that optimize process parameters in real-time and AI agents that autonomously track security compliance processes.

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Formulation Optimization Model

ML system that analyzes raw material properties, process conditions and target product parameters to suggest the most efficient formulation combination.

Process Monitoring and Control

System that continuously monitors critical process variables like reactor temperature, pressure and flow rate, detects deviations and suggests automatic corrections.

Security Compliance Agent

Compliance agent that tracks regulatory requirements and internal security protocols, detects non-compliance and automatically creates reports.

Quality Prediction Model

Model that predicts final product quality from process parameters before production is complete. Minimizes scrap and reprocessing costs.

Enterprise AI Solutions

AI Agent Solutions

Predictive and Analysis Models

Proactive Decision Making

Operational Efficiency

Continuously Active Systems

High Return on Investment

Data Security

Low-Risk Start

Discovery and Data Assessment

Strategy and Roadmap

Prototype and Proof of Concept

Development and Testing

Go-Live and Optimization

AI Agent and ML Solutions in Manufacturing Sector

Predictive Maintenance Model

Quality Control AI Agent

Production Planning Optimization

Waste and Scrap Minimization

Large Language Models (LLM)

ML Framework

Agent Framework

Data & Integration

Deployment & Infrastructure

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