Recommendation Engine & Personalization Platform​

A real-time personalization platform for, one of Poland’s top travel agents. Platform seamlessly integrates with just a single tag and offers a wide range of capabilities. From email and push notifications to user segmentation, A/B testing, and complex personalizations based on user behavior and CRM data.

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Go‑Live Date
System development
2017 - 2020

FIRECRUX has delivered 38,8% increase of CR (conversion rate) for

The shift in traffic from desktop to mobile channel, observed by the Client, was not synonymous with the increase in sales on mobile devices. This was largely influenced by users habits to search on the phone, but to buy on the desktop.

In order to influence users despite its habits, a series of personalizations and UX optimization were prepared. Best combination chosen by Firecrux AI Response Engine (AIRE) provided a 38.8% increase in sales on mobile devices compared to the previous time period.

Guiding principles

Push, Email, SMS in 1 place

Travelplanet needed a comprehensive customer communication management system for reservations and promotions. The system was tasked with proficiently targeting and segmenting users based on their interests and activities.

Omni‑Channel Personalization

The previous system caused almost 2 hours of daily downtime due to false positives. Reduction in of FP was crucial for enhancing production profitability. It was imperative to bring false positives down from 20% to a maximum of 5%.

Scalable architecture

Travelplanet was committed to building a scalable and sustainable architecture for the long run. The backend system should support millions of users.

Qualitative Analytics

Effective energy management aimed not only at achieving cost savings but also at extending the operational lifespan of X-ray lamps, which is crucial due to their high replacement costs.

Results and customer benefits

The project began with the analysis of a vast image database, a process that took nearly 6 months to appropriately classify. The acquired training material served as the foundation for developing multiple classification models using Machine Learning, particularly Deep Learning techniques.

Two Overlapping Neural Networks

We simultaneously employed two neural networks because deep learning has its limitations. FIRECRUX models for AI was trained using two datasets: one comprising images of pristine, uncontaminated products, and the other containing images of products mixed with contaminants. This dual dataset strategy allows us to address three key scenarios:

  1. Verification of a clean production line – in this case, the classification model trained on clean product images exhibits the highest score.
  2. Detection of contamination within the scanned area – the scoring is reversed to identify the presence of contaminants.
  3. Ensuring manufacturer safety in unusual situations involving impurities outside the tested spectrum or intentional sabotage – in such instances, the models produce similar scores, signaling a suspicious situation. For safety reasons, these situations are flagged and rejected for investment by our staff.

Hardware Requirements

System that has been developed is based on the FIRECRUX engine and can be operated on GPU, CPU, or VPU processors. In the current version at Milarex, it runs on a GPU processor using the nVidia Jetson TX2 Developer Kit (945-82771-0005-000). The CPU version is embedded on Intel i7 and i9 processors, while the VPU version utilizes Intel Movidius.

Furthermore, the software has been seamlessly integrated with X-ray lamps, including the SAXG monobloc x-ray generators from Spellman and a line scan detector from Detection Technology.

FP Reduction and Effectiveness Testing

The previous solution utilized by Milarex, developed by a Canadian company, led to significant downtime due to the occurrence of false positives (FP), averaging nearly 2 hours per day. During the initial phase of our solution’s development, around 2017, we placed considerable emphasis on addressing this FP issue. Through rigorous testing, both on quality control testers and in real-world contamination scenarios (such as detecting broken knives from portioning machines, identifiable medical plasters, or glass fragments), we successfully reduced the FP rate from approximately 20% to less than 1%.

Subsequently, we resolved this problem and integrated the option to mark FP (False Positives), NP (Non-Presence), and TP (True Positives) within the X-ray scanner management system.

We rely on machine-generated data, specifically the number of scans, detections, and saved confirmation photos, to monitor the system’s performance.

Real-time Contamination Detection

The completed system also enables on-the-fly scanning. Our software has been designed to accommodate two types of machines: cyclic and continuous scanning.

The cyclic scanning method proved sufficient for machines handling 9/12/16 pieces of salmon, as the production line’s manual worker efficiency was the bottleneck in these scenarios.

In 2021, a continuous scanning solution was developed. Instead of having 9/12/16 trays go into the bin upon contamination detection in the cyclic machine, the fish are now seamlessly redirected to the real-time scanning machine without the need for a ‘stop-start’ action.

Production Deployment and Licensing

The solution for a first machine was implemented in 2019 and an additional 13 machines were gradually added between 2019 and 2022. Subsequently, development efforts continued until November 2022 and a local data cloud was established.

The final version of the system achieved two significant milestones:

  • A 61% increase in contamination detection efficiency,
  • A 66% increase in production line speed.

In Nov 2022 licensing and maintenance agreements were signed and currently Greenlogic is solely dedicated to optimizing classification models to maintain a high level of contamination detection accuracy.

Benefits Beyond the Project Scope

New Production Detection Capabilities

Visual detection based on machine learning algorithms can play many roles in the production process. It will both find the right objects (desirable or not), but is also able to count, measure, weigh or compare them with a given pattern.


The classification model is trained based on the perfect product pattern, so even if the AI does not find a known contamination on the product, it will be able to react due to the lack of confirmation of a "clean scan".

Object Type

Recognition of the type of objects in order to complete the set (e.g. in the furniture industry).


An example of detection with the determination of the size of the object as a parameter of the threat.

Technical stack

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