Computer Vision & Machine Learning

Use AI to automate image recognition, analyze data, and streamline processes across your business.

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What are Computer Vision and Machine Learning?

Computer Vision enables image and video analysis in ways that previously required human attention. It can recognize objects, detect irregularities, and support processes based on visual observation.

Machine Learning makes it possible to identify patterns in data, predict events, and automate selected decisions. It is a tool that helps organizations make better use of the information they already have.

Well-implemented AI solutions are not just an add-on to the business. They become part of the process itself – increasing predictability, reducing manual effort, and making operations easier to scale.

In practice, this means:

fewer manual operations,
faster detection of errors and deviations,
greater control over the process,
better use of data in day-to-day work.

YOUR BENEFITS

Why implement Computer Vision or Machine Learning?

Greater operational efficiency

Automating selected tasks helps reduce the workload on your team and shortens the time required to complete repetitive activities.

Faster detection of errors and irregularities

The system can identify deviations, operational errors, or anomalies at an earlier stage – before they begin to affect quality, costs, or user experience.

Better use of data

The data you collect starts to actively support business decisions instead of remaining an underused resource.

Competitive advantage

Companies that implement valuable AI solutions faster are better positioned to develop products, optimize processes, and respond to market needs.

Specialization

Where can you use it?

Analytics and decision support

Detecting patterns and anomalies in operational data to help teams respond faster and plan more effectively.

Retail and e-commerce

Data analysis, personalized experiences, recommendations, and automation of selected parts of the sales process.

Logistics and warehousing

Recognition of products, labels, damage, and inconsistencies, support for warehouse operations, and reduction of operational errors.

Review

mobitouch delivers on time and is proactive, creative, and critical thinking.

Katarzyna Bocheńska
CEO of Oryva
They are reliable, communicative, and capable of delivering functional products within agreed timelines. If your project requires a practical, no-nonsense approach and close collaboration with a technical team, mobitouch is a solid choice.

Krystian Dereziński
CEO of MindEasy

Software creation process

What does the implementation process look like?

Discovery and AI readiness audit

Proof of Concept

Design and integration

Evaluation, tuning, and optimization

Publication

Discovery and AI readiness audit
We begin by understanding your product, processes, and data. We identify where AI can deliver real value – and where it would be nothing more than a gimmick. We assess technical readiness, data quality, and business potential.
Proof of Concept
We build a working prototype of the key AI functionality to validate assumptions using real data. A PoC makes it possible to assess the quality of the model’s outputs, its fit with user needs, and its technical feasibility – before you invest in a full implementation.
Design and integration
Based on the validated PoC, we design the target architecture: model selection (GPT, Claude, open-source), RAG strategy, data pipelines, prompt layer, and guardrails. We integrate AI into your product as a native feature – not as an add-on widget.
Evaluation, tuning, and optimization
AI models require continuous evaluation and refinement. We measure response quality, monitor costs, and optimize prompts and pipelines. When needed, we fine-tune models or migrate to more efficient solutions.
Publication
After a successful implementation, we help scale the solution through new use cases, additional models, AI agents, and integrations with other systems. We build AI as a lasting part of the product architecture – not as a one-off project.

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How do we work?

Our approach to the project

We work in short, iterative cycles – each one ends with a working increment that can be tested and evaluated. This allows us to validate assumptions quickly, respond to feedback, and reduce risk throughout the process.

AI projects come with their own specific challenges – outcomes are not always deterministic, and quality depends heavily on the data. That is why our process places strong emphasis on evaluation and measurability at every stage.

We work transparently: demos every 1–2 weeks, shared backlog grooming, and open access to model quality metrics.

Featured projects

Check out the projects we've kicked off

Oryva

Oryva to platforma oparta na sztucznej inteligencji, która pomaga małym i średnim firmom szybko i łatwo stworzyć profesjonalną stronę internetową.

Wystarczy opisać swoją działalność, aby wygenerować stronę dopasowaną do marki, z responsywnym designem i optymalizacją SEO. To prosty sposób na zbudowanie obecności w internecie bez konieczności programowania czy posiadania specjalistycznej wiedzy technicznej.

See case study

MindEasy is a neurotechnology startup developing solutions that connect brain activity with computers through brain-computer interface (BCI) technology. Its mission is to support users in both educational and therapeutic settings. The company works with universities, cultural institutions, and medical facilities to test its technologies in practical, real-world environments. MindEasy’s products can respond to what is happening in the user’s brain, using EEG signals and artificial intelligence to adapt content in real time.

See case study

Mindeasy neurotechnology edtech startup case study by mobitouch - example screen

Cross services

We believe that good design and development are the ones that work best on all devices and every resolution.

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Frequently asked questions

Find the answers

What is the difference between computer vision and machine learning?

Computer vision analyzes images and video, for example to recognize objects or detect irregularities. Machine learning finds patterns in data to predict events, classify cases or support decisions. Many solutions combine both.

Where can these technologies be used?

Typical areas are analytics and decision support, retail and e-commerce, and logistics and warehousing. Common examples include demand forecasting, anomaly detection, quality control and automatic classification.

How much data do we need to start?

It depends on the task. Some solutions can use ready-made models, while custom prediction models need historical data of sufficient quality. We check what data you have at the very beginning, before choosing the approach.

Can the solution work inside our existing systems?

Yes. We build models into your existing processes and systems, for example with ML.NET for applications built on .NET, so the results appear where your team already works.

How do you measure if the model works well?

We agree on success criteria at the start, such as accuracy or the share of cases handled automatically. Then we test the model on data it has not seen before and keep tuning it after launch.