Our Services
We bring artificial intelligence into your business processes, delivering AI integration solutions that turn data into actionable insights and enable smart automation.
Service strengths
Why choose our AI Integration services:
- Tailored models and accuracy
We use supervised machine learning and deep learning techniques to build models specifically trained on your data, ensuring accurate and relevant results. - Smart scalability
Cloud-native architectures that automatically adapt to workload, guaranteeing consistent performance even during usage peaks. - Security and compliance
We implement data encryption in transit and at rest, role-based access controls, and full auditing to comply with GDPR and industry regulations. - End-to-end support
From data collection and cleaning to model deployment, including continuous performance monitoring and periodic retraining.
Third-party technologies
Yolo
We use YOLO for real-time object detection in applications, powering fast and precise contextual AI features.
CVAT
CVAT enables collaborative image and video labeling, producing accurate datasets for training, validation, and continuous feedback.
Vultr
We deploy AI models on Vultr VPS, ensuring elastic scalability, low latency, security, and full control.
Haystack
By integrating Haystack, we provide semantic search across documents, delivering intelligent and contextually relevant answers.
The challenges we solve
Integrating AI into a company can pose technical and organizational obstacles that slow projects down or undermine ROI. With our structured approach, we tackle:
- Fragmented and low-quality data
- Centralized collection
- Automated normalization and validation to ensure effective training
- Model complexity
- Selecting the optimal architecture (decision trees, neural networks, transformers)
- Hyperparameter tuning to maximize performance
- Production scalability
- Containerization with Kubernetes
- Continuous deployment to keep services always available
- Horizontal scalability options
- Regulatory compliance
- Data governance
- Data anonymization
- Process audits to meet GDPR and internal policies
- Adoption and change management
- Team training
- Intuitive interfaces
- Custom dashboards
Frequently asked questions
We want you to have complete clarity before getting started: here are the answers to the most common questions about AI Integration.
What kind of data is needed to train an AI model?
We can work with structured datasets (CRM, ERP) or unstructured data (texts, images, logs): what matters is having representative and properly labeled samples.
How long does it take to develop an end-to-end AI solution?
From data analysis to production deployment, it typically takes 8–12 weeks, depending on dataset size and model complexity.
Can the AI model be integrated into my existing applications?
From data analysis to production deployment, it typically takes 8–12 weeks, depending on dataset size and model complexity.
How do you measure the effectiveness of an AI model in production?
We monitor metrics such as accuracy, precision, recall, AUC-ROC, and inference times through a dedicated dashboard, with alerts triggered in case of data or model drift.
Do you provide training and support for my team?
Absolutely: we organize hands-on workshops on MLOps, data management best practices, and dashboard usage to ensure your team’s operational autonomy.
