Domain-specific models: when corporate AI needs its own language
Corporate systems require domain-specific language models for security, efficiency, and compliance with unique business processes.
Ideas and experience in communications, technology and customer service.
Corporate systems require domain-specific language models for security, efficiency, and compliance with unique business processes.
The concept of recursive AI self-improvement is transforming process automation. We explore how companies can prepare their data and manage risks.
Platform engineering optimizes Kubernetes in hybrid infrastructures, ensuring cost predictability, enhanced security, and operational efficiency.
AI voicebots in telecom contact centers enhance efficiency but require a balance between automation and customer trust.
CERT-UA warns of intensified cyberattacks targeting critical infrastructure. We examine new attacker tactics and effective defense strategies based on Zero Trust architecture.
Integrating AI into RPA is transforming business processes, but success hinges on data readiness and effective risk management. Prepare your data.
Platform Engineering and IDP are transforming development, optimizing cloud costs, and enhancing security amidst increasing infrastructure complexity.
Domain-specific AI models are becoming a key tool for modernizing enterprise applications in 2026-2027, overcoming legacy system limitations.
Telecom operators must proactively prepare data and enhance cybersecurity to comply with the EU's AI Act, minimizing fraud risks.
Integrating Edge AI with 5G and data security measures is becoming the foundation for protecting critical infrastructure by 2027, requiring systematic data management.
STIR/SHAKEN protects corporate VoIP from fraud, enhances Caller ID trust, and ensures reliable communications in modern telecom networks.
Integrating antifraud systems with billing and softswitch is crucial for telecom operators in 2027 to effectively combat fraud.