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Ransomware-resilient architecture on AWS: How we secured a SaaS company’s data

As a cybersecurity services provider, we helped a SaaS company eliminate ransomware risks by designing a ransomware-resilient AWS architecture with immutable backups, cross-account replication, and automated disaster recovery.

Erbis· any

AI-Powered BI Transformation with Amazon Redshift and QuickSight Q

We successfully implemented a scalable, AI-driven business intelligence (BI) platform for SaaS by integrating Amazon Redshift with Amazon QuickSight Q. Erbis enabled advanced analytics, multi-tenancy, and generative AI capabilities while embedding seamless BI experiences into the client’s application.

Erbis· any

AI-driven identity management automation

We implemented an advanced AI-powered identity management system for an enterprise client. The project aimed to automate account registration and password management processes using OpenAI’s ChatGPT API, while introducing a caching mechanism to reduce costs and boost efficiency.

Erbis· any

Securing Telecom Platforms Across Legacy and 5G Networks

Telecom operators face growing cybersecurity pressure as 5G, IoT, AI-driven automation, and cloud-native infrastructure expand the attack surface across critical communications environments. This analysis examines how vulnerabilities emerge across OSS/BSS platforms, network orchestration systems, SIP and IMS infrastructure, cloud-native network functions, and customer-facing applications. It outlines practical approaches for maintaining resilience through zero-trust access models, secure CI/CD pipelines, API hardening, observability, and telecom-specific anomaly detection. For technology leaders, the challenge is no longer securing isolated components, but maintaining operational continuity and trust across highly interconnected telecom ecosystems.

AprioritArticle/Blog post

AI Compliance Automation for Scalable iGaming Operations

As regulatory complexity and multilingual player interactions increase, iGaming operators are under growing pressure to maintain service quality, auditability, and operational efficiency at scale. DigerCompanion positions AI as an operational compliance layer that automates routine support workflows while aligning responses with jurisdiction-specific policies and responsible-play requirements. The platform combines multilingual AI assistance, escalation logic, analytics, and compliance monitoring to reduce manual workload and improve consistency across player-facing operations. For operators expanding into regulated markets, the broader implication is that scalable AI support increasingly depends on governance, traceability, and operational integration rather than automation alone.

DigicodeArticle/Blog post

Operationalizing Enterprise AI Beyond Prototypes

Many enterprises are discovering that AI progress depends less on advanced models and more on operational readiness. Adam Górniak of Deepsense.ai examines why successful AI adoption increasingly depends on strengthening knowledge layers, improving existing workflows, and deploying narrowly scoped agentic systems before scaling broader initiatives. The article highlights the growing divide between organizations building production-grade AI systems step by step and those still constrained by fragmented data, legacy infrastructure, and unclear operational ownership. For technology leaders, the challenge is shifting from experimentation toward secure, validated, and operationally reliable AI workflows that can withstand real enterprise conditions.

deepsense.aiArticle/Blog post