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Asghar Mirzaei is a backend developer at Refact, focused on the APIs, integrations, and infrastructure that power the studio’s products. His work spans data pipelines, third-party services, backend architecture, and deployment systems, helping ensure that products are stable, scalable, and ready for real-world use. Asghar works closely with the team to connect product requirements with reliable technical foundations, especially in systems where performance, automation, and integration quality matter. At Refact, he contributes to the engineering work behind the interfaces, making sure the products the studio builds can run smoothly and dependably
Fortune Business Insights estimated the global AI SaaS market at $22.21 billion in 2025 and projected it to grow from $30.33 billion in 2026 to $367.6 billion by 2034. That growth does not mean every AI subscription is worth buying. It means SaaS teams now need better judgment about where AI belongs, how to test […]
Generative AI business adoption is real, but the results are uneven. Many teams have tried ChatGPT, copilots, content tools, or internal assistants. Far fewer have turned those experiments into production systems that save money, improve decisions, or change how work gets done. This article is for leaders, operators, consultants, and product teams deciding where generative […]
Inventory distortion costs global retail an estimated $1.73 trillion each year, according to Cognira’s 2026 retail forecasting research. That number is the reason machine learning retail deserves attention from ecommerce leaders, merchandising teams, supply chain operators, and retail executives. The promise is not smarter dashboards. The promise is better decisions about what to stock, how […]
Most generative AI for manufacturing pilots do not fail because the model is too weak. They fail because the answer is not tied to the right SOP, the tool sits outside daily work, or nobody defined what success should look like before the demo. For plant leaders, engineering teams, IT/OT groups, and operations executives, the […]
AI TRiSM matters most after an AI system starts touching real customers, sensitive data, internal workflows, or business decisions. The demo may work. The model may answer well in testing. The risk begins when people use it in ways your team did not predict. AI TRiSM stands for AI Trust, Risk, and Security Management. Gartner […]
A maintenance assistant that cites the right SOP is more useful than a chatbot that claims it can run the plant. That is the practical line to draw around generative AI use cases in manufacturing. The near-term value is not autonomous factory control. It is faster troubleshooting, better access to engineering knowledge, clearer work instructions, […]
Stanford’s 2026 AI Index reports that 88% of organizations now use AI in some form. That does not mean every workflow needs a model that can read images, listen to audio, watch video, and reason over documents at once. The best multimodal AI examples have a narrower pattern: the extra input adds evidence a text-only […]
SHRM reports that HR automations have risen 599% over the past two years. That sounds like a technology story, but it is really an operations story. If you are asking what is HR automation, the useful answer is not “AI for HR.” It is this: software runs repeatable HR work so people can spend less […]
Most bank leaders start with the wrong question. They ask, “Which platform should we use?” A better question is, “What job should this bot do, and what could go wrong if it does it badly?” Chatbots for banks are not just support widgets. They sit close to trust, money, identity, and regulation. A vague answer […]
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