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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
You won’t find the hard part of an ERP AI bot in the model itself. It is in the periphery: the data plumbing, identity and audit concerns, change management, and the thorny issue of who to blame when the bot makes a mistake. The numbers bear this out. Stanford’s 2026 AI Index tells us 88% […]
At your next product meeting you will hear it: “We can put an agent around it, or fine-tune later and let RAG handle the rest.” You will find yourself nodding along with the room, even as you ask yourself if you have just committed to a chatbot, a search engine or some kind of autonomous […]
You could say the average loyalty program is little more than a coupon dispenser with a points ledger. A customer will sign up, put your emails in the junk folder and only ever redeem what was already on their shopping list. The program chugs along but it does not learn. If one of your regulars […]
“Remind me to put in a call to the bank and get a dentist appointment for tomorrow,” a user might type. Your app is left with one muddled task rather than two. No crash, no refusal from the model, it simply failed to parse the sentence as you would have. You could call this the […]
You will not find much generative AI work that has made it past the pilot stage. According to Stanford’s 2026 AI Index, 88 per cent of organizations are running some form of AI, but Deloitte’s enterprise survey for the same year tells a different story: only one in three leaders would claim they have actually […]
MIT’s NANDA initiative found that roughly 95% of enterprise generative AI pilots deliver little to no measurable P&L impact. Most do not fail because the model is weak. They fail because the answer is not tied to the right SOP, the tool sits outside daily work, or no one defined success before the demo. That […]
AI in ERP is projected to reach USD 46.5 billion by 2033, but the useful question is smaller: what can an AI ERP bot safely do inside a real finance, procurement, inventory, or operations workflow? For CIOs, ERP leaders, operations teams, and product teams building AI-assisted workflows, the answer is not “let the bot run […]
Most teams do not need to build a new AI model from scratch. They need to build a reliable AI system around the right model, the right data, and the right workflow. That distinction matters because the cost, risk, and timeline change completely depending on what “building an AI model” means in your case. This […]
The AI in 5G networks market is estimated at $3.66 billion in 2025 and $14.88 billion by 2030, according to The Business Research Company and ResearchAndMarkets. That growth is real, but it hides a harder truth for product teams: AI 5G is not a magic layer that makes every connected product instant, autonomous, or easier […]
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