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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
The demo worked. Your AI feature answered twenty handpicked questions well. Then real users showed up with abbreviations, half-finished sentences, scanned PDFs and product names that never appeared in your examples, and the answers fell apart. The model didn’t get worse overnight. Your AI training dataset was too narrow, and nobody had measured how narrow. […]
Building a working AI demo is now the easy part. A coding agent and a hosted model can produce a convincing chatbot, summarizer or document assistant in days. That speed has changed what an AI proof of concept needs to do. When anyone can make the demo, the demo shows very little. Gartner’s 2024 survey […]
The support queue keeps growing. Somebody copies fields from an email into the CRM. The weekly report takes half a day to assemble. You tried a chatbot and a few Zapier rules, and the work still stalls the moment a request needs interpretation or a judgment call. That is where AI workflow automation earns its […]
Summarizing a customer thread, drafting a product description, or answering a policy question – all things an AI demo can do in seconds. Then someone connects it to the real CRM, real permissions, and real approval chain, and the answers become inaccurate. The model has not changed, but the environment has. This gap is what […]
LangChain’s survey of teams puts the number at 57% for those running AI agents in production over the 2024 to 2026 period. Yet MIT has put forward figures from the same timeframe indicating 95% of generative AI pilots have yet to make a dent on the P&L, and Gartner is forecasting that by 2027 more […]
You will not find a marketing automation project that has failed on account of the software being incapable. More often than not, it is the workflow itself that is at fault, running on top of poor data or an ambiguous process the team never truly put to bed. There is usually a question of ownership, […]
For the small business owner, the issue is seldom an “AI problem”. It is a matter of workflow, and AI is there to address it provided the scope is right. The difference is worth noting: some 80% of AI projects come to nothing, and you will not see a bad model as the cause. More […]
You get the contract over email. Some one will download it, give it a new name and put it in front of legal. After you have waited and followed up, you get back a revision to pass on to finance, and the final version ends up in a shared drive that is not exactly trusted […]
You will find that the majority of teams don’t have an automation problem per se. They have a process issue and their automation is only serving to highlight it. An invoice sits there for a manual touch, a demo request comes in to an inbox, or some Zap goes quiet because a column was renamed […]
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