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گرنگ!
بەخێربێیت، جگە لەم چەند بەشەی خوارەوە سەرجەم بەشەكانی یانەمان بە مەبەستی چاككاری شاردۆتەوە! لەماوەیەكی نزیكدا یانە بەتەواوی بابەتەكانی خۆی لە 2008ی ساڵی دامەزراندنیەوە تاكو ئێستا دەكەوێتە كار. [2025\09\06]
ههڵبژاردنهكانی گهڕان
Hi everyone.
I work with AI Development Services, a software engineering team focused on custom AI systems. We build LLM applications and integrate them into existing products, especially enterprise software.
The difficult part is usually not the first model demo. It is connecting the AI layer to production data, permissions, business rules, monitoring and human review. We outline this engineering approach at AI Development Services.
For teams already running AI in production, which integration or reliability issue required the most work?
The choice between hosted and open-source models is rarely ideological.
Hosted models can reduce time to market and operational burden. Open-source models can offer more control over deployment, data handling, customization and unit economics at scale. The right decision depends on workload, risk, latency, volume and the engineering capacity available to operate the system.
How are teams here making this decision for production applications?
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