خدمات تكامل الذكاء الاصطناعي: كيفية إضافة AI إلى منتجك الحالي

It depends on the product, so a range quoted without seeing it would be a guess. Build effort grows with the level: a basic LLM feature on a provider API is the smallest job, retrieval and agents add integration and evaluation work, and custom models add data preparation, training and ongoing maintenance. Monthly API spend is billed separately by the provider, scales with usage, and should be capped and monitored from day one.

Simple AI features like chatbots or content generation can be added in 2-4 weeks. More complex integrations with custom prompts and workflows take 2-3 months. Full ML implementations typically require 4-6+ months.

For 90% of use cases, existing LLM APIs (OpenAI, Claude) are the right choice - faster, cheaper, and more reliable than building custom. Build your own only when you have proprietary data that creates competitive advantage and 6+ months to invest.

Start with a single, high-value use case: customer support chatbot, content generation, or data analysis. Use existing APIs with good prompt engineering. Validate ROI before expanding to more complex implementations.

Not for LLM-based features. A skilled full-stack developer can implement chatbots, text generation, and summarization using APIs. Data scientists become necessary when building custom ML models or working with complex data pipelines.