Faced a challenge automating post-call summarization in our CRM. Tried traditional NLP—too generic. Ended up building a domain-specific agent that learned from our support logs. The breakthrough? Intent detection + memory optimization. If you're diving into similar territory, check out this ai agent development company we referenced for architecture patterns—saved us weeks. Real AI isn't magic, it's iteration.
Absolutely agree — real AI success comes from thoughtful iteration, not just off-the-shelf magic. We ran into a similar wall trying to use generic NLP models for support call summarization. They just didn’t capture the nuance or context of our domain.
What worked for us was combining intent detection with a lightweight memory layer trained on our historical support logs. This let our agent track conversation flow and extract key points accurately. Game changer.
Also, huge shoutout to the AI agent development company we consulted for architecture guidance — their frameworks and examples saved us from a lot of trial-and-error. If anyone here is considering building domain-specific solutions, especially in support or customer service, I’d strongly recommend exploring AI chatbot development services that specialize in custom logic and contextual understanding.
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