Designing a Cost-Efficient Agentic System

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Extracting deals from emails sounds straightforward until you attempt it at scale. It quickly becomes a deceptively hard NLP problem: unstructured text, image-only creatives, ambiguous marketing language, and no standardized format.

Solving it required multiple architectural shifts. What started as prompt-heavy extraction ultimately evolved into a cost-efficient agent-based system.

I designed this production-grade pipeline for Offerivo to reliably identify deals, coupons, and expiration dates while keeping inference costs under control.

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