A production localization pipeline built entirely on the OpenAI API: one English source of truth, SEO-aware translation prompts, cross-model verification with
2 projects · 5 articles · 2 reviews
OpenAI's API provides access to GPT models for text generation, embeddings, and function calling. I use it to build AI-powered features like smart search, content generation, and chatbots.
A production localization pipeline built entirely on the OpenAI API: one English source of truth, SEO-aware translation prompts, cross-model verification with
RAG system on OpenAI and Upstash Vector with 30 language support. Includes streaming chatbot, hybrid search (semantic + keyword), AI ticket classifier, and
Databases
Semantic search for internal docs with Postgres and pgvector: chunk, embed, and query by meaning so people find the right document, not the exact words.
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OpenAI API cost optimization: where the money actually goes in production, prompt caching, model routing, budget controls, and observability that surfaces
AI document processing in production: full PDF pipeline — OCR fallbacks, structured output, validation, cost at scale. Beyond naive GPT calls.
Add AI to an existing product without a rebuild. Three integration patterns, how to pick the right one, and what production-ready AI actually demands.
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Libraries
Databases
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RAG chatbot for e-commerce: resolve 70% of support queries across 25 languages — ingestion pipeline, hybrid search, confidence thresholds, and streaming UI.
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Libraries
Databases
We wanted to add an AI feature that turns messy user notes into structured records, but our first attempt returned unpredictable JSON that broke the app half the time.
Bram de Vries 🇳🇱
Product Lead
Service: ai integration
We process thousands of supplier invoices a month and the team was keying them in by hand.
Lena Brandt 🇩🇪
Head of Product
Service: ai integration
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