LLM Feature or AI Agent MVP, Shipped in 2 Weeksyoungwoo lee
You get a working AI feature in your product in two weeks — not a notebook demo. Built by someone who has shipped LLM pipelines to production and lived with what breaks.
What's included
A scoping session to pick the one workflow worth automating first. Implementation with Claude or OpenAI behind a single provider adapter, so you can switch models by config, not rewrite. Retrieval over your own data (RAG with pgvector on PostgreSQL) when the answer needs to be grounded in your documents, reviews or tickets. Classification and routing with explicit "unknown" outputs, so the model says nothing rather than guessing wrong. A human-review gate for anything risky, off by default, unlocked per customer or per case. Evaluation set and logging so you can see what the model actually did. Deployed to your stack (FastAPI/Flask, Docker) with a handover doc.
Good fit for
Customer-reply drafting, review or ticket triage, internal Q&A over company data, document extraction, and agent workflows that need guardrails before they touch a customer.
Why me
I built Revix, an AI review-operations system for Naver SmartStore sellers: tone profiles learned from the store's own writing, purchase-stage classification, a five-condition publish gate, and RAG chat over 19,731 embedded reviews. I also caught and fixed a 7.6x inflated statistic on my own landing page and pinned the true numbers with a test — I care that the numbers you show customers are real. Case studies are on my profile.
Pricing
From $2,500 for one end-to-end AI feature delivered in about two weeks. Larger scopes, multi-agent systems and ongoing model tuning are quoted after the scoping session.
Starting at$2,500
Duration2 weeks
Tags
PostgreSQL
Python
AI Automation
AI Developer
Service provided by
youngwoo lee proSeoul, South Korea
LLM Feature or AI Agent MVP, Shipped in 2 Weeksyoungwoo lee
Starting at$2,500
Duration2 weeks
Tags
PostgreSQL
Python
AI Automation
AI Developer
You get a working AI feature in your product in two weeks — not a notebook demo. Built by someone who has shipped LLM pipelines to production and lived with what breaks.
What's included
A scoping session to pick the one workflow worth automating first. Implementation with Claude or OpenAI behind a single provider adapter, so you can switch models by config, not rewrite. Retrieval over your own data (RAG with pgvector on PostgreSQL) when the answer needs to be grounded in your documents, reviews or tickets. Classification and routing with explicit "unknown" outputs, so the model says nothing rather than guessing wrong. A human-review gate for anything risky, off by default, unlocked per customer or per case. Evaluation set and logging so you can see what the model actually did. Deployed to your stack (FastAPI/Flask, Docker) with a handover doc.
Good fit for
Customer-reply drafting, review or ticket triage, internal Q&A over company data, document extraction, and agent workflows that need guardrails before they touch a customer.
Why me
I built Revix, an AI review-operations system for Naver SmartStore sellers: tone profiles learned from the store's own writing, purchase-stage classification, a five-condition publish gate, and RAG chat over 19,731 embedded reviews. I also caught and fixed a 7.6x inflated statistic on my own landing page and pinned the true numbers with a test — I care that the numbers you show customers are real. Case studies are on my profile.
Pricing
From $2,500 for one end-to-end AI feature delivered in about two weeks. Larger scopes, multi-agent systems and ongoing model tuning are quoted after the scoping session.
$2,500