Turning tax consultations
into a searchable asset
A RAG system that auto-collects KakaoTalk consultations at tax & accounting firms and lets AI search and summarize them. We built the full pipeline so the consultations piling up each day stop disappearing and start grounding the next one.
RAG · Serverless · Chrome ext.
Real screens.
Captured in September 2026. Select an image to enlarge it.
A tax firm's consultation data accumulates daily — and disappears just as quickly.
Threads completed inside the KakaoTalk Business Center were effectively unsearchable. A similar question arriving next month forced the staff to start from scratch every time, with no shared record of who answered what.
General-purpose tools like ChatGPT couldn't hold tax-specific context. The real need was to bring back 'the answer this firm already gave' — fast.
We pulled consultations into the server in one click, ran them through a live embedding pipeline, and layered a response engine that combines multiple search strategies.
A Chrome extension collects threads from the Business Center screen with one click and ships them to our server. Each record flows through SQS → Lambda → Cohere embeddings → pgvector and becomes searchable the moment it lands.
Retrieval doesn't lean on a single query. Multi-query rewrites the question, MMR diversifies results, and hybrid keyword-plus-vector search surfaces the answers. So 'similar cases' arrives as the firm's cumulative knowledge, not a single hit.
Stack.
Chrome Extension
Pulls Business Center consultations into the server with one click — no manual filing.
AWS Lambda · SQS · SAM
Ingestion, embedding and retrieval all serverless. Cost tracks traffic.
pgvector · Aurora
Vector search on top of Postgres. The team can use familiar SQL alongside it.
Cohere · GPT
Cohere for embeddings, GPT for response generation. Multi-query + MMR + hybrid search combined.