AI support knowledge base for a service team
A RAG assistant that helped support agents search policy, onboarding, and troubleshooting documents with grounded answers.
Client
Confidential service operations team
Outcome
Reduced repeated internal support lookups and made source documents easier to audit.
Date
Apr 18, 2026
Problem
The support team had answers spread across onboarding documents, policy notes, and historical troubleshooting threads. Agents could find answers, but the search process was slow and inconsistent.
Process
The first step was narrowing the assistant to internal support workflows. The feature did not try to answer everything; it focused on questions where documents were authoritative and citations mattered.
The build included document ingestion, metadata filters, retrieval inspection, prompt templates, and an evaluation set based on real support questions.
Stack
The product used Next.js for the interface, TypeScript for application logic, Postgres for source metadata, and a retrieval layer tuned around document type and recency.
Outcome
Agents could ask natural questions, inspect cited sources, and escalate when the answer was not confident. The team also gained a repeatable path for improving retrieval quality over time.
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