AI-Native Email Intelligence
Support replies with context, guardrails, and evaluation.
An email copilot that understands support requests, retrieves company knowledge, generates responses, and evaluates reply quality through an AI operations dashboard.
APPLICATION SCREENSHOT · ILLUSTRATIVE OUTPUT
The problem
A useful support reply needs more than fluent text: it needs customer context, relevant policy, appropriate priority, and an understandable quality check.
How it comes together
A LangGraph workflow classifies intent, priority, sentiment, and customer type before knowledge retrieval and response generation. Validation and evaluation stages then return a structured report to the dashboard.
Follow the flow.
Multi-stage orchestration with visible pipeline execution.
Semantic policy and FAQ retrieval backed by ChromaDB.
Claude generation with a Gemini fallback path.
Reply evaluation, analytics, and per-stage timing.
Why this approach?
Separate lightweight production dependencies from the heavier research evaluation profile, keeping the hosted backend practical while supporting deeper local analysis.
The hosted application opens at its sign-in page. Full authenticated generation was not exercised in this review. BERTScore and full embedding evaluation require the optional local evaluation profile; the lightweight runtime uses judge-based evaluation and fallbacks.