Cognitive Command

Problem

Professionals receiving 100+ emails daily spend 30–45 minutes just triaging — deciding what needs action, what can wait, and what should be forwarded. This is pattern-matching work: sender, subject line, thread context, and urgency signals combine into a repeatable decision that doesn't require human judgment. Yet most people do it manually, every day, as the first task of their morning. That time compounds. Over a five-day week it adds up to three or more hours lost before any real work begins.

Approach

Built a LangChain agent that connects to the inbox via API, reads each email in full, and classifies it into one of four categories — Urgent, Follow Up, Delegate, or Archive — along with a confidence score. NLP tokenization handles noisy subject lines, forwarded threads, and quoted reply chains that would otherwise confuse a naive classifier. The FastAPI backend processes emails in batches rather than one at a time, keeping throughput high even during morning inbox spikes. Classification logic is prompt-engineered with few-shot examples covering the edge cases that matter most: newsletters that contain action items, automated alerts that are genuinely urgent, and reply chains where the relevant message is buried three levels deep.

Stack

• LangChain — agent framework that orchestrates email reading, context assembly, and classification workflows end to end.
• FastAPI — async backend that handles batch email processing with low latency and clean REST endpoints.
• NLP — tokenization and preprocessing pipeline that normalizes noisy email content before it reaches the classifier.
• Automation — inbox API integration and batch scheduling that trigger processing without any manual intervention.
• AI Agents — few-shot classification chain that returns a category label and a calibrated confidence score for every email.

Results

Screenshots