AI Chat Application
A transformer-based assistant that turns a support backlog into a real-time conversation, with inference fast enough to feel like a person is typing.
- Role
- Product & Engineering Lead
- Stack
- PythonAI/MLNLP
Problem
Support conversations were queued, not answered. Every question — including the eighty percent that were variations on the same dozen intents — waited behind a human. The cost curve rose with volume while the experience got worse, and no amount of hiring changed the shape of that curve.
Solution
We built a transformer-based conversational layer that resolves the repetitive majority on its own and hands the genuinely novel cases to a human with full context attached. The design decision that mattered was scope: rather than chasing a system that answers everything, we defined a confident subset, measured it honestly, and made escalation a first-class path instead of a failure state. Real-time NLP inference kept latency inside the window where a conversation still feels like a conversation.
Outcome
Response time collapsed from queue-length to sub-second for the covered intents. Human effort shifted from triage to the exceptions that actually needed judgment, and the volume-to-headcount relationship stopped being linear.
Metrics
Bracketed figures are placeholders pending final reporting.
- reduction in response time
- [XX]%reduction in response time
- of conversations resolved without escalation
- [XX]%of conversations resolved without escalation
- increase in support throughput
- [XX]%increase in support throughput
Approach
The decisions that shaped it.
- Framed the intent taxonomy from real transcripts before writing any model code.
- Set a confidence threshold that favored graceful escalation over confident errors.
- Optimized the inference path for perceived latency, not just raw throughput.
- Instrumented every escalation as a labeled training signal for the next iteration.
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