AI-Powered Customer Support Bot
80% query automation, 94% satisfaction
Intelligent chatbot handling 10,000+ monthly queries for a SaaS company, with natural language understanding and seamless human handoff.
- Client
- TechFlow Inc
- Sector
- Ai/ml solution
- Engagement
- Build and handover
- Duration
- 12 weeks, pilot included
- Team
- 4 — lead, two engineers, conversation designer
- Stack
- Python, OpenAI, Node.js, MongoDB, WebSocket

IMPACT
- 80%
- of queries automated
- 94%
- customer satisfaction
- 10,000+
- queries a month
THE WORK
01The challenge:
Ten thousand questions a month, most of them asked before
TechFlow Inc sells scheduling software to HVAC and plumbing contractors, and its support inbox took more than 10,000 queries a month. Most were repeats: password resets, invoice copies, how to add a technician, why a job did not sync. Eight agents answered them by hand, in order of arrival, from one shared inbox.
Contractors wrote in at 6 a.m., before the trucks roll out, and the support team started at nine. First replies averaged five hours. The hard tickets, a broken integration or a billing error, waited behind the easy ones, and TechFlow was hiring a new agent every quarter just to keep pace.
02What we built:
A bot that answers from the account, and hands off with context
Every message from the in-app widget or email reaches a Python service that uses OpenAI models to classify the intent and draft a reply from TechFlow's help center and the customer's account record in MongoDB. Password resets, invoice copies and new technician seats are completed end to end, and replies stream back over WebSocket through a Node.js layer.
The bot passes a conversation to a person when its confidence drops below 0.8, when the customer asks to cancel, or when a billing change is involved. The agent picks up in the same chat thread with the transcript, the account plan and the bot's read of the problem already on screen.
03What changed:
Eight in ten questions close without an agent
The bot now resolves 80% of queries on its own, and post-chat surveys hold at 94% satisfaction. Answers to the questions it handles arrive in under ten seconds at any hour, and the wait for a human reply on the rest dropped from five hours to forty minutes.
TechFlow stopped adding support headcount and moved two agents onto onboarding calls for new accounts. The prompts, intents and handoff thresholds now belong to TechFlow's support operations lead, who reviews low-rated chats each Friday and changes them from an admin screen without a code release.
WHAT WE DID
Strategy
- Six-month ticket analysis
- Intent map and priorities
- Handoff rules
Build
- Python intent service
- OpenAI answer drafting
- Node.js WebSocket chat
- MongoDB account lookups
Handover
- Admin screen for thresholds
- Agent training sessions
- Runbook and test set
Next case
FleetlyWant this
Start a project