Redditor says he broke Zomato's AI after it refused to connect him to a human, post goes viral

Viral Reddit post: "I broke Zomato's AI support." What customer support teams should learn

A Reddit user shared screenshots of a messy chat with Zomato's AI assistant, Nugget AI, while trying to cancel extra items added by mistake. The bot repeatedly refused to connect him to a human. After spamming "Let me talk to customer support/agent," the system finally said it was escalating-by then, the order was already picked up.

His point was blunt: the AI couldn't handle a simple, time-sensitive request and didn't route him to a person when it mattered. He ended the post saying he's done with the service.

What actually happened (in short)

• Customer rushed an order, accidentally added extra items.
• Restaurant agreed to cancel the extras but said it had to go through platform support.
• AI assistant failed to understand or escalate quickly to a human.
• Escalation came only after repeated "human agent" requests, but too late.

Why this matters for support leaders

Time-sensitive intents like "cancel/modify order" are a different class of problem. A 2-3 minute delay kills the fix window and guarantees churn. If your AI forces users through loops, you don't just frustrate them-you create waste (unwanted food) and increase refunds, re-dispatches, and bad reviews.

Failure points to fix

• No priority routing for time-critical intents: Cancel/modify should be fast-lane flows with immediate human access.
• Poor intent recall: Repeated "agent/human/support" requests weren't treated as a hard escalation trigger.
• Bot refusal language: Saying "can't connect" feels like stonewalling. It signals you care more about containment than resolution.
• Thin integrations: If the restaurant can cancel but the platform can't act instantly, the system is miswired.
• No frustration detection: Rapid-fire messages, repeated keywords, or negative sentiment should trip an immediate handoff.

What to implement now

• Human-first for critical intents: For "cancel," "modify," "wrong address," "allergy," "payment error," route to a person in seconds-no questions asked.
• Hard escalation triggers: If a user says "agent," "human," "person," or repeats it 2+ times, auto-handoff.
• One-tap cancel window: Give users a 60-120 second cancel button after order placement that doesn't require chat at all.
• Restaurant sync: If the merchant agrees to cancel items, let support confirm and execute immediately. No bouncing.
• Bot phrasing that helps: Replace refusals with clarifying options: "I can connect you to a person now or try to cancel the items. Which do you prefer?"
• Timer-aware routing: If pickup ETA is under X minutes, escalate by default to a senior queue.
• Clean handoffs: Send the full transcript and detected intent to the agent so the user doesn't repeat themselves.
• Safety net playbooks: If the cancel fails, issue a credit and flag the merchant to avoid food waste. Communicate clearly.
• Shadow-mode testing: Before launch, run the bot in parallel with humans on live traffic and compare outcomes on cancel/modify scenarios.

Metrics that tell the truth

• Time to human (TTH): Median and 95th percentile for critical intents.
• Cancel success rate within window: Percentage of cancels completed before pickup.
• Containment with satisfaction: Bot-only resolutions that still hit CSAT/effort targets.
• Repeat-contact rate within 24 hours: If it's high, the bot is deflecting, not resolving.
• Waste/refund cost per incident: Track before/after changes to prove value.

Agent experience upgrades

• Pre-filled macros: "Partial item cancel," "Restaurant approved," "Pickup imminent" with policy-safe actions.
• Real-time status cards: Rider location, prep stage, and cancel eligibility at a glance.
• Priority lanes: A visible "time left to pickup" timer that bumps the conversation to the top of the queue.

Policy clarity

• Publish a simple cancel policy: time window, what's auto-approvable, and what triggers credits.
• Train the bot to summarize that policy in one sentence, then act-no long scripts.

Helpful resources

• NN/g: Guidelines for better chatbot UX
• AI upskilling for customer support teams (course collections)

The takeaway is simple: AI is useful, but control and speed matter more. Make escalation effortless, protect the cancel window, and wire your systems so the bot helps the agent-not blocks the customer.