Retail & E-commerce · Customer support automation
Most support tickets already have an answer — your team just looks it up every time
Banao deploys AI customer support automation that classifies every inbound query, handles order-status, return initiation, and policy questions without a human touch, and routes cases that need a person with context already assembled.
The system integrates with your OMS, your policy rules, and your existing helpdesk. It handles multilingual queries, preserves audit trails for regulated responses, and passes clean handoffs to agents on exceptions — so your team's time goes on the tickets that actually need judgment.
The first call is free · 45 minutes · no obligation
What we build
What a Banao support automation deployment includes
Customer support automation is not a generic chatbot. It is classification, integrations, escalation logic, and the handoff quality that makes agents trust it.
Intent classification and routing
Every inbound ticket — chat, email, form — is classified against your real intent taxonomy and sent to the right handler or human queue. Not a keyword match: a model trained on your own historical tickets.
WISMO automation with OMS integration
Order-status queries are resolved end-to-end by pulling live data from your OMS and composing a personalised response. No human lookup, no copy-paste from the tracker.
Returns and refunds against your policy
The AI checks eligibility against your return window, category rules, and seller terms, and initiates the workflow — or flags the exception for a human when policy is genuinely ambiguous.
Agent assist for human queues
When a ticket escalates, the agent gets a brief: order history, prior contacts, the AI's reason for escalation, and the relevant policy section. The agent answers in seconds, not minutes.
Multilingual query handling
Retail customers write in Hindi, Tamil, Arabic, or whatever language they reach for first. The system classifies and responds in the query language, with response templates reviewed in each market.
Escalation logic that agents trust
Hard rules define what the AI cannot touch — complaints above a value threshold, legal language, safety flags. Those go straight to a human, every time, with no AI attempt at a response.
Dogfooding
We run our own AI before asking you to run yours
Banao operates a ~300-person engineering company on its own AI products. Vikaas handles our demand-generation pipeline — classifying leads, routing enquiries, and composing first-contact responses — the same pattern we build for support desks.
A system that handles our own inbound volume before it reaches a client is already tested on real edge cases, not just benchmarks. That is the bar we hold every support automation deployment to.
Runs Banao's demand-gen classification and routing end to end.
Screens Banao's own engineering hires every week.
The honest version
When support automation is the wrong investment
AI handles volume well. It handles ambiguity and judgment poorly. We will tell you which side of that line your tickets sit on:
- Low ticket volume: below a few thousand tickets a month, a well-trained human team is cheaper and more flexible. The setup cost does not earn back.
- Policy that changes constantly: if your return rules, seller terms, or pricing logic shift monthly, the automation will give wrong answers faster than you can retrain it.
- Poor OMS data quality: WISMO automation is only as accurate as the order data it reads. If your OMS has gaps or delays, the bot will confidently give wrong status. The Discovery Sprint finds this in week one.
- High-stakes exceptions as the norm: if most of your tickets are complex disputes or regulated complaints, automation handles the margins — the ticket mix doesn't support the economics.
How we start
How we start — find the automation ceiling before you build it
We don't quote a support automation platform off a category description. We read your actual tickets first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We analyse a sample of your real ticket history — intent distribution, resolution patterns, escalation triggers — and hand back an automation rate estimate, an OMS integration map, and the honest cases where AI falls short. Yours to keep. If you proceed, the Sprint credits against the build.
- 02
Build
Train the classification model on your ticket history, integrate with OMS and helpdesk, configure policy rulesets for returns and refunds, and build the agent-assist context brief. Multilingual templates are part of the deliverable.
- 03
Production and continuous tuning
Go live with monitored escalation thresholds, a CSAT baseline for automated versus human resolutions, and a retraining cadence tied to policy changes. Escalation logic is tuned in the first 60 days against real deflection data.
FAQ
Frequently asked questions
How does WISMO automation work if our OMS data has gaps?
It doesn't — reliably. That is one of the first things the Discovery Sprint checks: OMS data completeness and latency. Where gaps exist we scope them as integration prerequisites, not model problems. Giving customers wrong order status is worse than giving them none.
What happens when the AI can't match a query intent?
It escalates with a confidence flag. The agent gets the original message, the top-two intent guesses, and a note that the model was uncertain. Your team audits these and corrections feed back into the classifier — so the unmatched rate falls over time rather than staying fixed.
Can it handle our return policy across different seller terms?
Yes, if the policy is encoded. We map your category rules, seller-specific windows, and exception flags into the policy layer at build time. Where terms conflict or are genuinely ambiguous the AI escalates — it does not guess.
Does it integrate with Zendesk, Freshdesk, or our in-house helpdesk?
Zendesk and Freshdesk integrations are standard. For in-house helpdesks, the week-one audit establishes whether a webhook integration is straightforward or needs a custom adapter.
How do we measure whether the automation is working?
Three numbers matter: deflection rate (tickets resolved without a human), CSAT delta between automated and human resolutions, and escalation accuracy (did the AI escalate what it should have). We set a baseline at go-live and report against it monthly.
Get started
Bring your top ten ticket categories
In 45 minutes we will show you which ones automation handles completely, which ones need a hybrid approach, and which ones should stay with a human. No pitch deck — just your tickets.
Book a Discovery Sprint