Agentic AI · Customer support AI agents
Banao builds customer support AI agents that handle Tier 1 and Tier 2 contacts end to end — reading the query, pulling the account record, answering or acting, and handing off to a person with full context when the case falls outside what the agent can safely resolve.
The agents we develop are not FAQ wrappers. They call your CRM, update tickets, process returns, and close requests — on the channels you already run, with a trace on every step and a human gate on any action that carries real consequence.
Banao— We run agentic query handling on our own hiring pipeline before we deploy it for clients.
A support agent in production handles the full contact — not just the lookup, but the action and the record update — with tracing on every step.
The agent reads the incoming message, identifies the intent — refund, order status, account issue, complaint — and routes to the right resolution path without keyword matching.
For policy and product questions, the agent retrieves the current answer from your knowledge base and generates a response grounded in it, with a citation so the customer knows the source.
The agent calls your CRM and order management system to pull the account record, order status, or ticket history — acting on real data, not placeholders.
Standard returns, status queries, password resets, and billing clarifications are resolved end to end without a human in the loop, with the ticket closed and the record updated.
When a case needs a person, the agent hands off the full conversation, the actions it already took, and its read of the issue — so the human agent does not start from zero.
We deploy the same agent logic across email, live chat, WhatsApp, and your help-desk widget — one configuration, consistent behaviour on every channel.
Every resolved and escalated contact is reviewed against the outcome. We retrain and adjust on the cases the agent got wrong, closing gaps as volume data accumulates.
The agent masks, redacts, or avoids storing personal data per your data policy — DPDP, GDPR, or PDPL depending on the deployment region.
The typical chatbot is an FAQ lookup in a chat interface. It answers the questions its builder thought of, fails on the ones they did not, and presents a dead end the moment the customer needs something that requires an action in a system.
A customer support AI agent is different in kind. It understands free-form intent, calls your backend systems, and can actually do something — update a record, trigger a return, close a ticket — not just describe the policy. Getting from a deflection rate that plateaus at 30% to one that holds above 60% requires the agent to move from answering to acting.
The architecture that makes this work is more involved than a chatbot: a planning loop that breaks a contact into steps, tool calls to your CRM and ticketing system, grounding in your current product and policy data, and guardrails that define which actions the agent can take on its own and which need a human sign-off. We build all of it as one deliverable.
"I got the wrong size and my event is on Friday" is an intent the agent must read, not a keyword to match. We train and evaluate on the messy, ambiguous inputs real customers send.
An agent that says "you can return your item via the returns portal" is a worse experience than no agent at all. Ours initiate the return, confirm the label, and update the ticket.
Policy documents, product catalogues, and SLA terms change. The agent is wired to your live knowledge base, not a snapshot baked into a model months ago.
The engineering for a production-grade support agent is concentrated in the places a demo hides: the edge cases, the system integrations, and the evaluation harness.
Edge cases in support are common and consequential. A customer who is angry, who has already contacted twice, whose order is in a payment dispute, whose account has a fraud flag — the agent must handle each differently from the default path, and the handling must be defined, tested, and traced.
The system integrations are the payload. Without a live call into your CRM, the agent is an expensive FAQ bot. We integrate through your APIs and build the retry, error, and fallback logic that makes the integration reliable at volume.
We build a task-level evaluation suite from your real contact history — the awkward, the adversarial, and the high-consequence cases — and score the agent against it before any live contact goes through it.
We start the agent on a subset of contact types — the clearest, highest-volume, lowest-risk ones — and widen coverage only as the eval numbers support it.
Every step the agent takes — the intent read, the system calls, the answer it chose — is logged and queryable, so your QA team can audit any contact after the fact.
Metrics are pending the post-launch measurement pack. Results will be published once verified.
E-commerce operator (anonymized)
An e-commerce operator with high order-status and returns volume deployed a support agent wired to their OMS and returns portal. Tier 1 deflection and average handle time on escalated contacts are being tracked through the measurement period.
Banao's InterviewGod agent handles the inbound query volume from applicants — screening, scheduling, and FAQ handling — before any recruiter touches the queue. It is the same pattern as a customer support agent applied to a hiring pipeline: inbound intent, system lookup, action or escalation, human hand-off when the case needs it.
Running agents on our own operations before deploying them for clients is not a sales point. It is the only way we know the failure modes before they become your problem.
We will tell you this before you commit a budget — not after:
We do not quote a support agent build off a brief. We test the part most likely to fail first.
We pick the highest-volume, most-automatable contact type, test the agent on your real data, and hand back a scoped design, an eval plan, and deflection-rate projections — yours to keep. If you proceed, the Sprint fee is credited against the build.
We develop the planning loop, CRM and ticketing integrations, knowledge-base grounding, escalation logic, guardrails, and the evaluation suite — as a single deliverable, not a sequence of handoffs.
We launch behind a staged rollout, track deflection and escalation quality, and close eval gaps on the contact types the agent handled poorly. Coverage widens as the numbers earn it.
What is a customer support AI agent, and how is it different from a chatbot?
A customer support AI agent understands free-form intent, calls your backend systems, takes actions — updating a ticket, processing a return, pulling an account record — and hands off to a person with full context when the case is outside its scope. A chatbot matches keywords to scripted answers and presents a dead end when it cannot match. The distinction is the ability to act on real systems, not just describe a policy.
Which support channels can the agent work on?
We deploy the same agent logic across email, live chat, WhatsApp, and help-desk widgets. The channel is the delivery mechanism; the reasoning and system-integration layer is shared. You do not need to maintain separate agents per channel.
What systems does the agent need to connect to?
At minimum: your CRM or customer database and your ticketing system. Depending on the use case, we also integrate with your returns portal, payment system, and knowledge base. We work through existing APIs — we do not require you to replace working software to add an agent.
What deflection rate can we expect?
It depends on your contact mix and how cleanly your backend systems expose the data the agent needs. In our Discovery Sprint we produce a projection based on your actual contact history and a feasibility test against your hardest cases, rather than quoting an industry benchmark that may not apply to your volume.
How do you handle queries the agent cannot answer?
The agent hands off to a human queue with the full conversation, the steps it already took, and its read of the issue — so the agent is a pre-brief, not a dead end. We define the escalation triggers with you during scoping: the contact types, the confidence thresholds, and the signals that mean a human must take over.
How do you stop the agent from giving a wrong answer?
We ground the agent in your live knowledge base and product data, so it answers from your facts rather than the model's training data. We also build an eval suite from your real contact history and score the agent on it before launch, so we can measure error rate on the cases you care about — not a synthetic benchmark.
How long does it take to deploy?
A 2-week Discovery Sprint, then a 6–10 week build depending on the number of contact types and integrations, then a staged rollout. Banao's ~300-engineer delivery bench means the build begins in weeks, not quarters.
What does it cost?
The Discovery Sprint is a fixed price and produces the scope, eval plan, and deflection projections you need to size the build. Build cost depends on the contact types covered, the integrations required, and the eval coverage needed — the Sprint pins these down before you commit to the full engagement.
In 45 minutes we will tell you whether an AI agent can own it — and what a production-grade build would take to get there.
Book a 45-min scoping call
