AI for Customer Support
AI Customer Support That Holds Up at Real Ticket Volume
Your support queue grows faster than you can hire, and most “AI support” pilots answer the easy questions while quietly escalating everything that matters. The hard part isn't the chatbot — it's grounding it in your knowledge base, routing accurately, and knowing when to hand to a human before a customer churns. Banao builds production customer support AI that deflects repetitive tickets, triages the rest, and stays accurate under load — the same support stack we run across our own 300-person operation.
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What we deliver
Where Your Support Team Is and Where It Needs to Be
Most support teams sit between two bad options: hire ahead of volume and burn budget, or let response times slide and watch CSAT fall. The bridge is AI that handles tier-1 volume accurately enough that your agents only see tickets that genuinely need a human. Banao has built conversational support at telecom scale — including the Elisa deployment and the EvoAI conversational engine — and we run the same NLP routing and sentiment models inside our own 300-person operation before any of it reaches a client. We engineer deflection, triage, and escalation as one grounded system wired into your existing helpdesk, not bolted on as three disconnected tools.
Deflect Tier-1 Tickets Without Losing Customers
Assistants grounded in your knowledge base resolve repetitive queries and hand off cleanly the moment intent gets complex — handoff logic tuned so customers never hit a dead end.
Get Every Ticket to the Right Agent First Time
NLP classifiers trained on your historical resolution patterns triage, prioritize, and route automatically — cutting the reassignment loops that inflate handle time.
Catch Frustrated Customers Before They Churn
Real-time sentiment and urgency detection across chat and email surfaces at-risk conversations to senior agents early — the same signal we use to flag escalations in our own queue.
Conversations That Understand Your Domain
Conversational engines trained on your product vocabulary, customer history, and multilingual data, so answers are specific to your business — not generic model output.
Instant, Accurate Answers From Your Own Content
Retrieval-augmented generation grounds responses in approved knowledge-base content and cites the source — so the bot answers from your data instead of hallucinating.
Automate Call-Center Volume Without the Maze
Voice assistants and AI-driven IVR resolve routine calls and route the rest with full context, so callers stop repeating themselves across three agents.
Turn Every Conversation Into Measurable Insight
Automated feedback capture and NPS via conversational surveys, with sentiment and theme trends your CX team can act on each week.
One Support Brain Across Web, App, Email, and Voice
Deflection, routing, and escalation orchestrated off a single model and knowledge layer, so customers get the same answer wherever they reach you.
How we deliver
How We Deploy Customer Support AI
- 01
Support Audit & Use-Case Discovery
Identify common pain points, repetitive queries, and escalation patterns to define AI opportunities in your support funnel. Why this matters: teams that skip this automate the wrong queries first and erode trust before they ever see deflection gains.
- 02
Training Data Strategy
Gather historical tickets, chat logs, and knowledge base content to prepare datasets for training NLP and automation models. Why this matters: a bot trained on thin or stale data hallucinates confidently — most failed support pilots die here, not in the model.
- 03
Bot Design & Persona Modeling
Define the tone, personality, and response structure of your virtual assistants in alignment with your brand’s identity. Why this matters: a tone mismatch between bot and brand reads as cheap, and customers disengage before the AI gets to help.
- 04
Model Development & Testing
Build AI models for intent classification, sentiment detection, and conversational flows, followed by rigorous A/B testing. Why this matters: without intent and escalation testing against real edge cases, the bot fails exactly when a customer is already frustrated.
- 05
Multi-Channel Integration
Deploy the bot on your website, mobile app, WhatsApp, or helpdesk with seamless CRM, email, and ticketing integrations. Why this matters: an assistant that can't see CRM and ticket history forces customers to repeat themselves — the fastest way to make automation feel worse than a human.
- 06
Monitoring & Continuous Improvement
Track bot performance, resolution rates, and customer feedback to fine-tune models and improve satisfaction continuously. Why this matters: support language drifts constantly; a model nobody retrains quietly loses accuracy until deflection rates collapse.
Recent work
Recent Work
Manentia AI needed clinicians to act on diagnostic data without being tied to a single workstation. Banao built teletracking software that delivers accurate, real-time reports to physicians on any device, with the data pipeline engineered for reliability under clinical load. Care teams can now review results and respond to patients anytime, anywhere — not just at the desk.
Client reviews
Client Success Stories
“Banao mapped where AI actually belonged in our support funnel before writing a line of code, then shipped deflection and routing that held up in production. The structured, business-first approach is why adoption stuck instead of stalling in a pilot.”
“Banao took our patient-facing support automation from concept to deployment while keeping it inside the access and audit controls healthcare demands. They engineered for the regulated edge cases most vendors discover too late.”
FAQ
Frequently asked questions
We ran an AI support pilot before and it stalled — why would this be different?
Most support pilots fail at data and escalation, not the model: the bot is trained on thin content and has no clean handoff, so it answers easy questions and frustrates everyone on the hard ones. We start with a support audit and a real training-data strategy, build grounded retrieval so answers come from your approved content, and tune human handoff before launch. We've broken and rebuilt our own internal support AI — that scar tissue is part of what you're hiring.
How do you stop the AI from hallucinating wrong answers to customers?
We ground responses in your knowledge base using retrieval-augmented generation, so the assistant answers from approved content and can cite its source instead of inventing one. Anything below a confidence threshold or outside scope is routed to a human rather than guessed. We also monitor accuracy continuously and retrain as your products and policies change.
Who owns the data, models, and IP?
You do — 100%. Custom code, trained models, conversation data, and configurations are yours. We don't retain your data, reuse it to train other clients' systems, or build derivative products on it. For regulated industries we sign DPAs alongside a mutual NDA.
Should we build this in-house or partner with you?
In-house teams typically spend 12–18 months hiring NLP talent and learning the failure modes while support volume keeps climbing. Because this is our day job, we compress that to weeks and bring patterns already proven across telecom-scale and high-volume consumer support. Many of our clients started in-house and came to us six months in — we'd rather save you those six months.
Will it integrate with our existing helpdesk and CRM?
Yes. We deploy on your website, app, WhatsApp, voice, and helpdesk, and integrate with your CRM and ticketing so the assistant works off live customer and ticket context. We're stack-agnostic by design — tell us what you run (Zendesk, Freshdesk, Salesforce, custom) and we'll map the integration approach in scoping.
Can it handle multiple languages and channels at once?
Yes. We build multilingual assistants on translation and language-specific training data, and orchestrate deflection, routing, and escalation across web, app, email, and voice off a single model and knowledge layer — so customers get a consistent answer wherever they reach you.
What does this cost and how long does it take?
Most customer support AI engagements run $50K–$250K depending on channels, languages, integrations, and volume. A focused deployment typically ships in 8–12 weeks, with a scoping conversation locking exact numbers before you commit. The fastest way to a real estimate is a 45-minute scoping call where we map your ticket data and integration surface.