Support agents that resolve tickets, not just deflect them.
AI agents on website chat, WhatsApp, email and in-app, grounded in your knowledge base, connected to your systems, escalating cleanly to humans.
What is an AI customer service agent?
An AI customer service agent resolves support requests end to end: it answers from your knowledge base, looks up the customer's own orders and account, performs policy-approved actions such as refunds or plan changes, and escalates to a human with full context. Eazyware deploys these agents on web chat, WhatsApp, email and in-app in three to six weeks.
| Service line | AI Agents & Automation |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $12,500 |
| Typical range | $12,500 – $42,000 |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Support volume grows faster than headcount. Generic chatbots answer from FAQs and fail the moment a customer asks about their order, their invoice, their account.
How do we approach it?
We start with your tickets, not with a chatbot. Two weeks of reading and clustering historical conversations tells us which intents make up most of the volume, which can be resolved with knowledge alone, which need account data, and which need a person. The knowledge pipeline ingests your policies, help centre and resolved tickets; the action layer connects to your helpdesk, order system or CRM with permissions scoped to what a tier-one agent could do. The agent launches in shadow mode drafting replies your team sends, moves to assisted mode where it acts with approval, and becomes autonomous per intent as the numbers earn it. Everything it could not answer becomes a list of gaps for your knowledge base.
What do clients use it for?
- Order, refund and account queries on WhatsApp and web
- Tier-1 support deflection with clean escalation
- Agent-assist inside Zendesk, Freshdesk or HubSpot
- In-app support for SaaS with account context
Is it the right fit?
Good fit when
- Support teams whose volume outpaces headcount
- E-commerce and SaaS with repeatable intents
- Teams with a knowledge base and a helpdesk
Probably not when
- Products with no documented policies or answers
- Support that is mostly bespoke consulting
What do we build?
- Omnichannel agents: web, WhatsApp Business, email, Slack, Teams, in-app
- Retrieval over your docs, policies and past tickets
- Account-aware actions: order lookup, refunds within policy, plan changes, ticket creation
- Sentiment-based escalation and agent-assist for human reps
- Multilingual support
- CSAT tracking, resolution analytics and knowledge-gap detection
What you get
- Deployed agent
- Channel integrations
- Knowledge pipeline
- Escalation rules
- Analytics dashboard
How does the engagement work?
- 01
Ticket analysis and intent mapping
- 02
Knowledge pipeline
- 03
Agent build
- 04
Shadow mode
- 05
Assisted mode
- 06
Autonomous for approved intents
What does good look like?
The routine majority of conversations resolved end to end, with the customer's own order or account in the answer, on the channels where customers already are. Escalations arrive with the conversation summarised. CSAT on AI-handled conversations is measured, not assumed. And the support team's day shifts to the conversations that need judgement.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Support automation goes wrong when it answers from FAQs about a customer's specific problem, when it can take actions outside policy, when escalation is a dead end, and when nobody looks at what it could not answer. We ground in account data, gate actions on policy, design the hand-off, and turn failures into knowledge-base work.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Auto-resolution rate by intent
- First response and resolution time
- CSAT on AI-handled tickets
- Knowledge gaps detected
Which technologies do we use?
- OpenAI / Anthropic
- Meta WhatsApp Cloud API
- Zendesk / Freshdesk / HubSpot / TheEazy CXM
- pgvector
- Node.js
Who does the work?
An AI engineer for retrieval and the agent, an integration engineer for your helpdesk and channels, and a delivery lead who runs the shadow and assisted phases with your support manager.
What do you need to bring?
An export of historical conversations for intent analysis, your policies and help centre, access to the helpdesk or CRM and the channels (WhatsApp Business, email, web), and a support manager to run the shadow and assisted phases with us.
Frequently asked questions
Will it hallucinate refunds?
Actions are policy-gated and permissioned. Anything outside policy goes to a human.
Works with our helpdesk?
Yes: Zendesk, Freshdesk, Intercom, HubSpot, or a custom CRM including TheEazy CXM.
How fast to launch?
Three to six weeks for the first channel.
Where does this fit?
AI Customer Service Agents is part of our AI Agents & Automation line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.