AI Auto-Reply Drafting
An auto-reply drafting agent writes a complete reply for each incoming ticket — drawn from your knowledge base, your past responses and that customer's history — ready for an agent to check and send. It handles the writing; the human keeps the judgement.
Who it's for
Support teams handling high volumes of similar queries.
What changes
Agents handle two to three times the tickets without rushing.
- Starting at
- ₹60,000
- Timeline
- 3–5 weeks
- Category
- AI Support Agents
- Built from
- Vashi, Navi Mumbai
Key takeaways
- Draft-and-review lets one agent handle two to three times the tickets without rushing.
- Consistency is the underrated benefit — the same question gets the same answer.
- Quality is capped by your knowledge base, not by the model.
- Builds in 3–4 weeks from ₹60,000.
- We recommend keeping a human in the loop far longer than most vendors suggest.
Where support agents actually lose time
Not on hard problems. On the twentieth explanation of the same policy that week, written slightly differently each time because the agent is composing from memory at 4pm.
The information almost always exists — in a help article, in a reply a colleague sent last month, in the terms document. Finding it and rewriting it is the work, and it is work that adds nothing a customer values.
How a drafted reply is assembled
The agent pulls from four sources and cites what it used, so the human reviewing it can check rather than trust.
- Your knowledge base and help articles, for the policy or the how-to.
- Past resolved tickets on the same topic — usually the richest source, and the one that carries your actual house style.
- This customer's history: what they bought, what they have already asked, whether they have complained before.
- Live system data where connected — order status, subscription state, outstanding balance.
Draft, review, send — and why not full automation
| Mode | What happens | Where we recommend it |
|---|---|---|
| Draft only | Agent reviews everything before sending | Start here, always |
| Auto-send, high confidence | Simple factual replies go automatically | After 4–8 weeks of measured accuracy |
| Auto-send, all | No human in the loop | Rarely — and never for anything commercial |
| Suggested snippets | Agent composes, agent gets suggestions | Teams with strong existing quality |
Most vendors push for row three quickly because it demos well. We push back. The gap between 92% and 99% accuracy is invisible in a demo and very visible when the 8% includes telling a customer their warranty covers something it does not.
Consistency is worth more than speed
Ask three agents the same policy question and you will get three answers, differing in detail and occasionally in substance. Customers compare notes, especially in forums and WhatsApp groups, and inconsistency is what turns a routine query into a complaint about being misled.
Drafting from one source removes that. When the policy changes, it changes in one place and every subsequent reply reflects it — instead of propagating through the team over several weeks.
Keeping the tone yours
Generic AI support prose is recognisable and slightly cold. We tune tone against your own best past replies rather than against a style guide, which produces something closer to how your team actually writes.
The specifics matter in the Indian context: whether your customers expect "Dear Sir" or a first name, how formal the sign-off should be, whether Hinglish is acceptable in chat but not in email. We set these per channel, because a WhatsApp reply and a formal email are not the same register.
What your team ends up working with
Drafted replies in your helpdesk with sources cited, tone matched to your past responses, per-channel register, customer history and live data pulled in, a confidence score on each draft, an audit of questions your knowledge base cannot answer, and reporting on edit rate — how often agents change the draft, which is the honest quality measure.
FAQ
Auto-Reply Drafting — your questions
How much does an agent actually have to edit?
That is the metric we report weekly, and it is the one to hold us to. Early on, expect meaningful edits on a third or more. After tuning, most teams settle at light edits on the majority and rewrites on a small minority. If the edit rate is not falling, something is wrong with the knowledge base rather than the model.
Can it reply in the customer's language?
Yes, including where your knowledge base is in English and the customer wrote in Hindi or Marathi. The draft comes back in the customer's language with the source cited in English for your agent to verify.
What stops it inventing a policy?
Retrieval grounding plus an explicit instruction to escalate rather than guess. If it cannot find the policy, the draft says so and flags the ticket instead of producing a confident answer. We test this deliberately before go-live by asking it things you have never documented.
Will our agents feel replaced?
How you introduce it decides that. Teams told "this removes the boring 60% so you can handle the interesting cases" adopt it fast. Teams that suspect it is a prelude to cuts find reasons it does not work. That is a management question, not a technical one, and it is worth thinking about before launch.
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- Refund & Cancellation AgentFaster refunds, fewer disputes, one consistent standard.
Next step
Want a Auto-Reply Drafting for your business?
Tell us what the process looks like today and we'll tell you what it would look like automated — and what it would cost.