How Bella Vita Cut Its Dependency on Calling Agents and Scaled Conversions with LimeChat's E-commerce AI Bot

Company Overview

Bella Vita has built a loyal customer base around one simple pitch: fragrances that smell (and feel) premium, at a price that doesn't. But that same value proposition creates a very specific pre-purchase problem — buyers want to be talked into a scent before they buy one they've never smelled. Notes, longevity, occasion-fit, gifting — these aren't queries a click-based bot can answer convincingly, and they aren't cheap to answer with a human calling team either.

Challenge

Bella Vita's WhatsApp channel was only automating around 20% of conversations. Everything past order tracking and basic FAQs — "which perfume suits an oily-skin, gym-going guy," "will this last through a wedding day," "what's a good gifting combo under ₹1,500" — got routed to a calling team. That team's job was to nurture and close these leads over the phone, which worked, but it meant every high-intent conversation needed a human on it before it could convert. That's an expensive, hard-to-scale way to sell perfume.

Solution with E-commerce AI Bot

Bella Vita deployed LimeChat's E-commerce AI Bot on WhatsApp to take over exactly the conversations the old system couldn't close on its own:

• Answering nuanced, subjective pre-purchase questions (notes, occasion, skin/climate fit, longevity) the way a well-trained store associate would — not with a static FAQ, but a real answer pulled from product data.
• Recommending and linking directly to the right product mid-conversation, instead of dropping the customer back onto the website to figure it out themselves.
• Handling the full journey from question to conversion on WhatsApp itself, so a hot lead didn't need to wait for a callback to be pushed toward a purchase.

Implementation Process

• Initial Pilot: LimeChat fine-tuned the AI bot on Bella Vita's product catalog and query history, so it could speak fluently about notes, longevity, and use-cases the way Bella Vita's own team would.
• Integration: The bot was plugged directly into Bella Vita's WhatsApp channel, with product recommendations and links surfaced inline as part of the conversation.
• Continuous Learning: The bot kept learning from live conversations to sharpen its recommendations and reduce hand-offs over time.

Results

• Automation rate jumped from 20% to 70% — the bot now closes the vast majority of conversations that used to need a human to carry them across the line.
• Calling team shrunk by 10 agents — with the bot handling nurture-and-convert on WhatsApp itself, Bella Vita no longer needed the same scale of outbound calling to close hot leads.
• Average daily orders rose from a base of 50-60/day — with AI-led conversations converting more of the traffic that used to stall out waiting on a callback.

Impact

For Prabhat, CX Head, and Sanchit, India Head at Bella Vita, the shift wasn't just about cost — it was about not losing a hot lead to the gap between "I have a question" and "someone calls me back." Letting the AI bot answer and convert in the same conversation meant Bella Vita could scale conversions without scaling its calling team in lockstep.

Conclusion

Bella Vita's rollout of LimeChat's E-commerce AI Bot shows what happens when a brand stops treating WhatsApp as a support channel and starts treating it as a selling channel. By letting AI carry conversations that used to need a human on the phone, Bella Vita cut its dependence on a large calling team while converting more of its daily traffic — proof that in a category built on trust and personal recommendation, AI can do the convincing too.