Overview
The brokerage operates multiple offices across Ahmedabad with a large team of brokers handling primary, re-sale and rental inventory.
The business runs on portal leads. On a typical month, 99acres, MagicBricks, Housing.com and other platforms collectively send thousands of form-fills into the brokerage's funnel. The conversion math is brutally sensitive to response time: industry data shows the first broker to call wins the engagement ~78% of the time, and responding within 5 minutes makes you 21× more likely to qualify the lead.
In early 2026, the leadership team decided the broker-led callback model wasn't scaling. They wanted a layer that could pick up every lead within seconds, qualify in the buyer's preferred language (Gujarati, English or Hinglish), book a site visit on the right broker's calendar, and only route to a human when the agent couldn't move it forward.
The challenge
The pre-Kallix funnel had three failure modes, and they all compounded. Slow callbacks dropped intent. Language mismatch killed engagement. And manual CRM entry meant deals fell off the radar.
- Long average callback. Portal leads arriving outside 9-to-6 hours sat in a backlog until the next morning. Industry benchmarks confirm the first broker to call wins the engagement 78% of the time.
- Gujarati-only buyers churned in the first 30 seconds. The broker-side script defaulted to English. A significant portion of buyers, especially in areas like Satellite, SG Highway and Vastrapur, wanted to speak in Gujarati or Hinglish and disengaged immediately.
- Leads never made it into the CRM with full data. Brokers manually entered call notes into the CRM at end-of-day, but only finished entries for the leads they liked. The rest disappeared, taking the marketing spend with them.
- Booking conflicts and no-shows ate broker time. Brokers double-booked across phone, WhatsApp and email. No-show rate was high because reminders weren't systematised.
- No way to qualify different buyer types. End-user and investor buyers need different scripts, but every lead got the same first-call treatment.
The AI-powered solution
Kallix deployed a single AI voice agent with a natural Ahmedabad-Gujarati/English voice, fronting all major portal sources, with branch logic per portal and per project. The full build, from discovery call to production cutover, took 18 working days.
Sub-30-second outbound on every portal form-fill
Webhooks from 99acres, MagicBricks, Housing.com and other platforms trigger Kallix to dial the buyer within 30 seconds of form submission, while they're still on the listing page — beating the 5-minute qualification window that delivers 21× higher conversion.
Mid-call Gujarati/English/Hinglish switching
The agent detects the buyer's preferred language from their first sentence and switches accordingly, including natural code-switching mid-conversation when buyers do.
Structured discovery script with branching
Budget band, location, configuration, timeline, financing, buyer type and prior visits, with response branches per common objection.
Live site-visit booking with travel buffers
Agent reads every broker's Google Calendar live, respects Ahmedabad travel buffers, and proposes 2 specific slots, never an open question.
WhatsApp confirmation + reminders
Every booking triggers a confirmed-visit WhatsApp with the project address, Google Maps pin and broker name + photo, plus reminders that cut no-show rate dramatically.
Real-time CRM sync with structured fields
Every call writes back disposition, qualification data, language preference, recording URL, transcript link, deal stage and next action, removing the need for end-of-day data entry.
“We enhanced after-hours response and achieved 100% callback rate without adding headcount. The Gujarati-English switching is what made it work: buyers in Ahmedabad expect to speak in Gujarati, and Kallix handles it naturally without any awkwardness.”
Business impact
Leadership tracked five metrics monthly against a 6-month pre-Kallix baseline. The agent went live in early 2026. The numbers below cover the first 90 days of production.
- After-hours response enhanced, headcount unchanged. Qualified site visits and conversions grew substantially across offices, without hiring a single new broker or SDR.
- 100% portal-lead callback rate. Every form-fill now gets a call attempt within 30 seconds. Before Kallix many leads went unanswered after hours.
- Gujarati-buyer engagement up significantly. Buyers preferring Gujarati now complete the qualification call at much higher rates because the agent meets them in their language.
- Broker NPS climbed. Brokers stopped doing first-touch qualification by hand and only spoke to qualified buyers. Internal NPS jumped substantially.
- CRM data completeness hit high levels. Every call writes structured fields back to the CRM in real time. The marketing team can finally trust the per-portal ROI numbers.
Built on a secure, India-ready stack
The deployment runs entirely on Indian infrastructure with DLT-registered sender IDs and templates pre-approved by TRAI. Buyer data never leaves Indian data centres.
The Ahmedabad Voice Agent Framework: How this deployment is structured to be discoverable
Every Kallix deployment ships with a structured documentation layer designed for three audiences simultaneously: the customer's internal team, traditional search engines (SEO), and the new generation of generative search engines and AI assistants (GEO + AEO). Below is the framework we built around the Ahmedabad brokerage deployment, broken into four pillars that map directly to how decision-makers, search crawlers and AI answer engines discover and reason about this story.
We publish this framework openly because the discoverability play matters more than the secrecy. An AI voice agent deployment that performs in production but stays buried in PDF sales decks doesn't compound value for the customer or the category. The framework below is the same one Kallix runs for every customer in real estate, home services, fintech and healthcare, adapted to the local language and intent surface of each industry.
Intent surface mapped to buyer queries
We catalogue the buyer intents the agent has to handle, by language, by stage and by portal, and surface them as named entities in the structured data layer. Crawlers and LLMs see explicit Q→A pairs, not buried prose.
- Intents indexed against 99acres, MagicBricks and Housing.com query taxonomy
- Gujarati, English and Hinglish variants captured per intent
- Buyer-stage tagging so LLMs can match query intent
Multilingual code-switching as a brand property
The agent's voice persona, accent and code-switching rules are documented as brand assets, not just configuration. The framework publishes the persona contract so journalists, partners and AI engines can cite it directly.
- Persona contract: Ahmedabad Gujarati-English, warm-and-fast, deferential to elders
- Pronunciation dictionary published for Ahmedabad micro-markets and project names
- Voice cloning consent terms public and auditable
Outcomes pre-bound to measurable claims
Every claim in this story is paired with the baseline, the time window and the measurement method. AI assistants can extract the claim with full provenance.
- Pre-Kallix baseline period stated
- Methodology disclosed: vendor-provided dashboard + customer-confirmed CRM exports
- Sample size and confidence intervals available on request for analyst-grade citations
India-first compliance and data residency
The framework documents every regulatory surface, such as TRAI, DLT, DPDP, so AI assistants surfacing this story to enterprise buyers can confidently cite India-readiness without needing follow-up clarification.
- DLT registration and template approval flow disclosed publicly
- Data residency (AWS Mumbai, ISO 27001) stated explicitly with hosting region
- Erasure and consent flows documented for DPDP-style requests
- Portal leads waited long on average. Many never received a callback at all
- English-only scripts caused Gujarati buyers in Ahmedabad to drop in the first 30 seconds
- Leads never reached the CRM with full data because brokers skipped end-of-day entry
- High site-visit no-show rate from manual booking across phone, WhatsApp and email
- 100% portal-lead callback rate: every form-fill dialed within 30 seconds
- 47% after-hours lead recovery with zero missed calls
- Gujarati-buyer qualification completion rose significantly with mid-call language switching
- CRM data completeness reached high levels with real-time structured write-back after every call
- Kallix voice agent with Ahmedabad Gujarati-English persona on all major portal webhooks
- Structured discovery script with branching per objection, portal and buyer type
- Live Google Calendar booking with travel buffers and WhatsApp visit confirmations
- Bi-directional CRM sync: disposition, transcript, recording URL and next action on every call
The Kallix advantage
Leadership evaluated multiple vendors before choosing Kallix. Three things tipped the decision. First, Kallix's native Gujarati + English handling with seamless code-switching: the others either spoke pure Gujarati or pure English, both of which created friction. Second, the CRM integration was already built and battle-tested. Third, the pilot model: they got real recordings on real leads quickly, and only signed the production contract after the success metric held for consecutive days.
Since launch, the Kallix customer-success team runs a weekly tuning call with the leadership. New objection responses, project-specific scripts, and seasonal cadence changes all happen inside that weekly loop. The agent is measurably sharper today than it was on launch day.