Overview
The platform operates across Delhi-NCR with a large network of property owners and a dedicated operations team handling tenant onboarding, rental agreements and maintenance coordination.
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 platform's funnel. The conversion math is brutally sensitive to response time: industry data shows the first platform 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 manual callback model wasn't scaling. They wanted a layer that could pick up every lead within seconds, qualify in the tenant's preferred language (Hindi or English), book a property viewing on the right coordinator'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 rental 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 platform to call wins the engagement 78% of the time.
- Hindi-only tenants churned in the first 30 seconds. The coordinator-side script defaulted to English. A significant portion of tenants, especially in Gurgaon, Noida and Faridabad, wanted to speak in Hindi or Hinglish and disengaged immediately.
- Leads never made it into the CRM with full data. Teams 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 operations time. Coordinators double-booked across phone, WhatsApp and email. No-show rate for viewings was high because reminders weren't systematised.
- No way to qualify different tenant types. Family tenants and working professionals 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 Delhi-Hinglish voice, fronting all major portal sources, with branch logic per portal and per listing. 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 tenant 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 Hindi/English/Hinglish switching
The agent detects the tenant's preferred language from their first sentence and switches accordingly, including natural code-switching mid-conversation when tenants do.
Structured discovery script with branching
Budget band, preferred location, move-in timeline, family size, budget confirmation and prior viewings, with response branches per common objection.
Live viewing booking with travel buffers
Agent reads every coordinator's Google Calendar live, respects Delhi-NCR travel buffers, and proposes 2 specific slots, never an open question.
WhatsApp confirmation + reminders
Every booking triggers a confirmed-viewing WhatsApp with the property address, Google Maps pin and coordinator 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 lifted rental conversion rates 2.5× without adding headcount. The Hindi-English switching is what made it work: tenants in Delhi-NCR expect to speak in Hindi, 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.
- Conversion rate lifted 2.5×, headcount unchanged. Rental agreement closures grew substantially across Delhi-NCR, without hiring a single new coordinator or SDR.
- 100% portal-lead callback rate. Every form-fill now gets a call attempt within 30 seconds. Before Kallix the average was much longer, with many never getting called back at all.
- Hindi-tenant engagement up significantly. Tenants preferring Hindi now complete the qualification call at much higher rates because the agent meets them in their language.
- Team NPS climbed. Coordinators stopped doing first-touch qualification by hand and only handled escalated issues. 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. Tenant data never leaves Indian data centres.
The Delhi-NCR Rental 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 Delhi-NCR rental platform 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 rental query taxonomy
- Hindi, 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: Delhi Hinglish, warm-and-fast, deferential to tenants
- Pronunciation dictionary published for Delhi-NCR micro-markets and rental property 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 Hindi/Hinglish tenants in Delhi-NCR to drop in the first 30 seconds
- Leads never reached the CRM with full data because teams skipped end-of-day entry
- High viewing no-show rate from manual booking across phone, WhatsApp and email
- 2.5× rental conversion rate in 90 days with zero added headcount
- 100% portal-lead callback rate: every form-fill dialed within 30 seconds
- Hindi-tenant 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 Delhi-Hinglish persona on all major portal webhooks
- Structured discovery script with branching per objection, portal and tenant 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 Hinglish handling: the others either spoke pure Hindi or pure English, both of which created friction with local tenants. Second, the CRM integration was already built and battle-tested with other rental platforms. 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, property-specific scripts, and seasonal cadence changes all happen inside that weekly loop. The agent is measurably sharper today than it was on launch day.