Real Estate CRM + AI
This is our existing Real Estate CRM with an AI layer on top: leads scored as they arrive, follow-up calls placed automatically, site visits confirmed and reminded over WhatsApp, buyers matched to available units, and payment milestones chased without anyone remembering to. Built around how Indian developers and brokers actually sell.
Who it's for
Builders and brokers already running the CRM.
What changes
The same sales team works twice the leads.
- Starting at
- ₹40,000
- Timeline
- 2–6 weeks
- Category
- AI for Your Existing Software
- Built from
- Vashi, Navi Mumbai
Key takeaways
- Most real estate leads die from slow first contact, not from being poor leads.
- Scoring lets a small sales team spend their hours on the enquiries worth their time.
- Site visit no-shows drop sharply with automated confirmation and reminders.
- Runs on the existing Lacewing CRM — this is an upgrade, not a migration.
- Deploys in 6–10 weeks from ₹2,50,000 for an existing CRM client.
Where real estate leads are lost
A portal enquiry that gets a call within five minutes converts at a completely different rate from one called back the next afternoon. Most developers know this and still call back the next afternoon, because the enquiry arrived at nine at night and the sales team saw it at eleven the next morning.
The second loss point is the site visit. A booked visit with no confirmation has a no-show rate that makes the whole weekend's planning unreliable.
The third is the long tail. A buyer who is six months from deciding gets three calls and then nothing, and buys from whoever was still in touch when they were ready.
The AI layer targets those three specifically rather than adding features across the product.
What gets scored and on what
Lead scoring only helps if it reflects what actually predicts a sale in your projects, which is why it is configured against your own closed data rather than a generic model.
| Signal | Weight | Why |
|---|---|---|
| Budget stated versus project pricing | High | The most common disqualifier |
| Source channel | High | Portal, referral and walk-in differ sharply |
| Response to first contact | High | Engagement predicts more than demographics |
| Location of the enquirer | Medium | Proximity affects site visit likelihood |
| Configuration requested versus available | Medium | Match to actual inventory |
| Time to first response | High | Your own speed, tracked as a factor |
| Repeat enquiry | High | Second enquiry is a strong signal |
The follow-up that keeps happening
The automation is deliberately modest in what it says and consistent in that it says it.
- Immediate acknowledgement. A WhatsApp message within a minute of the enquiry, with project details, so the buyer knows they have been heard.
- Voice callback on high-score leads. An AI call within minutes that qualifies budget and timeline and books a visit, handing to a human on interest.
- Site visit confirmation and reminder. The day before and two hours before, with directions and the contact person.
- Post-visit follow-up. Same evening, asking what they thought, which is when opinions are honest.
- Long-tail nurture. Monthly project updates to buyers who are not ready, so you are present when they are.
Matching buyers to inventory
In a project with a hundred and twenty units across four configurations, matching a buyer to what is actually available and suitable is a task a good sales manager does in their head and a new joiner does badly.
The system does it against live inventory: budget, configuration, floor preference, facing, and what is genuinely unsold rather than what was unsold last week.
It also surfaces near-matches with a reason — a unit slightly above budget with a better view, or a different configuration at the same price — which is the conversation that closes deals when the exact request is not available.
Payment milestone chasing
Construction-linked payment plans generate a steady stream of collection work that nobody enjoys and everyone postpones.
The system tracks each booking's milestones, sends demand notices at the trigger, follows up on schedule through WhatsApp and voice, and escalates to the accounts team only when a buyer is genuinely unresponsive.
The tone matters here and is configured carefully. A buyer who has committed to a flat is a customer for years, and automated collection messages that read as aggressive damage a relationship that is worth far more than the fortnight's delay.
What this costs and how it deploys
Six to ten weeks from ₹2,50,000 for an existing Lacewing Real Estate CRM client, covering scoring configured against your historical data, voice and WhatsApp automation, inventory matching and the collection workflow.
For a developer not currently on the CRM, the base implementation comes first and we would quote that together.
Running costs are usage-based: voice calls at ₹4–₹12 per minute and WhatsApp conversations at Meta's per-window rates. For a project generating four hundred enquiries a month, that typically lands between ₹8,000 and ₹25,000.
FAQ
Real Estate CRM + AI — your questions
Will buyers know they are talking to an AI?
Yes, because the call says so at the start. Attempting to pass a synthetic voice off as a person is both dishonest and counterproductive — buyers work it out and feel misled at exactly the point you are trying to build trust. In practice, disclosure does not hurt conversion for a first qualifying call, provided the handover to a human happens quickly once the buyer shows real interest. That handover speed matters much more than the disclosure.
Can it work with portal leads from 99acres and MagicBricks?
Yes, through their lead APIs or email parsing where an API is not available. Portal leads are exactly where speed matters most, since the same enquiry usually went to several developers and the first substantive response has a large advantage. We set up the integration so a portal enquiry triggers acknowledgement and scoring within a minute of arriving, which is well ahead of a manual process.
What about brokers and channel partners?
The CRM handles channel partner attribution, and the AI layer respects it — a lead registered by a broker is not called by your in-house team in a way that creates a conflict. Partner performance reporting comes with it, showing which channel partners bring leads that actually convert rather than which bring the most leads. That distinction usually changes how commission structures are set.
Does it handle multiple projects?
Yes, with per-project configuration for inventory, pricing, scoring weights and messaging. A buyer enquiring about one project can be matched against another where it suits their budget and location, which recovers enquiries that would otherwise be lost as unsuitable. Cross-project suggestions are configurable, since some developers want them and some prefer each project's team to work its own pipeline.
How is buyer data protected?
It sits in your CRM on your infrastructure, with role-based access so a sales executive sees their own leads rather than the whole database — which also addresses the practical problem of executives taking lead lists when they leave. Consent for calling and messaging is recorded per lead with its source and timestamp, which is what makes the outbound activity defensible under TRAI and DPDP requirements.
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Next step
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