Case Study of AMBAK

Turning Home-Loan Leads Into Qualified Conversations.

Ambak partnered with Fundamento to automate lead engagement and qualification across home-loan campaigns, using AI Voice Agents to understand customer interest, eligibility and intent across different borrower and allottee cohorts.

Written By

Team Fundamento

Published

Helping Home-Loan Businesses Qualify Demand Across Complex Lead Pools

Ambak operates across home-loan distribution, including home loans, LAP and balance-transfer opportunities.

Its lead universe spans allottee schemes, generic lost leads and partner-led acquisition—requiring different scripts, eligibility logic and engagement approaches.

Not Every Connected Lead Is A Qualified Borrower.

Ambak was engaging multiple lead pools across different schemes, geographies and loan types.

The operational challenge was not simply connecting with leads. It was distinguishing genuine interest from ambiguity, understanding the reason for hesitation and moving qualified prospects towards login and sanction.

Campaign Customer Context
MHADA Maharashtra allottees
DDA Delhi NCR allottees
Generic Lost leads, fresh loans, LAP & balance transfer
Partner Loan Channel-partner acquisition
Partner Loan PST Partner-led acquisition
High ambiguous-response rates made qualification—not connectivity—the critical funnel challenge.

Signal OS™ Turned Lead Conversations Into Qualification Signals.

The deployment used campaign source, geography, language and conversation outcomes to distinguish connected leads from genuinely interested prospects.

Signal What It Revealed
Campaign Source DDA, MHADA, Generic or Partner
Geography Regional borrower context
Language Hindi, English or Marathi
Engagement Connected → Engaged
Interest Level High / Medium / Low
Disposition Interested / Not Interested / Ambiguous
Architecture Layer Deployment Capability
Customer Signals Campaign • Geography • Language • Engagement • Interest
Decision Intelligence Campaign-specific rules • Interest classification
Journey Intelligence DDA • MHADA • Generic • Partner campaigns
Execution Layer AI Voice Agent
Human Intelligence Fully automated; no escalation layer
Lead Loaded → AI Calls → Engagement Detected → Interest Classified → Qualified → Login / Sanction / Disbursal

Five Acquisition Campaigns. One Qualification Intelligence Layer.

Journey Focus After Fundamento Signal
DDA Delhi NCR allottee qualification Interest / eligibility
MHADA Maharashtra allottee qualification Strongest interested conversion
Generic Lost / fresh / LAP / BT leads Intent & qualification
Partner Loan Channel acquisition Interest & qualification
Partner Loan PST Partner acquisition Interest & qualification
Feedback Post-interest check-in Customer sentiment
Data → Connected → Engaged → Interested → Qualified → Login Filed → Sanction / Disbursal

One Voice Layer Adapted To Different Home-Loan Contexts.

Agent / Layer Role
Voice Agent Lead engagement and qualification
Campaign Logic Selects appropriate campaign treatment
Language Routing Supports Hindi, English and Marathi
Eligibility Logic Assesses borrower fit
Disposition Layer Classifies interest and next step

Qualification Was Designed To Run Autonomously.

Human escalation: Not part of the documented deployment.  The Voice Agent handled engagement, qualification, explanation and disposition end-to-end.

AI Handles Human Teams
Lead engagement Not part of documented deployment
Eligibility discussion Not part of documented deployment
Product explanation Not part of documented deployment
Interest classification Not part of documented deployment

Capabilities Are Not Projected. They Are Measured.

86–88%

Connectivity

62–64%

Engagement

17–21%

Interested Conversion

3

Languages Supported

Campaign After Fundamento Connectivity After Fundamento Interested
Generic 55% 4%
DDA 86–87% 3–14%
MHADA 86–88% 17–21%
Partner Loan 68% 5%
Partner Loan PST 71% 3%

From Lead Engagement To Cohort-Level Home-Lending Intelligence.

  • Scheme-level borrower cohorts
  • Language-based routing
  • Interest-level classification
  • Pricing and eligibility objection patterns
  • Campaign-level conversion intelligence
  • Ambiguous-response as a qualification signal
Started With Expanded Into
Generic lead engagement Scheme-specific cohorts
Lead calling Qualification intelligence
One-language workflows Multilingual engagement
Generic scripts Campaign-specific logic
Contactability Interest and intent intelligence