4. Booking-to-Registration Autopilot
From booking form to KYC, agreement, stamp duty and registration — documents generated from the CRM record, missing papers chased conversationally, and every milestone visible to buyer, banker and legal. Target: Booking-to-agreement cycle halved, demand-note collections 10 days faster.
Business impact
Days from booking to registration
Build effort
Internal platform
Projected lift
Days from booking to registration
Time to value
From kickoff
Interactions / month
Agent-handled
Automation rate
No human in loop
Net value / year
Revenue + cost avoided − run cost
Payback
Cumulative net positive
Adoption curve
Agent-handled volume vs pre-agent baseline
Baseline (manual process)
Readiness scorecard
Six delivery dimensions
Outcome funnel
How days from booking to registration is produced
- Eligible interactions4,471
- Agent-handled3,67182%
- Qualified outcome1,82150%
- Days from booking to registration won79143%
Usage by geography
Cost mix
Share of run cost
- Inference35%
- Integration16%
- Data prep9%
- Change mgmt4%
Cohort retention
Share of users still using the product weekly
| W1 | W2 | W3 | W4 | W6 | W8 | |
|---|---|---|---|---|---|---|
| Cohort 1 | 105 | 96 | 87 | 78 | 67 | 61 |
| Cohort 2 | 107 | 94 | 86 | 72 | 61 | 55 |
| Cohort 3 | 102 | 96 | 83 | 75 | 60 | 48 |
| Cohort 4 | 100 | 97 | 87 | 78 | 68 | 61 |
| Cohort 5 | 101 | 91 | 81 | 75 | 63 | 57 |
Performance by geography
Indexed 0–100 across five signals
| Volume | Accuracy | Deflection | CSAT | Revenue | |
|---|---|---|---|---|---|
| Ahmedabad | 88 | 78 | 88 | 61 | 55 |
| Bengaluru | 95 | 83 | 44 | 38 | 49 |
| Pune | 86 | 75 | 59 | 91 | 76 |
| Surat | 52 | 57 | 54 | 38 | 77 |
Model quality
Live vs target
| Metric | Current | Target | 30-day | Status |
|---|---|---|---|---|
| Precision | 89 | 90 | +3.1 | Closing gap |
| Recall | 64 | 85 | +4.4 | Closing gap |
| Grounded-answer rate | 82 | 95 | +1.8 | Closing gap |
| Escalation rate | 6 | 6 | -2.2 | On target |
| P95 latency (s) | 1 | 3 | -0.4 | Closing gap |
| Hallucination flags / 1k | 1 | 1 | -0.9 | On target |
Service levels
82%
Impact score
60%
Delivery ease
93%
Uptime (30 days)
Unit economics
₹ per year, indexed to pilot scale
- Revenue influenced₹3.5Cr
- Cost avoided₹-6L
- Run cost₹15L
- Net value₹-22L
Cumulative ROI by quarter
Channel mix
- Voice150%
- WhatsApp-71%
- Web-71%
- Field app114%
- Email-21%
Language mix
- English2%
- Hindi-10%
- Gujarati-1%
- Kannada-12%
- Marathi-17%
Segment performance
Volume, automation and win rate per market
| Market | Interactions | Automation | Days from booking to registration | Value | Trend |
|---|---|---|---|---|---|
| Ahmedabad | -125 | 32% | -2% | ₹-56L | t0t1t2t3t4t5t6t7 |
| Bengaluru | 137 | 23% | -6% | ₹-18L | t0t1t2t3t4t5t6t7 |
| Pune | 268 | 19% | -8% | ₹1L | t0t1t2t3t4t5t6t7 |
| Surat | -116 | 17% | -9% | ₹10L | t0t1t2t3t4t5t6t7 |
| NRI - GCC | 142 | 36% | 4% | ₹-25L | t0t1t2t3t4t5t6t7 |
Experiment log
What we tested on this product
| Experiment | Status | Result | Owner |
|---|---|---|---|
| Prompt v3 vs v2 | Won | +10% days from booking to registration | Aiera Labs |
| Voice-first vs chat-first | Running | +0% so far | Growth Pod |
| Retrieval window 8k → 32k | Won | -0% escalations | Platform |
| Human review threshold 0.7 | Rolled back | no lift | Ops |
| Vernacular tone pack | Running | +2 CSAT pts | CX |
Risk register
Owned mitigations before scale
| Risk | Severity | Mitigation | Owner |
|---|---|---|---|
| Data freshness in source systems | Medium | Hourly sync + staleness alerts | Data Eng |
| Regulatory language in outputs | High | RERA disclosure | Compliance |
| Field-team adoption | Medium | In-app nudges + weekly coaching drills | Sales Ops |
| Model cost drift | Low | Router to smaller models for routine intents | Platform |
Where it sits in the portfolio
Tap another dot to switch product
Systems it touches
- Live
DigiLocker / KYC APIs
PAN, Aadhaar and address verification
- In build
eSign & eStamp providers
Agreement execution and stamp duty
- Live
SAP receivables
Demand notes, receipts and interest on delay
- Planned
Lender portals
Disbursement status per booked unit
Product brief
Who uses it
CRM team, buyers, legal, bankers
Metric it moves
Days from booking to registration
Why Arvind wins
Cuts the cash-collection cycle, not just paperwork
Guardrails
Applied to every response
- RERA disclosure
- No price commitment on call
- DND scrubbing
- Call recording consent
Delivery plan
14-week path to production
Weeks 1–3
Ground the data
Connect 3 product sets, historical conversations and CRM outcomes.
Weeks 4–6
Pilot with one team
Run in Ahmedabad with human review on every output.
Weeks 7–10
Automate the loop
Hand days from booking to registration decisions to the agent with escalation thresholds.
Weeks 11–14
Scale across lines
Roll out to 5 languages and all geographies.
How it works
End-to-end, from signal to learned outcome
01
Capture the signal
Every Voice and WhatsApp interaction, plus CRM and order events, is streamed in and tagged to a customer, Plotted development and geography.
02
Ground the context
Document generation indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.
03
Reason and decide
OCR + KYC validation scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.
04
Act in the workflow
eSign and payment orchestration writes back into CRM/ERP and fires the customer-facing action in English or Hindi, so the team sees it where they already work.
05
Learn from outcome
Won/lost and days from booking to registration outcomes flow back nightly; prompts, thresholds and routing are retuned every fortnight.
Data it runs on
Sources, contents and refresh cadence
- Streaming
Conversation history
Voice, WhatsApp, Portal leads, Channel partners, Walk-in transcripts with intent, objection and sentiment labels.
- Hourly
Catalogue & pricing
Plotted development · Villas & row houses · Apartments with specs, availability and approved commercial language.
- 15 min
CRM outcomes
Stage moves, win/loss reasons and owner activity per enquiry.
- Hourly
Operations systems
ERP order, stock, dispatch and service records used to answer status questions.
- Daily
Market signals
Demand and competitor movement across 5 geographies via DeployOneSense.
Customer & team journey
1Trigger
CRM team, buyers, legal, bankers hits the moment this product exists for — booking-to-agreement cycle halved, demand-note collections 10 days faster
2Agent acts
Aiera responds within seconds on Voice, grounded in the live catalogue and the customer's own history.
3Human in the loop
Anything below the confidence threshold, or touching RERA disclosure, routes to a named owner with full context attached.
4Close the loop
Outcome is written back, the customer gets a summary on WhatsApp, and days from booking to registration is attributed to this product.
Metric tree
What we steer and what we protect
North star
Days from booking to registration
Drivers
- Agent-handled share of eligible interactions
- First-response time and resolution time
- Qualified outcomes per 100 conversations in Ahmedabad
- Repeat engagement within 30 days
Guardrail metrics
- Grounded-answer rate above 95%
- Escalation rate inside agreed band
- RERA disclosure
- Run cost per conversation below target
Squad to build it
Who is needed and for what
- 0.3 FTE
Product owner (Arvind)
Scope, adoption and the business case
- 1 FTE
Conversation designer
Prompts, tone and 5-language scripts
- 1 FTE
Data / integration engineer
CRM, ERP and telephony wiring
- 0.5 FTE
ML engineer
Scoring models, evaluation harness, drift
- 0.2 FTE each
Ops champion per market
Floor adoption across 5 geographies
Commercials
How the product pays for itself
- Delivery model
- Internal platform
- Cost drivers
- Inference volume, telephony minutes, integration upkeep
- Charge basis
- Per handled conversation
- Break-even
- 5 months at pilot volume
- Scale unit
- One geography × one Plotted development
Rollout waves
Scope and the exit test for each wave
Wave 1 — Prove
Ahmedabad, Plotted development, human review on every output.
Exit test: Days from booking to registration beats manual baseline on 200+ conversations.
Wave 2 — Automate
Add Bengaluru and Hindi; agent acts without review above threshold.
Exit test: Escalation rate under 10% for four straight weeks.
Wave 3 — Extend
All 3 product sets and 5 channels, wired into CRM reporting.
Exit test: Adoption above 70% of eligible interactions.
Wave 4 — Compound
Roll across 5 geographies and share the model with sibling Arvind businesses.
Exit test: Net value positive at run-rate; owned by business-as-usual team.
Dependencies to unblock
Needed before wave 1 starts
- API access to CRM and ERP for Arvind SmartSpaces
- Approved language pack for English, Hindi, Gujarati, Kannada, Marathi
- Telephony numbers and consent records for outbound
- Sign-off on RERA disclosure
- Named market champions for floor adoption
Open questions for the business
Answer these to lock the scope
- Which Ahmedabad team runs the pilot and who signs off on outcomes?
- How far back does usable conversation history go for Plotted development?
- What confidence threshold is acceptable before the agent acts unattended?
- Which existing report does days from booking to registration get compared against?