1. Launch & Absorption Intelligence

One live model of every tower: inventory, absorption velocity, discount leakage, construction spend and collections, re-priced weekly so release sequencing and unit pricing are decisions backed by cashflow, not committee memory. Target: 2-4% higher realisation at the same absorption pace, discount leakage visible weekly.

Core buildPrototypeNow

Business impact

93/100

Realisation per sq ft

Build effort

56/100

Internal platform

Projected lift

+23%vs pre-agent baseline

Realisation per sq ft

Time to value

≤ 90 days

From kickoff

Interactions / month

4,158+15%

Agent-handled

Automation rate

84%-5% human touches

No human in loop

Net value / year

₹1.5Cr

Revenue + cost avoided − run cost

Payback

6 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul99 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How realisation per sq ft is produced

  • Eligible interactions4,916
  • Agent-handled3,31667%
  • Qualified outcome1,76653%
  • Realisation per sq ft won67638%

Usage by geography

256
318
219
299
209
AhmedabadBengaluruPuneSuratNRI - GCC

Cost mix

Share of run cost

56
  • Inference48%
  • Integration31%
  • Data prep19%
  • Change mgmt13%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11019283796761
Cohort 21039782786256
Cohort 31089781705752
Cohort 41029388796756
Cohort 51019083746256

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
Ahmedabad4775935651
Bengaluru7652617376
Pune9676796083
Surat6466928543

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision9090+3.1On target
Recall9085+4.4On target
Grounded-answer rate9695+1.8On target
Escalation rate126-2.2On target
P95 latency (s)43-0.4On target
Hallucination flags / 1k21-0.9On target

Service levels

93%

Impact score

44%

Delivery ease

94%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹2.4Cr
  • Cost avoided₹1.5Cr
  • Run cost₹47L
  • Net value₹1.5Cr

Cumulative ROI by quarter

Q1127% peakQ4

Channel mix

  • Voice14%
  • WhatsApp23%
  • Web25%
  • Field app26%
  • Email12%

Language mix

5
  • English37%
  • Hindi37%
  • Gujarati22%
  • Kannada30%
  • Marathi19%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationRealisation per sq ftValueTrend
Ahmedabad1,116
93%
37%₹39L
t0t1t2t3t4t5t6t7
Bengaluru758
94%
41%₹29L
t0t1t2t3t4t5t6t7
Pune579
74%
43%₹64L
t0t1t2t3t4t5t6t7
Surat939
64%
30%₹42L
t0t1t2t3t4t5t6t7
NRI - GCC669
59%
24%₹31L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+12% realisation per sq ftAiera Labs
Voice-first vs chat-firstRunning+6% so farGrowth Pod
Retrieval window 8k → 32kWon-7% escalationsPlatform
Human review threshold 0.7Rolled backno liftOps
Vernacular tone packRunning+8 CSAT ptsCX

Risk register

Owned mitigations before scale

RiskSeverityMitigationOwner
Data freshness in source systemsMediumHourly sync + staleness alertsData Eng
Regulatory language in outputsHighRERA disclosureCompliance
Field-team adoptionMediumIn-app nudges + weekly coaching drillsSales Ops
Model cost driftLowRouter to smaller models for routine intentsPlatform

Where it sits in the portfolio

Tap another dot to switch product

Quick winsBig bets
Impact ↑Effort →

Systems it touches

  • SAP RE-FX / ERP

    Unit master, cost-to-complete, collections and receivables

    Live

  • Salesforce CRM

    Enquiry, hold, booking and cancellation events

    Live

  • Primavera / MS Project

    Construction schedule and milestone slippage

    In build

  • DeployOneSense market feed

    Competitor launch pricing and micro-market absorption

    Live

Product brief

Who uses it

CEO, sales strategy, project finance

Metric it moves

Realisation per sq ft

Why Arvind wins

Ten years of Arvind township transactions joined to actual construction spend — no portal can rebuild it

Absorption + price elasticity modelsCashflow and IRR engineScenario simulator

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 realisation per sq ft 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

Absorption + price elasticity models indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.

03

Reason and decide

Cashflow and IRR engine scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.

04

Act in the workflow

Scenario simulator 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 realisation per sq ft outcomes flow back nightly; prompts, thresholds and routing are retuned every fortnight.

Data it runs on

Sources, contents and refresh cadence

  • Conversation history

    Voice, WhatsApp, Portal leads, Channel partners, Walk-in transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Plotted development · Villas & row houses · Apartments with specs, availability and approved commercial language.

    Hourly

  • CRM outcomes

    Stage moves, win/loss reasons and owner activity per enquiry.

    15 min

  • Operations systems

    ERP order, stock, dispatch and service records used to answer status questions.

    Hourly

  • Market signals

    Demand and competitor movement across 5 geographies via DeployOneSense.

    Daily

Customer & team journey

  1. 1Trigger

    CEO, sales strategy, project finance hits the moment this product exists for — 2-4% higher realisation at the same absorption pace, discount leakage visible weekly

  2. 2Agent acts

    Aiera responds within seconds on Voice, grounded in the live catalogue and the customer's own history.

  3. 3Human in the loop

    Anything below the confidence threshold, or touching RERA disclosure, routes to a named owner with full context attached.

  4. 4Close the loop

    Outcome is written back, the customer gets a summary on WhatsApp, and realisation per sq ft is attributed to this product.

Metric tree

What we steer and what we protect

North star

Realisation per sq ft

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

  • Product owner (Arvind)

    Scope, adoption and the business case

    0.5 FTE

  • Conversation designer

    Prompts, tone and 5-language scripts

    1 FTE

  • Data / integration engineer

    CRM, ERP and telephony wiring

    2 FTE

  • ML engineer

    Scoring models, evaluation harness, drift

    0.5 FTE

  • Ops champion per market

    Floor adoption across 5 geographies

    0.2 FTE each

Commercials

How the product pays for itself

Delivery model
Internal platform
Cost drivers
Inference volume, telephony minutes, integration upkeep
Charge basis
Per market licence + usage
Break-even
4 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: Realisation per sq ft 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 realisation per sq ft get compared against?
← Back to portfolio