2. Corporate Leasing Copilot

Reads the tenant RFP, models the fit-out and CAM budget, prices options live on the negotiation call and drafts the LOI the same day. Target: RFP response in 24 hours, deal cycle two weeks shorter.

Quick winPilotNow

Business impact

85/100

RFP turnaround

Build effort

36/100

Internal platform

Projected lift

+21%vs pre-agent baseline

RFP turnaround

Time to value

≤ 90 days

From kickoff

Interactions / month

4,032+10%

Agent-handled

Automation rate

85%-6% human touches

No human in loop

Net value / year

₹4L

Revenue + cost avoided − run cost

Payback

4 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul96 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How rfp turnaround is produced

  • Eligible interactions4,497
  • Agent-handled3,29773%
  • Qualified outcome1,64750%
  • RFP turnaround won75746%

Usage by geography

-99
10
65
92
-24
AhmedabadGandhinagarBengaluruPuneNRI - USA/GCC

Cost mix

Share of run cost

36
  • Inference43%
  • Integration18%
  • Data prep15%
  • Change mgmt5%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11089482766961
Cohort 21009385756451
Cohort 31059585726048
Cohort 41079688766657
Cohort 51079384766854

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
Ahmedabad4459458254
Gandhinagar9340978854
Bengaluru6845994191
Pune4492967068

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision9190+3.1On target
Recall7185+4.4Closing gap
Grounded-answer rate8895+1.8Closing gap
Escalation rate86-2.2On target
P95 latency (s)23-0.4Closing gap
Hallucination flags / 1k-21-0.9Closing gap

Service levels

85%

Impact score

64%

Delivery ease

95%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹3.2Cr
  • Cost avoided₹42L
  • Run cost₹24L
  • Net value₹4L

Cumulative ROI by quarter

Q186% peakQ4

Channel mix

  • Voice32%
  • WhatsApp14%
  • Web9%
  • Field app-32%
  • Email77%

Language mix

5
  • English-10%
  • Hindi-16%
  • Gujarati-4%
  • Kannada-13%
  • Marathi-3%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationRFP turnaroundValueTrend
Ahmedabad-99
25%
3%₹12L
t0t1t2t3t4t5t6t7
Gandhinagar150
40%
10%₹16L
t0t1t2t3t4t5t6t7
Bengaluru-175
47%
14%₹18L
t0t1t2t3t4t5t6t7
Pune112
51%
16%₹-21L
t0t1t2t3t4t5t6t7
NRI - USA/GCC256
53%
17%₹-1L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+7% rfp turnaroundAiera Labs
Voice-first vs chat-firstRunning+2% so farGrowth Pod
Retrieval window 8k → 32kWon--1% escalationsPlatform
Human review threshold 0.7Rolled backno liftOps
Vernacular tone packRunning+1 CSAT ptsCX

Risk register

Owned mitigations before scale

RiskSeverityMitigationOwner
Data freshness in source systemsMediumHourly sync + staleness alertsData Eng
Regulatory language in outputsHighRERA registration numbersCompliance
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

  • Cost library / ERP

    Live fit-out rates, contractor quotes and CAM history

    Live

  • Salesforce CRM

    Pursuit stages, competitor set and approval workflow

    Live

  • DocuSign

    LOI and lease execution

    In build

Product brief

Who uses it

Corporate tenants, leasing team

Metric it moves

RFP turnaround

Why Arvind wins

Speed wins shortlists — a day instead of a week

RFP parsingParametric fit-out estimatorLOI generation

Guardrails

Applied to every response

  • RERA registration numbers
  • No assured-return claims
  • Broker attribution rules
  • 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 rfp turnaround 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, Residential and geography.

02

Ground the context

RFP parsing indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.

03

Reason and decide

Parametric fit-out estimator scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.

04

Act in the workflow

LOI generation 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 rfp turnaround 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, Broker network, Portals, Site walk-in transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Residential · Commercial · Services 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

    Corporate tenants, leasing team hits the moment this product exists for — rfp response in 24 hours, deal cycle two weeks shorter

  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 registration numbers, 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 rfp turnaround is attributed to this product.

Metric tree

What we steer and what we protect

North star

RFP turnaround

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 registration numbers
  • 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

    0.3 FTE

  • Conversation designer

    Prompts, tone and 5-language scripts

    1 FTE

  • Data / integration engineer

    CRM, ERP and telephony wiring

    1 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 handled conversation
Break-even
4 months at pilot volume
Scale unit
One geography × one Residential

Rollout waves

Scope and the exit test for each wave

Wave 1 — Prove

Ahmedabad, Residential, human review on every output.

Exit test: RFP turnaround beats manual baseline on 200+ conversations.

Wave 2 — Automate

Add Gandhinagar 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 Real Estate
  • Approved language pack for English, Hindi, Gujarati, Kannada, Marathi
  • Telephony numbers and consent records for outbound
  • Sign-off on RERA registration numbers
  • 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 Residential?
  • What confidence threshold is acceptable before the agent acts unattended?
  • Which existing report does rfp turnaround get compared against?
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