IndiaUtilities — power distribution & EV chargingLaunch · $50K
Tata Power

Tata Power serves 12 million meters — outage nights and bill-shock mornings are when they all call at once

A utility's contact demand is spiky (outages) and cyclical (billing); an agentic layer for outage status, bill explanation, and new-connection tracking absorbs the spikes in local languages, while solar and EV-charging arms get a sales-grade conversational funnel.

Entry use case
Outage status & billing-query resolution for distribution customers
Expected outcome
Deflect the outage-night call spike with proactive, area-grounded status updates and resolve bill-shock queries conversationally, cutting call-center load at the two most predictable demand peaks.
Recommended next step
Scope a pre-monsoon Launch pilot in the Mumbai discom on outage and billing workflows, with call-deflection baselines from last monsoon season.
What we understand

Tata Power's operating reality

Tata Power distributes electricity to over 12 million customers across Mumbai, Delhi (Tata Power-DDL), and Odisha discoms, alongside generation and renewables businesses.

Public fact

It operates one of India's largest public EV-charging networks (EZ Charge) and a leading rooftop-solar business — both consumer-facing growth lines with sales funnels.

Public fact

Smart-meter rollouts across its discoms are changing billing granularity and creating a new class of 'why is my bill different' customer queries.

Public fact

Outage events concentrate thousands of near-identical status calls into short windows — the classic case where proactive area-based updates suppress inbound volume.

Reasoned inference

Monsoon-season outage spikes in Mumbai and Odisha likely dominate annual contact-center stress, making a pre-monsoon pilot the natural timing hook.

Seller hypothesis — validate

Validate with the account team before outreach: OMS/billing API readiness per discom (Mumbai vs. Delhi vs. Odisha maturity differs) · Procurement route: discom-level vs. Tata Power group digital office · Regulatory constraints on proactive customer communication per state ERC

Build vs buy

Why we have a right to win here

Buy-led target

Limited internal ability or appetite to build the core platform — strong candidate for the packaged solution

Evidence: A regulated utility whose engineering is grid, generation, and OT-centric; customer-facing IT (billing/CIS, outage management) is bought from vendors under conservative procurement cycles. There is no internal AI organization aimed at customer experience, and discom cost scrutiny by regulators favors provable purchased solutions.

Why they won't build the full stack: Utility engineering priorities are smart-meter rollout, network reliability, and renewables — building conversational AI would be an off-mission use of regulated-entity budgets that ERCs scrutinize. A contained, benchmarked purchase fits both the procurement culture and the regulatory optics.

What management is signalling

Continued emphasis on renewables growth, smart-meter rollout across discoms, and EV-charging network expansion.

InferenceRecent investor communications (validate) · FY25-FY26

GTM implication: Smart meters are changing bills faster than customer understanding — bill-explanation automation rides an already-funded program and reduces regulator-visible complaints.

What already exists (don't pitch this)
  • Tata Power / discom apps and web self-service
  • Discom IVRs and call centers
  • One-way outage SMS alerts
  • EZ Charge app for the EV-charging network
What customers still can't do end-to-end (pitch this)
  • →Proactive area-grounded outage status that suppresses call spikes
  • →Personalized bill explanation from smart-meter consumption data
  • →Vernacular voice (Marathi, Odia) at outage-night scale
  • →Consultative lead handling for rooftop-solar and EZ Charge funnels
Opportunity map

Where agentic communications pays off first

WorkflowWhy it matters hereValueComplexitySpeedChannels
Outage status & proactive restoration updates
Area-grounded status and restoration ETAs pushed proactively; spike volume suppressed.
Outage calls are the utility's defining spike; every proactive update is a call that never happens.
Friction today: Customers call repeatedly for the same area-level answer; IVR gives generic messages.
WhatsAppSMSVoice
Bill explanation & payment
Personalized bill explanation from consumption data with in-channel payment; complaint volume down.
Bill-shock queries cluster after billing cycles and smart-meter transitions; unresolved ones become regulator complaints.
Friction today: Agents read tariff tables; customers want their bill explained, not tariff policy.
WhatsAppVoice
Rooftop-solar & EZ Charge lead funnel
Qualified solar/charging leads with subsidy-scheme grounding, surveys booked for field teams.
Solar and EV-charging are growth businesses needing consultative lead handling, not utility service queues.
Friction today: Subsidy, net-metering, and installation questions overwhelm generic call scripts.
WhatsAppWeb chatVoice
Watch the change

Outage status & billing-query resolution for distribution customers: today vs the agentic model

Scenario: A monsoon night outage in suburban Mumbai triggers thousands of identical calls while the restoration crew's ETA sits unshared in the outage-management system
Today
same interaction, two worlds
Agentic layer
Languages served
2–3 staffed
10+ in one deployment
Platform capability
Cost per contact
$13.50 median assisted
$1.84 median self-service
Benchmark
QA coverage
1–2% sampled
100% scored
Platform capability
Sources & assumptions
  • · Languages served: Platform capability: Gemini + Chirp speech
  • · Cost per contact: Gartner customer service cost benchmarks, 2024
  • · QA coverage: Platform capability: every interaction logged and evaluated
  • · Gartner, customer service cost benchmarks (2024): $13.50 median assisted vs $1.84 self-service per contact
  • · McKinsey, digital-first collections research: 20–25% NPL reduction among leaders; up to 40% opex reduction with gen AI
  • · Baymard Institute: ~70% average cart abandonment (meta-analysis)
  • · IAMAI–Kantar via IBEF (2025): 900M+ Indian internet users; 98% consume Indic-language content
  • · LeadSquared and vendor funnel studies: 78% of students choose the first institution to respond (directional, vendor data)
  • · HDI / ITSM operator benchmarks: $15–25 per L1 ticket; 40–60% of L1 volume is resets/status (validate per customer)
  • · Conventional-flow wait times and volumes are typical operator patterns — assumptions to replace with the customer's own baseline
  • · Agentic-flow behaviors (context retention, 100% logging, in-line policy checks) are platform capabilities, not projections
Recommended solution

One integrated stack, opinionated for this account

Channels · Tilicho Labs
WhatsAppSMSVoiceWeb chat

Voice & channel orchestration, telephony, conversational execution, session/state, routing, integration build. Capability coverage validated during implementation.

Intelligence · Google Cloud
Gemini reasoningEnterprise groundingWorkflow agentsMulti-agent orchestrationGoverned actionsEvaluation & analytics
Systems · Tata Power
Outage-management systemGIS/area mappingBilling/CISSmart-meter dataPayment gatewayCRMScheme/subsidy databases

API access to these systems is the critical-path dependency.

Trust & languages
HindiEnglishMarathiOdia

Identity-bound sessions, policy-bounded actions, 100% audit logging, human approvals at defined points, in-tenant intelligence.

Business case

The economics, with your numbers

Addressable monthly interactions1.5M
Seller assumption — replace in discovery
Current cost per interaction ($)$0.6
Industry benchmark scale — validate
Automation / assistance rate (%)55%
Seller assumption — pilot proves this
$900K
Current operating cost / mo
$2.5M
Modelled gross benefit / yr
0.2 mo
Payback on Launch
71%
3-yr ROI (modelled)
Automated/assisted interactions per month825K
Modelled AI run-cost per month (usage + cloud, system estimate)$289K
New monthly operating cost$694K

All figures are modelling estimates from the labeled inputs above — nothing here is customer-provided yet. The pilot's first job is replacing these assumptions with Tata Power's measured baseline. Package price covers implementation only; recurring usage billed separately.

Recommended package

Launch — $50K implementation

Launch · $50K · A focused, fast production pilot8–10 weeks to a live, measured pilot

Why this package for Tata Power: Utility procurement rewards a contained, provable pilot: one discom geography, outage plus billing workflows, clear deflection metrics — Launch scope, then group-wide expansion across discoms and the solar/EV arms.

Included
  • Up to 3 channels
  • One priority workflow
  • Limited enterprise integrations (1–2 systems)
  • API credential & security setup
  • Core conversational + workflow configuration
  • Basic analytics
  • Controlled production pilot with defined success criteria
Not included
  • ✕Usage & consumption (billed separately)
  • ✕Additional workflows
  • ✕Multi-geography rollout
  • ✕Managed operations
Recurring costs (separate from the package)

Packages cover implementation and integration only. Recurring costs are billed separately: Tilicho Labs platform usage (~$0.15/call-min indicative, usage only), Google Cloud consumption, telephony/carrier charges, managed operations, and support & optimization. No package includes unlimited usage.

Customer resources required
  • · API access + credentials for: Outage-management system, GIS/area mapping, Billing/CIS
  • · A named business owner for outage status & billing-query resolution for distribution customers
  • · Security review counterpart and policy sign-off (Outage-management system scope)
  • · Baseline metrics for the pilot's success thresholds
Executive messages

What to say to whom

CEO

“Tata Power's story spans discoms, solar, and EV charging — one conversational layer serves all three, turning a cost-center pilot into group-wide customer infrastructure.”

CIO / CTO

“Integration is OMS, billing, and payment APIs in one discom geography — a contained Launch pilot with utility-grade logging, deployable before the next monsoon season.”

COO

“Your call centers are sized for outage nights that happen twelve times a year. Proactive area-grounded updates flatten that peak permanently.”

Chief Customer Officer (Distribution)

“Smart meters changed bills faster than customer understanding. Conversational bill explanation in Marathi, Hindi, and Odia is how you keep trust — and regulator complaint counts — in check.”

Chief Risk / Compliance Officer

“Regulator-visible service standards (restoration communication, complaint SLAs) get audit-complete conversation records; DPDP-aligned consent governs all proactive outreach.”

CFO / Procurement

“A Launch-priced pilot with deflection metrics on your two most predictable peaks — outage nights and billing weeks — gives procurement a clean, benchmarked expansion decision.”

Outreach

Pre-built offer emails for Tata Power

Written from this account's own research — the strategic signal, the capability gap, the entry workflow, and the modelled economics — not a mail-merge template. Pick the moment and the persona, edit anything, then copy or open in your mail client.

Moment in the deal
Cold outreach — no prior conversation
Who it's addressed to
Cares about: The workflow itself and its daily failure modes
Register
Length
Draft — edit freely before sending
Open in mail client

Customer-safe by construction: drafts are composed only from customer-facing fields. Account tier, build-vs-buy classification, priority score, internal routing, and partner-commercial detail are not inputs to the composer, so they cannot appear in a draft. Money figures are always framed as modelled from the customer's own volumes. Read before sending — you own what goes out.

The pursuit

Tier 3 — longer-term / partner-led

Why this tier

Distribution utility serving ~12M+ customers (Mumbai, Delhi, Odisha) with growing rooftop-solar and EV-charging businesses — high-volume, low-complexity billing and outage conversations, but conservative utility procurement cycles temper deal speed.

Recommended next step

Scope a pre-monsoon Launch pilot in the Mumbai discom on outage and billing workflows, with call-deflection baselines from last monsoon season.

Entry: Outage status & billing-query resolution for distribution customers · Launch package · 8–10 weeks to a live, measured pilot. Human fallback throughout; success thresholds agreed before build.

Start the pursuit

Research-based priority-account universe assembled from public information, market scale, communication volume, and solution fit. This is NOT an authoritative list of top Google Cloud customers; existing Google Cloud relationships are noted only where publicly reported. Validate every account with the account team before outreach.