IndiaAirline — full serviceScale · $100K
Air India

Vihaan.AI rebuilt the fleet and the brand — the contact experience is the last legacy system passengers still touch

Air India is spending billions to be judged as a premium global carrier; an agentic service layer across disruption, refunds, and loyalty gives merged Vistara-era passengers consistent premium communication faster than legacy contact-center re-platforming ever could.

Entry use case
Disruption communication & rebooking for the merged network
Expected outcome
Deliver proactive, consistent rebooking and refund conversations across the merged Air India-Vistara network, converting the most complained-about journeys into evidence of the transformation.
Recommended next step
Executive briefing with the Air India digital transformation office: position disruption communication as a Vihaan.AI quick win, then scope a two-route international pilot.
What we understand

Air India's operating reality

Tata took Air India private in 2022 and runs the Vihaan.AI transformation; Vistara merged into Air India in November 2024, and Air India Express consolidated AIX Connect.

Public fact

Air India placed one of aviation's largest aircraft orders (470 firm plus follow-ons) and is retrofitting legacy widebodies to support premium international repositioning.

Public fact

Service consistency is the publicly acknowledged gap: legacy-fleet product, merged workforces, and integration friction generate elevated complaint volumes, under DGCA passenger-rights scrutiny.

Public fact

Merging Vistara's premium-trained expectations base into Air India makes ex-Vistara flyers the most churn-sensitive cohort — they measure the merged airline against Vistara service memories.

Reasoned inference

Refund and baggage-claim backlogs from the merger transition likely dominate repeat-contact volume and DGCA-visible complaint statistics.

Seller hypothesis — validate

Validate with the account team before outreach: Post-merger PSS/loyalty stack status and integration timeline · Existing technology-partner commitments in the transformation program (Air India has publicly worked with multiple large vendors) · Complaint-volume breakdown by category (refunds vs. baggage vs. disruption)

Build vs buy

Why we have a right to win here

Partner-led target

Technically capable but needs industry workflows, integration, acceleration, or managed operations

Evidence: Air India publicly deployed AI.g — the airline industry's first generative-AI virtual agent, built on Azure OpenAI with Microsoft — which shows the transformation buys and partners for platforms rather than building AI in-house. Vihaan.AI is explicitly a best-of-breed procurement program across the stack.

Why they won't build the full stack: The transformation office optimizes for speed and visible service improvement, not technology ownership; engineering attention is consumed by merging two airlines' systems. Extending partnered AI from chat into voice, proactive disruption, and case-truth workflows is the natural next buy.

What management is signalling

The Vihaan.AI transformation continues with a $70B+ fleet order, widebody retrofits, and sustained Tata investment in premium repositioning.

Reported factPublic transformation disclosures · 2022-2026

GTM implication: Position disruption and refund communication as the fastest passenger-visible proof of the transformation — a rounding error against fleet spend with outsized brand ROI.

Merger-transition friction (refunds, baggage, service consistency) remains the loudest complaint category under DGCA scrutiny.

InferenceRecent public reporting (validate) · 2025-2026

GTM implication: Lead with refund/baggage case-truth workflows — the regulator-visible metric the transformation office is judged on.

What already exists (don't pitch this)
  • AI.g GenAI virtual agent on web and WhatsApp handling high query volumes
  • Rebuilt app and digital channels under the transformation
  • Merged Air India-Vistara contact centers
What customers still can't do end-to-end (pitch this)
  • →Proactive disruption communication at international-network scale
  • →Voice-channel automation beyond the chat assistant
  • →Live refund and baggage case truth across merged legacy systems
  • →Loyalty-context recognition for the merged Maharaja Club base
Opportunity map

Where agentic communications pays off first

WorkflowWhy it matters hereValueComplexitySpeedChannels
Merged-network disruption & rebooking
One consistent, proactive disruption conversation across the merged network, 24/7 and multilingual.
International long-haul disruptions strand high-value passengers across timezones; the transformation is judged in these moments.
Friction today: Contact centers span legacy stacks and merged teams; policy application inconsistent between ex-Vistara and legacy channels.
AppWhatsAppVoiceEmail
Refund & baggage-claim resolution
Live case truth with proactive updates; complaint escalations and DGCA-visible grievances reduced.
Refund delays and baggage claims are the loudest complaint categories in a merger transition.
Friction today: Cases span merged reservation systems and airport handlers; status opaque for weeks.
WhatsAppVoiceEmail
Maharaja Club loyalty service
Recognized-member service with points/tier grounding; premium cohort retention protected.
Merged loyalty (Vistara CV points into Maharaja Club) created a wave of points, tier, and credit queries among the most valuable flyers.
Friction today: Loyalty service queues behind general service; tier-mismatch cases need manual review.
VoiceWhatsAppApp
Watch the change

Disruption communication & rebooking for the merged network: today vs the agentic model

Scenario: A Delhi-London widebody goes technical overnight and 280 passengers — a third of them ex-Vistara loyalists — need rebooking, hotels, and answers in the next two hours
Today
same interaction, two worlds
Agentic layer
Disruption call spike
10× volume, melted queues
suppressed by proactive rebooking
Assumption
Cost per contact
$13.50 median assisted
$1.84 median self-service
Benchmark
QA coverage
1–2% sampled
100% scored
Platform capability
Sources & assumptions
  • · Disruption call spike: Operator-reported pattern (assumption); suppression measured in pilot
  • · 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
AppWhatsAppVoiceEmail

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 · Air India
PSSOps systemsRebooking engineBaggage systems (WorldTracer)PaymentsLoyalty platformCRM

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

Trust & languages
EnglishHindiPunjabiGujaratiMalayalamBengali

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 interactions2.5M
Seller assumption — replace in discovery
Current cost per interaction ($)$1.5
Industry benchmark scale — validate
Automation / assistance rate (%)55%
Seller assumption — pilot proves this
$3.8M
Current operating cost / mo
$19.0M
Modelled gross benefit / yr
0.1 mo
Payback on Scale
326%
3-yr ROI (modelled)
Automated/assisted interactions per month1.4M
Modelled AI run-cost per month (usage + cloud, system estimate)$481K
New monthly operating cost$2.2M

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 Air India's measured baseline. Package price covers implementation only; recurring usage billed separately.

Recommended package

Scale — $100K implementation

Scale · $100K · A multi-channel production deployment10–14 weeks to production across priority workflows

Why this package for Air India: Disruption plus refund/baggage workflows across the merged network is a Scale-grade deployment; group-wide Transform scope belongs after the merged PSS and loyalty stack stabilizes.

Included
  • 4–6 channels
  • 2–3 priority workflows
  • Multiple enterprise integrations
  • API credential & security setup
  • Advanced orchestration
  • Multilingual support
  • Agent Assist / human escalation
  • Production analytics
  • Expansion roadmap
Not included
  • ✕Usage & consumption (billed separately)
  • ✕Enterprise-wide governance build-out
  • ✕Multi-BU rollout
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: PSS, Ops systems, Rebooking engine
  • · A named business owner for disruption communication & rebooking for the merged network
  • · Security review counterpart and policy sign-off (PSS scope)
  • · Baseline metrics for the pilot's success thresholds
Executive messages

What to say to whom

CEO

“Vihaan.AI's thesis is that Air India can be world-class again. Passengers experience that claim mostly through disruption handling — make it the fastest-visible proof of the transformation.”

CIO / CTO

“You're mid-way through merging two airlines' systems. An agent layer grounded on the surviving PSS gives consistent front-end service now, decoupled from back-end integration timelines.”

COO

“Merged teams apply policy differently by legacy habit. An agentic layer applies one policy matrix across every channel and language, with humans handling judgment cases with full context.”

Chief Customer Experience Officer

“Ex-Vistara flyers are grading you against their memories. Proactive, recognized, premium-tone communication at scale is how the merged brand keeps them through the retrofit years.”

Chief Risk / Compliance Officer

“DGCA refund timelines and passenger-rights obligations become encoded policy with complete audit trails — turning your most regulator-visible complaint categories into managed processes.”

CFO / Procurement

“Against a $70B fleet spend, the cost of fixing communication is a rounding error with outsized brand ROI. Price it on complaint-volume reduction and premium-cohort retention.”

Outreach

Pre-built offer emails for Air India

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 1 — immediate strategic pursuit

Why this tier

Tata's flagship transformation: Vihaan.AI turnaround, Vistara merged in Nov 2024, a $70B+ fleet order, and premium international repositioning — service experience is the explicit battleground, and legacy service debt is the explicit gap.

30 / 60 / 90-day plan
  • Day 0–30: Executive briefing with the Air India digital transformation office: position disruption communication as a Vihaan.AI quick win, then scope a two-route international pilot.; confirm sponsor and baseline data access; validate: Post-merger PSS/loyalty stack status and integration timeline
  • Day 31–60: architecture & security review with the platform team; Tilicho Labs scoping on App + WhatsApp; pilot scope signed
  • Day 61–90: Scale package kickoff; disruption communication & rebooking for the merged network pilot in build; success thresholds locked with the Chief Customer Experience Officer
Recommended next step

Executive briefing with the Air India digital transformation office: position disruption communication as a Vihaan.AI quick win, then scope a two-route international pilot.

Entry: Disruption communication & rebooking for the merged network · Scale package · 10–14 weeks to production across priority workflows. 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.