Gemini Enterprise Frontline · seller home

The agentic communications layer for the enterprise — one platform behind every inbound and outbound interaction

Not a contact-centre suite. Not a voice bot. The cross-enterprise intelligence and action layer for communications embedded in business workflows — collections, service, sales, employee and student support — delivered with partners, governed on Gemini Enterprise Agent Platform.

Lead when
communications are embedded in enterprise workflows spanning functions and systems of record
Involve GECX when
the centre of gravity is contact-centre modernization — queues, desktops, WFM, service ops
Tilicho Labs always for
the communications platform — telephony, channels, conversational execution, session/routing — and implementation
Strategic thesis

Why this play, why now

Category: Agentic Communications Layer

We are not selling a bot or a CC suite — we are creating the layer through which enterprises design, govern, deploy, and improve agentic interactions across every channel. Broader than conversational AI vendors, orthogonal to CCaaS, native to the Gemini Enterprise narrative.

The implementation gap is the moat

Models are table stakes. Telephony, channel integration, compliance, state, language handling, observability, and workflow integration are the hard 80% — Google provides the platform, partners industrialize the gap into repeatable vertical IP.

Regulated + multilingual is our wedge

In-tenant deployment (data, models, state, telemetry inside the customer's cloud) plus Indic/regional language quality is a combination point-solution vendors can't match and hyperscaler rivals haven't packaged.

Consumption compounds

Every contained interaction is model + speech + search + data consumption. One lighthouse use case ramps into an enterprise-wide agent platform commitment — track expansion, not just the first deal.

Opportunity sizing

The verified numbers behind the pitch

Every figure below is sourced and confidence-tagged. Use them verbatim; do not improvise market sizes in front of customers.

80%
of common customer-service issues will be resolved autonomously by agentic AI by 2029, cutting operational costs ~30%
Gartner press release, Mar 2025 · Verified
$80B
reduction in contact-centre agent labor costs from conversational AI in 2026
Gartner press release, Aug 2022 · Verified
$13.50 vs $1.84
median cost per assisted contact vs self-service contact — a 7× gap
Gartner customer service cost benchmarks, 2024 · Verified
$11.6B → $41.4B
conversational AI market, 2024 → 2030 (23.7% CAGR)
Grand View Research, 2025 · Verified
~$165B
combined global contact-centre software (~$48B) and outsourcing (~$117B) spend
Emergen Research + Market Data Forecast, 2025 (derived) · Estimate
900M+
Indian internet users in 2025; 98% consume content in Indic languages
IAMAI–Kantar via IBEF, Jan 2025 · Verified
up to 40%
opex reduction and ~10% recovery improvement from gen-AI-enabled collections
McKinsey digital-first collections research · Verified
$3.6B → $9.8B
OTT business messaging revenue (WhatsApp-led), 2025 → 2029
Juniper Research · Verified
Account qualifier

Score the account before you spend cycles on it

Eight weighted criteria. 70+ is a priority pursuit; under 45 goes back to nurture. Be honest — the pilot factory only has so many slots.

Monthly interaction volumeweight 20
Current cost pressure on interactionsweight 15
Executive sponsorshipweight 15
Multi-language needweight 10
Data-residency / in-tenant preferenceweight 10
Integration readiness (APIs to core systems)weight 10
Outsourcing / BPO dependenceweight 10
Use-case urgency (trigger event)weight 10
Opportunity score
—/ 100
Answer all criteria to score the account

GECX boundary

When to lead with what

Customer Engagement Suite (GECX) owns contact-centre modernization. We own the cross-enterprise agent layer. The tree and the capability map below are the rules of engagement — deviating from them creates channel conflict that kills both deals.

Decision tree
What is the customer's centre of gravity for this initiative?
Non-negotiables:never position Gemini Enterprise as a CC-stack replacement · never rebuild productized CC functions · escalations always land in the customer's existing CC platform · joint accounts get one plan with named swimlane owners.
Capability swimlanes
Contact-centre infrastructure (CCaaS)
Queues, ACD, agent seats, WFM ecosystem
GECX-led
Omnichannel routing & agent desktop
Human-agent workspace and routing rules
GECX-led
Workforce management & quality management
Scheduling, adherence, QM workflows
GECX-led
IVR / customer-service telephony
Carrier connectivity within the CC stack
GECX-led
CRM case management
Salesforce, ServiceNow, custom — integrated, never replaced
Customer-owned
Customer-service conversational agents (CC-centric)
GECX virtual agents in CC contexts; GE agents when part of broader workflows
Joint
Cross-enterprise multi-agent orchestration
Agents spanning sales, ops, finance, HR, collections
Gemini Enterprise-led
Enterprise workflow actions (act in ERP/core systems)
Governed tool-calling into systems of record
Gemini Enterprise-led
Employee-facing agents (HR/IT/finance)
Outside the contact-centre domain entirely
Gemini Enterprise-led
Outbound engagement & proactive notifications
Campaign-triggered agentic outreach beyond CC dialers
Gemini Enterprise-led
Agent governance, evaluation & observability
Policies, evals, audit across all agents
Gemini Enterprise-led
Enterprise search & grounding
Governed knowledge across the enterprise
Gemini Enterprise-led
Model & speech services (Gemini, STT/TTS)
Shared Google Cloud foundation consumed by both
Joint
Telephony & channel integration (non-CC)
SIP, BSP, WhatsApp, RCS wiring for agentic use cases
Partner-led
Industry agent packs & connectors
Repeatable vertical IP on the platform
Partner-led
Data platform & analytics (BigQuery et al.)
Interaction data lands in customer's warehouse
Joint
Business rules, offer matrices, policies
Customer defines; platform enforces
Customer-owned
Sales motion

Eleven stages, each with a gate

No stage skipping: the pilot only performs when discovery baselined real metrics and the customer signed off one use case.

1. Account selection
Score accounts with the qualifier; prioritize regulated, high-volume, multilingual
Gate: Score ≥ 60 or named strategic account
2. Executive hypothesis
One-page hypothesis: use case, value lever, sponsor. Vertical microsite as the opener
Gate: Sponsor meeting secured
3. Discovery
Discovery guide by persona; collect volumes, costs, languages, systems
Gate: Baseline metrics documented
4. Value assessment
ROI calculator with customer's own numbers; agree the formula, not the outcome
Gate: Customer accepts the model's assumptions
5. Use-case prioritization
Score use cases on value × feasibility × speed; pick ONE for pilot
Gate: Single lighthouse use case signed off
6. Architecture workshop
Joint session: reference architecture, integration boundaries, security blueprint; GECX decision-tree check
Gate: Architecture agreed; GECX lane assigned
7. Demo
Industry agent pack demo on customer-like data; partner presents delivery model
Gate: Technical validation complete
8. Pilot
8–12 week pilot per the pilot factory; weekly metric reviews
Gate: Success thresholds met
9. Production
Production-readiness checklist; support model; consumption ramp plan
Gate: Live traffic, agreed SLOs
10. Expansion
Next use cases from the vertical roadmap; quarterly value reviews
Gate: Second use case in flight
11. Standardization
Enterprise agent-platform agreement; CoE model; committed consumption
Gate: Multi-year platform commitment
Solution packages & industry plays

What you can actually sell today

Financial Services
Every customer conversation, inside your risk perimeter
Open customer microsite
Lighthouse / fastest / consumption
  • lighthouse: Early-bucket collections & payment reminders
  • consumption: Loan & credit-card servicing (balance, statements, disputes intake)
  • fastest: Lead qualification for loans, cards, insurance
  • executive: Fraud-related customer verification callbacks
  • partner: Relationship-manager assist
Discovery questions that open doors
  • · What share of early-bucket accounts get zero successful contacts in month one?
  • · What does a completed collector conversation cost you today, fully loaded?
  • · Which languages does your customer base speak vs your collector bench?
  • · Where must conversation data physically reside, and who audits it?
PackageBuyerTriggerHandoff modelTimelineCommercial
Collections AgentCollections / Risk leader (BFSI, Telecom, Education fees)Account enters DPD bucket (e.g., 1–30 days past due) or a promise-to-pay is broken.Disputes, hardship or vulnerability cues, and legal-stage accounts route to human collectors with full context.8–12 weeks to production pilot on one bucket and one language pairPlatform consumption + per-completed-interaction implementation fee; expand by bucket and language
Inbound Service AgentCustomer Service / Contact Centre leaderCustomer calls, messages, or emails on any serviced line or number.Sentiment, complexity, or policy triggers route to human agents inside the existing contact-centre stack.6–10 weeks for a top-5-intents production pilotConsumption-led; price per resolved interaction as maturity grows
Outbound Lead AgentSales / Growth / Admissions leaderNew lead from web form, campaign, marketplace, or walk-in registration.Hot leads transfer live or book meetings; complex product questions route to specialists.6–8 weeks on a single campaign sourcePer-qualified-lead framing on top of platform consumption
Employee & Student Support AgentCHRO, CIO, GCC site leader, University registrar / dean of studentsAny employee or student question via chat, voice, email, or portal.Sensitive HR matters, grievances, and judgment calls route to named human owners.6–10 weeks for two functions (e.g., HR + IT) in one geographyPer-seat platform economics; strong Gemini Enterprise seat-activation motion
Agent Assist & Conversation IntelligenceContact Centre / Quality / Operations leaderLive human-assisted interaction, or batch analysis of recorded interactions.Human agent stays in control throughout; the AI advises and documents.4–8 weeks; lowest-risk entry point because no customer-facing automationPer-monitored-seat or per-analyzed-interaction consumption
Delivery model — Tilicho Labs

Who owns what

Tilicho Labs is the communications and implementation layer: we leverage its existing platform and IP (one-time integration fee + usage-based consumption) instead of funding redevelopment of capabilities it already has. Gemini Enterprise Agent Platform provides intelligence, agents, and governance. Validate exact Tilicho capability coverage during implementation — never present unconfirmed capabilities as production-ready.

Google Cloud owns
  • Gemini intelligence & reasoning
  • Enterprise grounding & search
  • Domain & workflow agents, multi-agent orchestration
  • Enterprise-system actions & governance
  • Security, identity, data & analytics
  • Agent evaluation & observability
Tilicho Labs owns
  • Communications & voice-AI solution layer (existing platform + IP)
  • Telephony & channel integrations
  • API-based conversational execution
  • Session, state & routing orchestration
  • Implementation accelerators & customer-specific integration
  • Deployment support & managed optimization (usage-based platform pricing)
Customer owns
  • Enterprise data & systems of record
  • Business rules & regulatory decisions
  • Process ownership & change management
  • Human approvals & escalation operations
  • Success metrics & governance board
Pilot factory

The repeatable 8–12 week pilot

One narrowly-defined use case, baseline metrics before build, production-like traffic before verdict. Central pilot funding (e.g., ~$250K for a strategic partner build-out) is decided per-vertical on evidence from the first two customer pilots — it is not assumed.

Weeks 1–2 · Frame
  • Baseline metrics captured
  • One use case, tightly scoped
  • Security review started
  • Evaluation dataset drafted from real interactions
Weeks 3–6 · Build
  • Partner wires channels + integrations
  • Agent instructions & policy matrices configured
  • Grounding sources connected
  • Eval harness passing on golden set
Weeks 7–10 · Prove
  • Production-like traffic (shadow, then live slice)
  • Weekly metric reviews vs thresholds
  • Human-fallback drills
  • Cost guardrails monitored
Weeks 11–12 · Decide
  • Results vs success thresholds
  • Production-readiness checklist
  • Consumption ramp forecast
  • Go/no-go with sponsor; ownership handover
Objection handling

You will hear these. Say this.

“We already have a chatbot / IVR vendor.”

Those deflect within one channel. This is the layer that reasons and acts across all channels and systems — keep the channels, upgrade the intelligence behind them. Offer a conversation-intelligence audit of real traffic as a no-risk first step.

“Isn't this what your Customer Engagement Suite does?”

Customer Engagement Suite modernizes the contact centre. This is for communications embedded in wider workflows — collections, sales, HR, education. Where the customer needs both, we run a joint motion with explicit swimlanes. Use the decision tree; never improvise the boundary.

“Voice AI startups demo better.”

Demos aren't deployments. The gap is telephony, compliance, state, observability, and integration at enterprise scale — inside the customer's tenant. Startups are potential partners on our platform, not necessarily competitors.

“Customers will reject AI calls.”

Design decides this: disclosure, language, latency, and instant human escape hatches. Pilot metrics include CSAT and complaint rate against the human baseline — we measure it rather than argue it.

“The ROI numbers feel inflated.”

We never present outcome claims — the calculator uses the customer's own volumes and costs, shows every formula, and pilots establish the real containment rate before any scale decision.

“What about hallucination and compliance risk?”

Agents are grounded in governed sources, act only through permissioned tools, follow policy matrices they cannot override, and log everything. Compare against today's baseline: sampled QA on 2% of human calls.

“Why not build it ourselves on raw models?”

Many try; the platform is the hard part — orchestration, state, evals, governance, channel infrastructure. Internal teams that start from raw APIs rebuild all of it. Offer the reference architecture as the accelerant either way.

Content rules for anything customer-facing

No unsupported savings claims · no fabricated references · no unverified competitive or partner claims · no GECX conflict · no internal metrics (seat activation, consumption targets) in customer material · every number carries its source and confidence.

Open customer site