(For Google Employee's Only)

Autonomous Supply Chain and Manufacturing with Google Cloud

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Fixed Price • Fixed Scope • 4-Week Pilot

Gemini Enterprise Accelerator — Supply Chain and Manufacturing AI Agents

FPFS: Fixed Price Fixed Scope – Pilot in 4 Weeks. Production in 3 Months

Pluto7’s FPFS Pilot for Planning in a Box – Pi Agent is a 4-week, fixed-price engagement designed to prove AI value fast — using customer data, on their Google Cloud Tenant. We help businesses solve one high-impact supply chain use case like inventory optimization or demand forecasting with predictable cost, clear outcomes, and zero guesswork

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Driving Annual GCP Consumption. Transactable via Marketplace.

How We Move Fast with FPFS

We remove friction before the pilot starts by showing value early.

Customized Demo

Working AI demo tailored to their use case

TCO / ROI

Showcase measurable business impact

FPFS Pilot

Fast, fixed 4-week engagement around one use case

Production Rollout

Infrastructure and pipeline setup with limited scaling in 3 months

THE 1-YEAR AI JOURNEY

Build. Scale. Govern. Optimize

Accelerating long-term AI adoption and enterprise-grade autonomy.

BUILD Month 1
Pilot Execution
Solve one high-impact use case using 2–3 data sources to demonstrate measurable business value and rapid ROI within 30 days.
Rapid ROI $90k for 4 Weeks
SCALE Month 2-4
Production Rollout
Deploy an enterprise-grade production solution with secure architecture, AIOps, MLOps, and automated operations for reliable scaling.
Enterprise Scale $40k per month
GOVERN Month 5-8
Stabilize & Scale
Stabilize production, monitor performance, strengthen governance, and expand adoption across additional business units, SKUs, or locations.
Reliable Growth $30k per month
OPTIMIZE Month 8-12
Excellence & Review
Continuously measure business outcomes, refine models and processes, and extend the platform to new high-value AI use cases.
Continuous Excellence $30k per month

Powered by Gemini Enterprise, Vertex AI, and Google Cloud. Delivering 10X Faster Decision Intelligence with Functional Business Agents.

This stepwise approach lets us move fast without asking customers to take blind bets.

FPFS Deliverables

  • GCP and Planning in a Box Pi Agent Platform Setup
  • Data ingestion from 2–3 sources (SAP, Oracle, Salesforce, BigQuery, CSV, APIs) – one time manual upload during Pilot
  • Single Use Case Implementation using Planning in a Box Accelerators spanning 4 layers of the Platform
    • Pi.Decision: 1 or 2 Dashboards for insights and AI agent (Pi Agent) deployed on Gemini Enterprise
    • Pi.Semantic: Business glossary and semantic layer mapping specific to the single use-case
    • Pi.Unify: Supports Manual upload of data, data validation, and ingestion logs
    • Pi.Shield: Governance layer showcasing Trust score, logs, and in-built protection.
  • Documentation: BRD, Production Rollout Plan

FPFS is the first step toward full production. Costing $90K, this 4-week pilot proves value quickly, paving way for a ~3-month production rollout covering platform setup, security, infrastructure hardening, and stabilization.

Connect with Pluto7 Account Manager Check Production Pricing

BOM: Key GCP components

  • Gemini Enterprise
  • Cortex framework
  • BigQuery
  • Vertex AI
  • Agent Builder
  • Gemini
  • Google Agent Development Kit (ADK)
  • Cloud Storage
  • Looker
  • Data Fusion

What You’ll Find on This Page

FPFS Use Cases Discovery Cheat Sheet Sales Asset Library Spot Good Opportunity 30 Second Pitch Competitive Battlecards

FPFS Use Cases

Demand Forecasting/Sensing

Pilot Scope (4 Weeks)

  • Platform: GCP + Planning in a Box
  • Data: 25 SKUs, 2–3 manually uploaded sources
  • Forecasting: Monthly sales quantity, 12-month horizon, level 2–3
  • Models: 15–20 forecasting algorithms
  • Output: Forecasts, accuracy vs baseline, model metrics & decomposition
  • Documents: BRD, TDD, Production Rollout Plan

Production Scope (3 Months)

  • Infrastructure Hardening: Production-ready DevOps, MLOps, and SecurityOps
  • Data Quality & Governance: AutoDQ rules and basic reconciliation for reliable data
  • Observability: End-to-end logging and monitoring for forecast pipelines
  • Scaling & Performance: Optimized performance with scaling up to 50 SKUs
  • UI Enhancements: Minimal updates to improve intuitiveness and usability
  • Change Management: Planner enablement to adopt, interpret, and trust forecasts

Real-Time Inventory Visibility

Pilot Scope (4 Weeks)

  • Platform: GCP + Planning in a Box
  • Data: Real-time ingestion from 1–2 source systems into BigQuery
  • Model: Centralized Master Ledger (digital twin of inventory)
  • Coverage: 25 SKUs
  • Output: Master Ledger view with search/sort and 3 KPIs (On-Hand vs Turnover, Inventory Status, Inventory at Risk)
  • Documents: BRD, TDD, Production Rollout Plan

Production Scope (3 Months)

  • Infrastructure Hardening: Production-ready DevOps, DataOps, and SecurityOps
  • Data Quality & Governance: AutoDQ rules and reconciliation for reliable data
  • Observability: End-to-end logging and monitoring for real-time ingestion
  • Scaling & Performance: Optimized performance with controlled scaling up to 50 SKUs
  • UI Enhancements: Minimal updates to improve usability and navigation
  • Change Management: User enablement to interpret inventory signals and KPIs

Defect Detection

Pilot Scope (4 Weeks)

  • Platform: GCP + Planning in a Box (PiAB) deployed for quality analytics
  • Data: One production line, 10–15 SKUs; one-time upload of historical process, sensor, and inspection data
  • Analytics Logic: Defect pattern detection and correlation of process variables with defect outcomes
  • Risk Scoring: Identification of high-risk SKUs and process conditions likely to cause defects
  • Outputs: Dashboard showing top defect drivers, early-warning signals, and defect risk hotspots
  • Documents: BRD, TDD, Production Rollout Plan

Production Scope (3 Months)

  • Infrastructure Hardening: Production-ready DevOps, MLOps, and SecurityOps
  • Data Quality & Governance: Validation rules and reconciliation for trusted data
  • Observability: Logging and monitoring for ranking calculations and data refreshes
  • Scaling & Performance: Optimized performance with scaling up to 25 SKUs
  • UI Enhancements: Intuitive dashboards for defect analysis, root-cause exploration
  • Change Management: User enablement to interpret rankings and take actions

OTIF Optimization

Pilot Scope (4 Weeks)

  • Platform: GCP + Planning in a Box
  • Data: Real-time ingestion from inventory, DC, and order sources into BigQuery
  • Coverage: 25 SKUs across selected DCs / warehouses
  • Logic: Availability-based delivery date calculation per SKU–location
  • Output: Committed delivery date, and inventory source visibility
  • Documents: BRD, TDD, Production Rollout Plan

Production Scope (3 Months)

  • Infrastructure Hardening: Production-ready DevOps, DataOps, and SecurityOps
  • Data Quality & Governance: AutoDQ rules and reconciliation for reliable data
  • Observability: End-to-end logging and monitoring for OTIF calculations
  • Scaling & Performance: Optimized performance with controlled scaling up to 50 SKUs
  • UI Enhancements: Minimal updates to make insights intuitive
  • Change Management: User enablement to use OTIF Calculation in decision-making

AI-Powered Inventory Simulation

Pilot Scope (4 Weeks)

  • Platform: GCP + Planning in a Box
  • Data: Manual upload for 25 SKUs
  • Scenarios: 2 predefined optimization scenarios per SKU (50 simulations total), parallel batch-run using Pub/Sub and Cloud Run Service
  • Scenarios Covered: Safety stock increase vs reorder point optimization
  • Automation: Agent-driven actions such as PO creation based on selected recommendations
  • Notifications: Email summary with simulation results and link to recommendations page
  • Documents: BRD, TDD, Production Rollout Plan

Production Scope (3 Months)

  • Infrastructure Hardening: Production-ready DevOps, MLOps, and SecurityOps
  • Data Quality & Governance: AutoDQ rules and reconciliation for reliable data
  • Observability: Logging and monitoring for simulation runs, agent actions, and failures
  • Scaling & Performance: Optimized batch execution with scaling up to 50 SKUs
  • UI Enhancements: Minimal updates to make insights intuitive
  • Change Management: Planner enablement to interpret outcomes and approve actions

Supplier Risk Scoring

Pilot Scope (4 Weeks)

  • Platform & Data: GCP + Planning in a Box
  • Data: 15 SKUs with associated suppliers. One-time manual upload of supplier, product, and historical performance data
  • Logic: Supplier scoring and ranking based on reliability metrics (e.g., OTIF, lead time adherence, quality)
  • Output: Dashboard showing top 3 reliable suppliers, best supplier per SKU, and best supplier overall
  • Documents: BRD, TDD, Production Rollout Plan

Production Scope (3 Months)

  • Infrastructure Hardening: Production-ready DevOps, MLOps, and SecurityOps
  • Data Quality & Governance: Validation rules and reconciliation for trusted data
  • Observability: Logging and monitoring for ranking calculations and data refreshes
  • Scaling & Performance: Optimized performance with scaling up to 30 SKUs
  • UI Enhancements: Intuitive dashboards for supplier comparison and drill-downs
  • Change Management: User enablement to interpret rankings and take actions
Explore Pricing

Discovery Cheat Sheet

Key Questions to Qualify FPFS Opportunities

1

What are the top supply chain or inventory planning challenges you’re facing today?

5

Is there urgency around improving forecasting accuracy or inventory efficiency?

2

Do you have at least 6–12 months of historical demand, sales, or inventory data?

6

Would you benefit from a Marketplace-transactable FPFS offering?

3

How many core systems are involved (SAP/Oracle/Salesforce/etc.)?

7

Do you see value with real time S&OP planning with a digital twin?

4

Are you exploring AI/ML initiatives for supply chain or planning in 2026?

8

Do you prefer a low-risk pilot with predictable cost and timeline?

If the customer says "yes" to any 3+, they are FPFS-ready.

Sales Asset Library

Tacori

Tacori

Tacori achieved better order planning and marketing analytics with Google Cortex, Generative AI, and Looker.

Learn More
Lixil

Lixil

Lixil implemented Preventive Maintenance Machine Learning Models leveraging Google Cortex, Vertex AI, and Generative AI.

Learn More
Ulta Beauty

Ulta Beauty

Ulta Beauty deployed ML models to accurately predict the delay and quantity of their products using Google Cortex, Vertex AI, and Looker.

Learn More

Planning in a Box Explained – Full Version

Planning in a Box Explained – Short Version

How We Engage

What a Good Opportunity Looks Like

Strong Opportunity Signals

Customer is discussing forecasting, inventory, financial planning, or supply chain automation
They have budget committed to Google Cloud or Marketplace
They have multiple systems and struggle with data fragmentation
They have 6–12+ months of historical data
They want AI pilots but fear high cost/complexity

Pilot Readiness Signals

  • Access to relevant data sources for the pilot scope (Pluto7 will help define what’s needed)
  • Alignment on pilot scope and timeline (focused use case vs enterprise-wide rollout)
  • A clearly identified business owner (Supply Chain, Planning, Operations)
  • IT engagement or sponsorship to support data access and deployment

30 Second Pitch

30-second pitch

"Pluto7 offers a 4-week, Fixed Price, Fixed Scope pilot built on Google Cloud that uses the customer's own data to rapidly improve inventory visibility and forecasting accuracy. It's powered by their Planning in a Box Pi Agent platform, which unifies data, builds an AI-ready semantic layer, and delivers real insights in a month. The pilot is marketplace transactable, leverages BigQuery and Vertex, and typically expands into a full planning or digital twin deployment."

When to position it

  • Customer wants AI but doesn’t know where to start
  • They need value quickly
  • They have data silos
  • They want predictable cost & scope

Competitive Battlecards

GenAI Platforms

Pricing

Partnerships

Collaterals

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