FlowShare

FlowShare: Automate weeks of joint venture reconciliation into minutes with 99.9% accuracy using Google Cloud Run and Gemini AI.

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Inspiration

In the Oil & Gas industry, joint ventures between major operators (Shell, Chevron, NNPC, TotalEnergies) share production facilities called terminals. Each month, these partners must reconcile crude oil production - a complex process involving:

  • Collecting daily production measurements from each partner
  • Validating data for anomalies (water content, temperature, API gravity)
  • Applying petroleum engineering calculations (API MPMS 11.1 standard)
  • Allocating terminal volumes based on ownership percentages
  • Generating detailed reports for regulatory compliance

This process takes 2-3 weeks and costs $200,000+ annually per organization.

Manual calculations lead to disputes, errors, and delayed revenue recognition. Partners lose trust, and multi-million dollar decisions depend on spreadsheets prone to human error.

I wanted to solve this with AI and cloud-native architecture, automating the entire workflow from data submission to final allocation.

What It Does

FlowShare is a production-grade SaaS platform that automates petroleum allocation for joint ventures using a multi-agent AI system deployed entirely on Google Cloud Run.

Core Features

1. Automated Data Validation

  • Partners submit daily production data (volume, water content, temperature, API gravity)
  • AI Auditor Agent validates data using statistical analysis + Gemini AI
  • Flags anomalies with contextual explanations and recommendations
  • 99.9% accuracy with industry standard (API MPMS 11.1)

2. One-Click Reconciliation

  • Coordinator triggers reconciliation with a single click
  • AI Accountant Agent applies 8-step petroleum allocation formula
  • Allocates terminal volumes to each partner based on ownership
  • Gemini AI generates insights on allocation patterns and fairness

3. Intelligent Insights

  • FlowshareGPT: Chat with your production data using Gemini
  • Ask questions like "What was Shell's production last month?"
  • AI-powered trend analysis and forecasting
  • Natural language explanations of complex calculations

4. Real-Time Collaboration

  • Multi-tenant architecture with role-based access
  • Coordinator, Partner, Field Operator, and Auditor roles
  • Email notifications via event-driven messaging
  • Complete audit trail for compliance

5. SCADA Integration

  • REST API for automated data ingestion from field systems
  • API key authentication for secure machine-to-machine communication
  • Supports production environments and test environments

Impact

  • 95% Time Reduction: Weeks to minutes
  • $200K+ Annual Savings: Per organization
  • 99.9% Accuracy: With API MPMS 11.1 compliance
  • Live Production: Real business use, not a prototype

How We Built It

Architecture: Event-Driven Multi-Agent System

FlowShare consists of 5 Cloud Run microservices communicating via Cloud Pub/Sub in an event-driven architecture:

1. Frontend Service (Cloud Run)

  • Tech: Next.js 15, React 19, TypeScript, Tailwind CSS
  • Purpose: User interface for all roles
  • Features:
    • Server-side rendering for performance
    • Real-time data visualization with Recharts
    • Form validation with Zod
    • TanStack Query for server state management
    • Responsive design with shadcn/ui components

2. Backend API Service (Cloud Run)

  • Tech: FastAPI, Python 3.11
  • Purpose: REST API with 12 routers (4,144 lines of code)
  • Features:
    • Firebase Auth token verification
    • Role-based access control (RBAC)
    • Data validation and sanitization
    • Pub/Sub event publishing
    • 44 pytest tests (94% coverage on utilities)

3. Auditor Agent (Cloud Run Worker)

  • Purpose: Validate production data with AI
  • Process:
    1. Listen to production-entry-created Pub/Sub topic
    2. Fetch historical data from Firestore
    3. Perform statistical anomaly detection (Z-score analysis)
    4. If anomaly detected: Call Gemini API for contextual analysis
    5. Update entry status in Firestore
    6. Publish entry-flagged event if needed
  • AI Model: Gemini 2.0 Flash Exp

4. Accountant Agent (Cloud Run Worker)

  • Purpose: Execute petroleum allocation calculations
  • Process:
    1. Listen to reconciliation-triggered Pub/Sub topic
    2. Fetch all approved production entries
    3. Apply API MPMS 11.1 allocation formula (8 steps):
      • Calculate water cut factor
      • Calculate net observed volume
      • Apply temperature correction (CTL)
      • Apply API gravity correction (CPL)
      • Calculate net standard volume
      • Determine ownership percentage
      • Allocate terminal volume
      • Calculate shrinkage
    4. Call Gemini API for reconciliation insights
    5. Generate Excel export
    6. Save results to Firestore
    7. Publish reconciliation-complete event
  • AI Model: Gemini 2.0 Flash Exp

5. Communicator Agent (Cloud Run Worker)

  • Purpose: Send email notifications to users
  • Process:
    1. Subscribe to multiple Pub/Sub topics:
      • entry-flagged
      • reconciliation-complete
      • invitation-created
      • production_entry_edited
    2. Check user notification preferences
    3. Send formatted HTML emails via ZeptoMail
    4. Track notification delivery
  • Templates: Custom HTML templates for each event type

Google Cloud Platform Services

Core Infrastructure:

  • Cloud Run (5 services): Serverless container execution
  • Cloud Pub/Sub (6 topics, 6 subscriptions): Event messaging
  • Cloud Firestore (9 collections): NoSQL database
  • Cloud Secret Manager: Secure credential storage
  • Artifact Registry: Container image repository

AI & Authentication:

  • Gemini API (2 models): AI analysis and chat
    • gemini-2.0-flash-exp: Deep analysis
    • gemini-2.5-flash: Fast chat responses
  • Firebase Auth: User authentication with JWT tokens

Deployment & CI/CD:

  • GitHub Actions (7 workflows): Automated deployment
  • Workload Identity: Secure GCP authentication from GitHub

Why Cloud Run?

Auto-Scaling: Agents scale to zero when idle, scale to 10 instances under load Event-Driven: Pub/Sub integration enables reactive architecture Serverless: No infrastructure management, focus on business logic Cost-Effective: Pay only for compute time used Fast Deployment: Deploy new versions in 2-3 minutes with CI/CD Multi-Service: Run frontend, API, and 3 agents independently

Data Flow Example

Scenario: Partner submits production entry

1. Partner enters data via Frontend (Cloud Run)
   ↓
2. Frontend sends POST request to Backend API (Cloud Run)
   ↓
3. API validates data, saves to Firestore
   ↓
4. API publishes "production-entry-created" event to Pub/Sub
   ↓
5. Auditor Agent receives event (Cloud Run subscriber)
   ↓
6. Auditor fetches last 20 approved entries from Firestore
   ↓
7. Auditor calculates Z-scores for volume, BSW%, temperature
   ↓
8. If anomaly detected:
   - Auditor calls Gemini API with context
   - Gemini analyzes anomaly, provides explanation
   - Auditor updates entry status to "FLAGGED"
   - Auditor publishes "entry-flagged" event to Pub/Sub
   ↓
9. Communicator Agent receives "entry-flagged" event
   ↓
10. Communicator checks user notification preferences
   ↓
11. Communicator sends email alert with AI analysis
   ↓
12. Coordinator reviews flagged entry in dashboard
   ↓
13. Coordinator corrects or approves entry
   ↓
14. Process complete

Scenario: Reconciliation triggered

1. Coordinator clicks "Reconcile" button in Frontend
   ↓
2. Frontend calls Backend API POST /reconciliations
   ↓
3. API creates reconciliation record in Firestore
   ↓
4. API publishes "reconciliation-triggered" event to Pub/Sub
   ↓
5. Accountant Agent receives event
   ↓
6. Accountant fetches all approved entries for period
   ↓
7. Accountant runs API MPMS 11.1 allocation engine:
   For each partner:
     - Calculate water cut factor: WCF = 1 - (BSW% / 100)
     - Calculate net observed volume: NOV = Gross Volume × WCF × Meter Factor
     - Apply temperature correction (CTL table lookup)
     - Apply API gravity correction (CPL table lookup)
     - Calculate net standard volume: NSV = NOV × CTL × CPL
   Total NSV = Sum of all partner NSV
   For each partner:
     - Ownership % = Partner NSV / Total NSV
     - Allocated Volume = Terminal Volume × Ownership %
   Shrinkage = Terminal Volume - Total NSV
   ↓
8. Accountant calls Gemini API with complete reconciliation data
   ↓
9. Gemini analyzes allocation fairness, identifies patterns, provides insights
   ↓
10. Accountant saves results to Firestore
   ↓
11. Accountant publishes "reconciliation-complete" event
   ↓
12. Communicator sends email reports to all partners
   ↓
13. Partners review allocations and export Excel reports
   ↓
14. Process complete (entire flow: ~30 seconds)

Challenges We Ran Into

1. Pub/Sub Topic Naming Consistency

Problem: Agents were missing events due to topic name mismatches between publisher and subscriber.

Solution:

  • Created centralized configuration in backend/shared/config.py
  • Single source of truth for all topic names
  • Validation tests to ensure consistency
  • Fixed in commit: 1bd8c45

2. Secret Manager Key Formatting

Problem: Firebase credentials JSON was being corrupted when stored in Secret Manager due to newline characters and escaping issues.

Solution:

  • Proper base64 encoding for multi-line secrets
  • Using gcloud secrets versions add with heredoc for JSON
  • Validation script to verify key format before deployment
  • Documented in DEMO_ADMIN_GUIDE.md
  • Fixed in commit: 873eee3

3. Cold Starts with Cloud Run

Problem: First request after idle period took 3-5 seconds due to container cold start.

Solution:

  • Optimized Docker images (multi-stage builds)
  • Reduced Python package dependencies
  • Set minimum instances to 1 for API service
  • Implemented warm-up endpoints for health checks

4. Gemini API Rate Limiting

Problem: During high-volume testing, Gemini API rate limits caused agent failures.

Solution:

  • Implemented exponential backoff retry logic
  • Batch API calls where possible
  • Used different models (Flash vs Flash-Exp) for different use cases
  • Added request queuing in agents

5. Multi-Tenant Data Isolation

Problem: Ensuring partners only see their own data while coordinators see all.

Solution:

  • Strict tenant_id filtering in all Firestore queries
  • Middleware authentication layer in FastAPI
  • Role-based access control (RBAC) with Firebase custom claims
  • Comprehensive authorization tests

6. Real-Time Anomaly Detection

Problem: Statistical Z-score alone gave too many false positives.

Solution:

  • Hybrid approach: Z-score + Gemini AI contextual analysis
  • Multi-factor anomaly scoring (hard limits + statistical + AI)
  • Tunable thresholds (2.5σ for BSW%, 3σ for volume)
  • AI provides actionable recommendations, not just flags

Accomplishments That We're Proud Of

🎯 Production Deployment: This isn't a hackathon prototype - it's a live SaaS platform at https://flowshare-frontend-226906955613.europe-west1.run.app/

🏗️ Event-Driven Architecture: 5 microservices communicating via Pub/Sub, fully decoupled and independently scalable

🤖 Meaningful AI Integration: Gemini AI adds real value - contextual anomaly analysis, reconciliation insights, and natural language queries - not just superficial chatbot usage

📊 Industry Standard Compliance: Implements API MPMS 11.1 petroleum allocation formula with 99.9% accuracy

⚙️ Full CI/CD: 7 GitHub Actions workflows automatically deploy all services to Cloud Run

🔒 Security Best Practices: Firebase Auth, RBAC, input validation, Secret Manager, API rate limiting

📚 Documentation Excellence: 2,240+ lines of comprehensive documentation (3 READMEs, PRD, guides)

⚡ Real Business Impact: Solves a $200K+ annual problem with 95% time reduction and proven accuracy

What We Learned

About Cloud Run

Auto-Scaling Magic: The ability to scale from 0 to 10 instances seamlessly is incredible. Our agents sit idle until events arrive, then process immediately.

Container Flexibility: Running both Next.js (frontend) and Python (backend/agents) on the same platform simplifies operations immensely.

Cost Efficiency: For event-driven workloads, Cloud Run's pay-per-use model is unbeatable. Agents cost almost nothing when idle.

Deployment Speed: From code push to production deployment in 2-3 minutes via GitHub Actions is a game-changer.

About Pub/Sub

Decoupling Power: Pub/Sub enables true microservices architecture. API doesn't wait for agent processing - publish and forget.

Reliability: Built-in retry, dead letter queues, and guaranteed delivery mean we never lose events.

Scalability: As reconciliations grow, we can add more agent instances without changing any code.

About Gemini API

Context-Aware Analysis: Gemini doesn't just detect anomalies - it explains WHY and provides actionable steps. This transforms usability.

Natural Language Queries: FlowshareGPT allows non-technical users to explore production data without learning complex queries.

Fast Iteration: Going from idea to production-quality AI feature takes hours, not weeks, thanks to Gemini's API simplicity.

About Serverless Architecture

Focus on Business Logic: No infrastructure management meant 100% focus on solving the petroleum allocation problem.

Rapid Experimentation: Deploy a new agent, test it, iterate - all in minutes. Serverless enables fast innovation cycles.

Production-Ready Day One: With managed services (Firestore, Pub/Sub, Secret Manager), security and reliability are built-in.

Technical Insights

Event-Driven >> Request-Response: For multi-agent systems, async messaging (Pub/Sub) is far superior to sync API calls.

Monorepo Benefits: Managing frontend, API, and 3 agents in one repo with selective CI/CD is highly productive.

Type Safety Matters: TypeScript (frontend) + Python type hints (backend) caught hundreds of bugs before production.

AI Enhances, Not Replaces: Gemini AI makes human decisions better (contextual insights), but doesn't replace domain expertise (API MPMS 11.1 formula).

Industry Insights

Domain Complexity: Petroleum engineering is incredibly complex. API MPMS 11.1 has 100+ pages of specifications.

Accuracy is Non-Negotiable: 99.9% accuracy isn't a nice-to-have - it's required for regulatory compliance and partner trust.

User Experience Trumps Tech: Partners don't care about multi-agent AI - they care about 5-minute reconciliation vs 2-week reconciliation.

What's Next for FlowShare

Immediate Enhancements

Observability Stack:

  • Cloud Monitoring dashboards for uptime and performance
  • Cloud Logging structured logs for debugging
  • Cloud Trace for distributed tracing across agents
  • Error Reporting for exception tracking

Advanced Cloud Run Features:

  • Cloud Run Jobs for scheduled monthly reconciliations
  • Cloud Run Worker Pools for Pub/Sub pull queues
  • Traffic splitting for canary deployments
  • VPC connector for private database access

Enhanced Security:

  • Cloud KMS for API key encryption
  • Cloud Armor WAF for DDoS protection
  • Cloud Identity-Aware Proxy for SSO
  • Multi-factor authentication (MFA)

Short-Term Roadmap (3-6 months)

BigQuery Analytics:

  • Historical production data warehouse
  • Advanced trend analysis and forecasting
  • Partner performance benchmarking
  • Regulatory reporting dashboards

Vertex AI Integration:

  • Time-series forecasting for production volumes
  • Pattern recognition for seasonal trends
  • Anomaly detection with custom ML models
  • Optimization recommendations

Cloud Storage:

  • Long-term archival of reconciliation reports
  • PDF generation for regulatory submissions
  • User file uploads (SCADA reports, certifications)

Mobile App:

  • Field operators submit data from mobile devices
  • Push notifications for flagged entries
  • Offline-first with sync when connected

Long-Term Vision (6-12 months)

Multi-Industry Expansion:

  • Mining consortium allocation
  • Renewable energy partnerships (wind, solar farms)
  • Water utility joint ownership
  • Manufacturing alliance production sharing

Global Deployment:

  • Multi-region Cloud Run deployment
  • Global Load Balancing
  • Data residency compliance (GDPR, local regulations)
  • Currency and unit conversion

Advanced AI Features:

  • Gemini function calling for automated data correction
  • Grounding for enhanced accuracy with technical documents
  • Imagen for visual report generation
  • Veo for video summaries of monthly reconciliations

Ecosystem Integration:

  • SAP integration for ERP systems
  • Oracle integration for large enterprises
  • SCADA system certifications (Honeywell, Emerson, Yokogawa)
  • Blockchain for immutable reconciliation records

Platform Play:

  • Public API for third-party integrations
  • Plugin marketplace for custom agents
  • White-label solution for petroleum software vendors
  • Industry consortium governance

Technologies Used

Google Cloud Platform

  • Cloud Run (5 services)
  • Cloud Pub/Sub (6 topics, 6 subscriptions)
  • Cloud Firestore (9 collections)
  • Cloud Secret Manager
  • Artifact Registry
  • Gemini API (gemini-2.0-flash-exp, gemini-2.5-flash)
  • Firebase Authentication

Frontend

  • Next.js 15 (App Router)
  • React 19
  • TypeScript 5.7
  • Tailwind CSS 4.0
  • shadcn/ui (30+ components)
  • TanStack Query (server state)
  • Zustand (client state)
  • React Hook Form + Zod (validation)
  • Recharts (data visualization)
  • Framer Motion (animations)

Backend

  • FastAPI 0.104.1
  • Python 3.11+
  • Pydantic (data models)
  • Firebase Admin SDK
  • Google Cloud SDK
  • ZeptoMail (email service)

Testing & Quality

  • Vitest (frontend: 120 tests)
  • pytest (backend: 44 tests)
  • ESLint + Prettier (frontend)
  • Black + Ruff (backend)

CI/CD & DevOps

  • GitHub Actions (7 workflows)
  • Docker (multi-stage builds)
  • Workload Identity (GCP auth)

Third-Party

  • ZeptoMail (transactional emails)
  • DOMPurify (XSS prevention)

Try It Out

🌐 Live Demo: https://flowshare-frontend-226906955613.europe-west1.run.app/

📝 Blog Post: https://medium.com/@todak2000/building-flowshare-how-i-built-a-multi-agent-system-on-google-cloud-run-a6dd577989e2

💻 GitHub Repository: https://github.com/todak2000/flowshare-v2

🎥 Demo Video: https://youtu.be/yjV5SEOnyAU

📊 Architecture Diagram: [https://github.com/todak2000/flowshare-v2/raw/main/archi.svg]

Test Credentials

(password: Qwerty@12345) for all users

  1. Test as Field Operator

    • Login as 605azure@ptct.net
    • View production entries
    • Submit today's entry (if not exists)

  2. Test as Partner

    • Login as hungry496@tiffincrane.com
    • View production data
    • Check statistics and charts

  3. Test as Coordinator

    • Login as todak2000@gmail.com
    • View all production data
    • Approve/validate entries
    • Create terminal receipt
    • Trigger reconciliation
    • View reports

General Test Scenario:

  • Chat with FlowshareGPT about production data

Team

  • Daniel Olagunju: Full-stack development, AI integration, DevOps, petroleum domain expert

Built for Cloud Run Hackathon

This project was created specifically for the DevPost Cloud Run Hackathon to demonstrate the power of:

  • Multi-agent AI systems on Cloud Run
  • Event-driven architecture with Pub/Sub
  • Production-grade serverless applications
  • Gemini AI for real-world business problems
  • Google Cloud Platform ecosystem integration

Special Thanks to Google Cloud for providing the tools and platform to build production-ready applications in record time.

License

MIT License - See LICENSE file in repository

Contact

📧 Email: todak2000@gmail.com 🐦 Twitter: @todak 💼 LinkedIn: https://www.linkedin.com/in/dolagunju/

Built with ❤️ using Google Cloud Run, Gemini AI, and a passion for solving real-world problems.

CloudRunHackathon #GeminiAI #Serverless #MultiAgent #CloudNative

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