Sera AI

A self-operating campaign engine that fetches creators, auto-invites, negotiates terms, manages content, and handles payments.

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Sera AI - Powered by DigitalOcean Gradient AI

Inspiration

Influencer marketing today is manual, fragmented, and operationally heavy. Brands spend hours discovering creators, negotiating pricing, tracking deliverables, and managing payments. Creators face inconsistent communication, unclear expectations, and delayed payouts.

While the marketing layer is digital, the decision-making and execution layer remains human-dependent.

For the DigitalOcean Gradient AI Hackathon, we reimagined this industry not just as an automation problem - but as an AI infrastructure problem.

Instead of building another marketplace, we asked:

  • What if campaign intelligence could be trained?
  • What if negotiation could be model-driven?
  • What if performance prediction improved over time?
  • What if the entire system ran on scalable GPU-backed AI infrastructure?

Sera AI was rebuilt as a production-ready, multi-agent AI operating system, powered by DigitalOcean Gradient AI from training to deployment.

What It Does

Sera AI is an AI-powered autonomous campaign engine that manages the entire influencer lifecycle using trained models and deployable AI agents.

Brands create campaigns with defined goals and budgets. Once launched, AI agents:

  • Discover and rank creators using a trained scoring model
  • Predict campaign ROI before launch
  • Optimize commission structures dynamically
  • Simulate negotiation outcomes using fine-tuned language models
  • Track content deliverables automatically
  • Generate structured invoices
  • Trigger payment workflows
  • Continuously learn from campaign outcomes

Unlike rule-based automation systems, Sera AI uses trained intelligence models deployed on DigitalOcean Gradient™ AI infrastructure.

How We Built It (Using Gradient AI Full Stack)

To meet hackathon requirements, Sera AI was rebuilt to leverage the complete AI lifecycle:

1. Model Training (GPU-Powered)

Using DigitalOcean GPU-backed compute, we trained:

  • Creator Performance Prediction Model

    • Regression model trained on historical engagement + campaign data
    • Predicts ROI probability before campaign launch

  • Commission Optimization Model

    • Learns optimal payout structures based on performance patterns

  • Negotiation Agent Persona

    • Fine-tuned LLM to simulate realistic negotiation tone and counteroffers

Training was performed on GPU droplets to accelerate experimentation and model iteration.

2. Model Deployment

Trained models were:

  • Containerized using Docker
  • Deployed as scalable inference endpoints
  • Integrated into our Node.js backend via API layer
  • Hosted fully on DigitalOcean infrastructure

This ensures production-grade deployment rather than local experimentation.

3. Multi-Agent Orchestration Layer

Sera AI uses a modular AI agent system:

  • Matching Agent (uses trained scoring model)
  • Negotiation Agent (LLM-based persona agent)
  • Workflow Agent (state-driven milestone manager)
  • Invoice Agent (automated billing logic)
  • Payment Intelligence Module (performance-aligned payouts)

A centralized orchestration engine manages:

  • Agent communication
  • Campaign state transitions
  • Model inference triggers
  • Retraining feedback loops

4. Monitoring & Scalability

We implemented:

  • Model accuracy tracking
  • Prediction confidence logging
  • Campaign performance analytics
  • Infrastructure scaling through DigitalOcean compute resources

This transforms Sera AI from a static automation tool into a continuously improving AI system.

Challenges We Ran Into

1. Moving from Rule-Based to Model-Based Logic

Negotiation and creator selection are traditionally human processes. Translating these into trainable models required building structured datasets and defining measurable outcomes.

2. Multi-Agent Synchronization

With multiple AI agents making decisions, we had to ensure clean state management to prevent logical conflicts between matching, negotiation, and payment modules.

3. Infrastructure Integration

Deploying trained models into production-ready inference endpoints required careful containerization and backend integration within the hackathon timeline.

Accomplishments We’re Proud Of

  • Successfully trained and deployed AI models using GPU-backed infrastructure
  • Built a working multi-agent AI campaign engine
  • Integrated prediction, negotiation, and optimization models into one system
  • Designed a modular architecture ready for scaling
  • Delivered a production-deployable AI application, not just a prototype

Most importantly, Sera AI demonstrates the full AI lifecycle on DigitalOcean Gradient AI - from training to deployment to real-time inference.

What We Learned

We learned that true AI automation is not just workflow logic - it requires:

  • Structured datasets
  • Measurable performance signals
  • Iterative training cycles
  • Clean model deployment architecture

We also learned that multi-agent systems must be carefully orchestrated to avoid cascading decision conflicts.

Most importantly, deploying AI models to real infrastructure transforms ideas into products.

What’s Next for Sera AI

Next, we plan to:

  • Implement automated retraining pipelines based on campaign outcomes
  • Introduce reinforcement learning for negotiation improvement
  • Expand predictive analytics dashboards for brands
  • Stress-test scalability with larger campaign volumes
  • Conduct live pilot deployments with real brands and creators

Our long-term vision is to position Sera AI as the AI-native operating system for influencer marketing, powered by scalable cloud infrastructure and continuously improving intelligence models.

With DigitalOcean Gradient™ AI, Sera AI moves beyond automation - into autonomous decision-making at scale.

Built With

  • digitalocean-app-platform
  • digitalocean-gpu-droplets
  • digitalocean-gradient?-ai
  • digitalocean-managed-databases
  • docker
  • express.js
  • fine-tuned-llm
  • instagram-graph-api
  • langchain
  • mongodb
  • mongodb-vector-search
  • multi-agent-architecture
  • node.js
  • oauth-2.0
  • python-(model-training)
  • pytorch
  • react-19
  • rest-apis
  • scikit-learn
  • stripe
  • tailwind-css
  • webhooks
  • youtube-data-api
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