Our Use Cases

Discover how K22.ai can be applied across various industries and business functions to drive innovation and efficiency.

Data Engineering Workflows (Weave Agent)

Core Use Cases

  • Ingest data from cloud, streaming, and on-prem sources
  • Normalize, clean, and transform datasets
  • Apply quality checks, constraints, and data validation
  • Schema evolution and drift detection
  • Data cataloging and governance tagging

Target Outcomes

  • Clean, AI-ready, and governed datasets
  • Scalable pipelines built via natural language or agent delegation
  • Upstream integration with Tensor, Seer, Forge

Data Science Workflows (Seer Agent)

Core Use Cases

  • Exploratory data analysis (EDA)
  • Statistical profiling and feature engineering
  • Pattern and anomaly detection
  • Time-series forecasting and trend analysis
  • Business metric correlation and storytelling

Target Outcomes

  • Clear, explainable insights and data hypotheses
  • Feature-rich datasets for modeling
  • Visual reports for stakeholders

ML Engineering Workflows (Tensor Agent)

Core Use Cases

  • Model training and tuning (XGBoost, Scikit-learn, PyTorch, etc.)
  • Hyperparameter optimization and evaluation
  • Bias, drift, and fairness analysis
  • Model registration and lineage tracking
  • Deployment to staging and production endpoints

Target Outcomes

  • High-performance, auditable ML models
  • Integration with Seer outputs and Forge deployment flows
  • Continuous evaluation pipelines with guardrails

GenAI Application Workflows (Forge Agent)

Core Use Cases

  • Prompt design and evaluation (for LLMs)
  • RAG pipeline generation and optimization
  • Streamlit or frontend app scaffolding
  • Guardrail integration (toxicity, hallucination, injection prevention)
  • UX-centric testing, tracing, and feedback loops

Target Outcomes

  • Production-grade AI copilots and apps
  • Secure, controlled, and optimized GenAI interfaces
  • Reusability of LLM flows across internal tools

Cross-Agent Workflows

End-to-End Automation

    Example: Predict Customer Churn

    • Weave ingests and cleans CRM and support data
    • Seer analyzes churn patterns and identifies key drivers
    • Tensor builds and deploys a predictive model
    • Forge exposes it as an API and integrates it into a customer dashboard

Data Refresh and Monitoring

  • Scheduled agent triggers refresh data (Weave)
  • Drift check is run (Seer → Tensor)
  • If significant drift detected, auto-retraining is initiated (Tensor)
  • Stakeholder notified via Slack (Forge)

Use Case Templates

  • Predictive Maintenance (manufacturing)
  • Risk Scoring (finance)
  • Personalized Recommendations (retail)
  • HR Attrition Forecasting (enterprise ops)
  • Demand Forecasting (supply chain)
  • Internal GenAI Search Copilot (all verticals)