export const highlightCards = [
  {
    title: "Tailored Learning Environments",
    description:
      "Domain-specific simulators let agents explore safely before production."
  },
  {
    title: "Actively Learning AI Agents",
    description:
      "Policies evolve in real time based on fresh feedback loops."
  },
  {
    title: "Simulation-First Experimentation",
    description:
      "Stress test strategies, analyze edge cases, and surface emergent behavior at scale."
  },
  {
    title: "Adaptive Decision Systems",
    description:
      "Evolve from static LLM workflows to continuous-learning pipelines that deliver measurable outcomes."
  }
];

export const services = [
  {
    name: "Adaptive Intelligence Consulting",
    summary:
      "Translate business objectives into RL frameworks and experimentation roadmaps.",
    details: [
      "Translate business objectives into RL frameworks and experimentation roadmaps.",
      "Align KPIs with reward design and long-term strategic impact.",
      "Identify automation opportunities and define ROI metrics.",
      "Connect data science and operations into unified adaptive workflows."
    ]
  },
  {
    name: "Simulation Environment Design",
    summary:
      "Build synthetic environments that de-risk policy learning.",
    details: [
      "Build synthetic environments that de-risk policy learning.",
      "Model multi-agent dynamics, rare events, and complex feedback loops.",
      "Accelerate policy robustness via controlled experiments.",
      "Deploy cloud or edge simulators with observability built-in."
    ]
  },
  {
    name: "Policy Learning & Optimization",
    summary:
      "Engineer adaptive policies for volatile, high-variance environments.",
    details: [
      "Apply bandits, DQN, actor-critic methods, and continual learning.",
      "Shape rewards to reflect constraints and maintain exploration balance.",
      "Benchmark across simulation and production with safety gates."
    ]
  },
  {
    name: "RL Integration & Deployment",
    summary:
      "Embed decision layers within CRM, ERP, and workflow systems.",
    details: [
      "Provide secure policy APIs with runtime guardrails.",
      "Enable low-latency inference, CI/CD retraining, and observability.",
      "Align fully with existing data ecosystems."
    ]
  },
  {
    name: "Managed RL-as-a-Service",
    summary:
      "Full RL operations with outcome-based SLAs.",
    details: [
      "Multi-agent workload support at scale.",
      "Automated evaluation, drift correction, versioning, and rollouts.",
      "Continuous retraining based on live feedback signals."
    ]
  },
  {
    name: "Analytics & Governance",
    summary: "Executive-ready transparency into adaptive systems.",
    details: [
      "Interpretability reports, fairness audits, and ROI tracking.",
      "Governance dashboards for compliance, ethics, and real-world impact.",
      "Continuous monitoring to reinforce trust and alignment."
    ]
  }
];

export const solutions = [
  {
    name: "Adaptive Recommendation Engine",
    summary:
      "Ensemble bandits + hierarchical clustering for in-the-moment personalization.",
    details: [
      "Learns from user behavior and context in real time.",
      "Balances exploration, conversion, and trend sensitivity.",
      "Plugs into e-commerce and media systems."
    ]
  },
  {
    name: "Dynamic Pricing & Demand Optimization",
    summary:
      "RL-driven real-time pricing adjustments.",
    details: [
      "Models elasticity, competition, and seasonality.",
      "Continuous contextual experimentation under safety controls.",
      "Tuned for retail, SaaS, and travel."
    ]
  },
  {
    name: "Operational Workflow Optimizer",
    summary:
      "Agents that streamline operations by learning from every task.",
    details: [
      "Automates routing, scheduling, and resource allocation.",
      "Predicts delays and rebalances workloads.",
      "Integrates with logistics and ERP systems."
    ]
  },
  {
    name: "Personalized Engagement Engine",
    summary:
      "Campaigns that self-tune based on reward signals.",
    details: [
      "Optimizes cadence, channel, tone, and sequencing.",
      "Learns across the customer journey.",
      "Connects to CRM and marketing automation stacks."
    ]
  },
  {
    name: "Resource Allocation & Simulation Suite",
    summary:
      "Multi-agent simulation for fleets, supply chains, and infrastructure.",
    details: [
      "Stress tests, rare event modeling, and sensitivity analyses.",
      "Sensor-driven real-time coordination logic.",
      "APIs and dashboards for operations teams."
    ]
  },
  {
    name: "Decision Intelligence Dashboard",
    summary:
      "Full transparency into every policy decision.",
    details: [
      "Reward curves, drift charts, governance metrics.",
      "Built-in explainability and compliance reporting.",
      "Automates oversight with auditable outputs."
    ]
  }
];

export const researchPillars = [
  {
    name: "RLX Leaderboards",
    description:
      "Benchmark agents on exploration, generalization, and safety metrics with transparent scorecards."
  },
  {
    name: "Self-Reflective Learning (SRL)",
    description:
      "Teach agents to audit their own trajectories, revise strategies, and document reasoning trails."
  },
  {
    name: "Meta-Ethical Reward Shaping",
    description:
      "Align policies with nuanced cultural and human values via value-sensitive reward engineering."
  },
  {
    name: "Safe-RL Protocols",
    description:
      "Engineer verifiably robust policies for high-risk domains with formal safeguards."
  }
];

export const metrics = [
  {
    label: "AgentOps Observability",
    stat: "45+",
    description:
      "prebuilt monitors track policy drift, fairness, governance, and ROI in flight."
  },
  {
    label: "Agentic Guardrails",
    stat: "Zero-Trust",
    description:
      "alignment controls, safety throttles, and runtime prevention for harmful actions."
  },
  {
    label: "ROI Realization",
    stat: "8-12 mo",
    description:
      "time to measurable uplift across pricing, operations, logistics, and engagement programs."
  }
];

export const navLinks = [
  { label: "Services", href: "#services" },
  { label: "Solutions", href: "#solutions" },
  { label: "Research", href: "#research" },
  { label: "About", href: "#about" },
  { label: "Contact", href: "#contact" }
];