GenAI Observability
Full Request Capture
Capture every LLM request and response across all your providers — inputs, outputs, token usage, latency, and cost in one real-time view.
Deploying an AI model is just the beginning. At Spundan, we ensure your models remain accurate, fair, and reliable long after launch — with full visibility into their real-world performance.
Whether you're managing a single predictive model or an enterprise-scale ML pipeline, our observability solutions give you the tools to detect drift, diagnose failures, and maintain trust in your AI systems — continuously and at scale.
GenAI Observability
Full Request Capture
Capture every LLM request and response across all your providers — inputs, outputs, token usage, latency, and cost in one real-time view.
GenAI Evaluations & Quality
Quality Monitoring
Track and analyze AI output quality over time — detect hallucinations, toxicity, and accuracy issues before they impact your users.
AI Agent Observability
Execution Flow Visibility
Deep dive into your agent's logic — every tool call, MCP session, and step-by-step trace visualized for effective debugging.
VectorDB Observability
Retrieval Health Tracking
Monitor your RAG pipeline end-to-end — query latency, similarity scores, and index health to ensure accurate retrieval always.
MCP Observability
Protocol Visibility at Scale
Track tool usage, session health, transport performance, and integration reliability across your entire AI ecosystem.
GPU Observability
Infrastructure at a Glance
Monitor GPU utilization, memory, thermals, and multi-GPU coordination — keep your AI hardware at peak efficiency.
From GenAI models and agents to vector databases, MCP servers, and GPU infrastructure — we observe every layer of your AI stack, not just the model.
Our observability solutions are built on open standards — giving you vendor-neutral, portable instrumentation that works across any cloud, tool, or provider.
We go beyond metrics — continuously evaluating output quality, hallucinations, toxicity, and bias so your AI systems are trustworthy, not just fast.
Track LLM spend, token usage, and GPU costs in real time — with actionable insights that help you optimize AI infrastructure costs without sacrificing performance.
Purpose-built observability for modern agentic AI systems — tracking tool calls, session health, invocation patterns, and distributed traces across complex agent workflows.
Our observability frameworks are designed with GDPR, HIPAA, and emerging AI regulations in mind — ensuring your models are always audit-ready and ethically governed.
AI Model Observability is the practice of continuously monitoring your deployed models to ensure they perform as expected. Without it, models can silently degrade due to data drift, changing user behavior, or infrastructure issues — leading to poor decisions and lost business value.
Standard monitoring tracks predefined metrics. Observability goes deeper — it gives you the ability to ask any question about your model's behavior, even ones you didn't anticipate at deployment time. This includes root cause analysis, explainability, and lineage tracking.
Yes. We are tool-agnostic and can integrate with your current stack — whether you use AWS SageMaker, Azure ML, Google Vertex AI, MLflow, or custom pipelines. We design observability layers that plug into your workflows without disrupting them.
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