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LLM Observability

LLM Observability

Bring clarity and control to your AI systems.

As enterprises are integrating LLMs (Large Language Models) into their workflows, they are realizing the need for reliable AI outputs and transparency into the decision making. LLMOPS (Large Language Model Operations) is a comprehensive pipeline that evaluates and optimizes generated content. It integrates observability tools, dashboards and risk mitigation processes to ensure compliances and quality.

Hence, it can provide visibility into how models perform in real world conditions. It can look into aspects like model drift, hallucinations and policy violations for reliable AI systems that meet business goals.

Benefits

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Compliance & Risk Management

Reduces violations by monitoring AI outputs for safety, ethics, and legal standards

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Performance & Quality Assurance

Evaluates coherence, fluency, relevance, groundedness, and retrieval quality

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Operational Efficiency

Streamlines workflows and reduces manual oversight

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User Experience Enhancement

Builds trust through consistent, high-quality AI interactions

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Features

Here are the key features of this accelerator.

Monitoring Dashboards

Real-time, centralized dashboards that visualize how your LLMs and AI systems are performing. This helps teams get immediate visibility into health, performance, and user behaviour without digging into logs manually.

Feedback Loop Creation

Allows capturing human feedback on AI outputs. It then feeds that data back into your model-evaluation or retraining pipelines. This continuous feedback and refinement process ensures your models improve over time, aligning more closely with user expectations and business goals.

Compliance Monitoring

Provides mechanisms to track AI output against ethical, regulatory and organizational standards. This ensures your LLM deployments stay within compliance boundaries and reduces risk when deploying AI at scale.

Integration Capabilities

Built to integrate seamlessly with external data and analytics platforms (for example via APIs or connectors to systems like BigQuery or existing dashboards). That lets you combine LLM specific metrics with broader business, infrastructure or analytics data. Thus enabling holistic monitoring, reporting and correlation across systems.

Use Cases

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    AI Monitoring and Compliance

    Enables organizations to continuously oversee model outputs, detect policy or compliance violations (e.g. bias, privacy leaks, unsafe content) and ensure AI behaviour stays aligned with legal and ethical standards

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    Performance Evaluation of LLMs

    Tracks metrics like latency, error rates, token usage, and output quality, helping teams evaluate how LLMs perform in real-world conditions, detect degradation over time, and benchmark models or configurations against defined KPIs

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    Risk Management in AI Deployments

    Provides visibility into model drift, hallucinations or unexpected behaviour, helping identify and mitigate risks before they impact users or business operations.

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    Human-in-the-loop Feedback Optimization

    Supports feedback driven improvement cycles, enabling human reviewers or end-users to flag poor outputs or provide corrections. This feedback is then used to refine prompts, retrain or tune models, improving accuracy, relevance, and overall trust over time.

Accelerators

Trust your AI to do the right thing.

Implement observability and feedback loops that keep models aligned with business goals.

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