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Fiddler AI Solutions for the Public Sector

  • Agentic

    Evaluate and monitor cost-effective agentic systems.

    Get aggregated and granular visibility to understand performance and behavior: from the application → session → agent → trace → to the span

    Agentic Observability for Autonomous and Reflective Multi-Agent Systems

    Enterprises are rapidly adopting multi-agent systems, which can introduce exponential complexity. Agentic applications bring autonomy, reasoning, and coordination that break the traditional monitoring framework apart, leaving enterprises without the traceability and interpretability needed to understand why agents make specific decisions and their dependencies. Fiddler restores visibility, context, and control across the entire agentic lifecycle.

    Build. Test. Monitor. Improve.

    Fiddler delivers end-to-end Agentic Observability, giving enterprises complete visibility across the agentic hierarchy. It observes agents holistically and makes them interpretable, so that you can understand system behaviors, dependencies, and outcomes.

    By combining evaluations in development with monitoring in production, Fiddler provides a continuous feedback loop, making agentic systems more reliable, cost-effective, and scalable.

    Fiddler is a rich, contextually-aware performance experience that reveals system-wide insights. It is not a one-trace-at-a-time debuggability experience.

    Deliver High Performance AI

    From evaluations in development to monitoring in production, launch high-performing agents.

    Protect From Costly Risks

    Build reliable agents and enforce runtime guardrails to safeguard operations, and prevent costly and reputational risks.

    Maximize Mission Success

    Optimize resources and decision accuracy through in-environment scoring across testing and production—enhancing mission outcomes while minimizing hidden costs and operational risk.

    Build & Test: Launch Reliable Agents

    • Evaluate agents with curated golden and challenger datasets to improve performance and behavior.
    • Run low-cost experiments with different prompts and responses to reach better outcomes.
    • Stress-test edge cases to bolster weaknesses early, reducing risks and harmful incidents after launch.

    Monitor: Gain Complete Visibility Across the Agentic Hierarchy

    • View aggregate and granular insights in customizable dashboards and reports.
    • Monitor key metrics, including hallucination, toxicity, PII/PHI, and jailbreak, and custom KPIs.
    • Get visibility up and down the agentic hierarchy to see what happened in any interaction.
    • Track agent reasoning chains, tool calls, and decision paths across sessions.

    Analyze & Improve: Understand the ‘Why’ to Optimize Agent Performance

    • Perform hierarchical root-cause analysis to pinpoint the failing span with full context, reducing MTTI and MTTR.
    • Uncover cross-agent dependencies and bottlenecks that impact system performance.
    • Surface critical issues with targeted filtering, sorting, and span attributes to drive improvements.
    • Create a feedback loop to improve agent performance, accuracy and safety.
  • LLM Observability

    Fiddler provides a complete workflow to safeguard, monitor, and analyze LLM applications.

    An All-in-One Platform for Your LLM Observability and Security Needs

    The Fiddler AI Observability and Security platform is built to help enterprises launch accurate, safe, and trustworthy LLM applications. With Fiddler, you can safeguard, monitor, and analyze generative AI (GenAI) and LLM applications in production.

    Metrics-driven LLMOps in Production Environments

    Proactively Detect LLM Risks

    Safeguard LLM applications with low-latency model scoring and LLM guardrails to mitigate costly risks, including hallucinations, safety violations, prompt injection attacks, and jailbreaking attempts.

    Analyze Issues in Prompts and Responses

    Utilize prompt and response monitoring to receive real-time alerts, diagnose issues, and understand the underlying causes of problems as they arise.

    Pinpoint High-Density Clusters

    Visualize qualitative insights by identifying data patterns and trends on a 3D UMAP visualization.

    Track Key Metrics with AI Observability Dashboards

    Create dashboards and reports that track PII, toxicity, hallucination, and other LLM metrics to increase cross-team collaboration to improve LLMs.

    The MOOD Stack: Empowering AI Observability for LLM Applications

    The MOOD stack is the new stack for LLMOps to standardize and accelerate LLM application development, deployment, and management. The stack comprises Modeling, AI Observability, Orchestration, and Data layers.

    AI Observability is the most critical layer of the MOOD stack, enabling governance, interpretability, and the monitoring of operational performance and risks of LLMs. This layer provides the visibility and confidence for stakeholders across the enterprise to ensure production LLMs are performant, accurate, safe, and trustworthy.

  • Fiddler Trust Service for Monitoring and Guardrails

    How Fiddler Trust Service Strengthens AI Guardrails and LLM Monitoring

    As part of the Fiddler AI Observability and Security platform, the Fiddler Trust Service is an enterprise-grade solution designed to strengthen AI guardrails and LLM monitoring, while mitigating LLM security risks. It provides high-quality, rapid monitoring of LLM prompts and responses, ensuring more reliable deployments in live environments.

    Powering the Fiddler Trust Service are proprietary, fine-tuned Fiddler Trust Models, designed for task-specific, high accuracy scoring of LLM prompts and responses with low latency. Trust Models leverage extensive training across thousands of datasets to provide accurate LLM monitoring and early threat detection, eliminating the need for manual dataset uploads. These models are built to handle higher traffic and inferences as LLM deployments scale, ensuring data protection in all environments — including air gapped deployments — and offering a cost-effective alternative to closed sourced models.

    Fiddler Trust Models deliver guardrails that moderate LLM security risks, including hallucinations, toxicity, and prompt injection attacks. They also enable comprehensive LLM monitoring and online diagnostics for Generative AI (GenAI) applications, helping enterprises maintain safety, compliance, and trust in AI-driven interactions.

    Fiddler Trust Models are Fast, Cost-Effective, and Accurate

    Key LLM Metrics Scoring for Guardrails and Monitoring

    With the Fiddler Trust Service, you can score an extensive set of metrics, ensuring your LLM applications deliver the most advanced LLM use cases and stringent agency demands. At the same time, it safeguards your LLM applications from harmful and costly risks.

    Hallucination Metrics

    • Faithfulness / Groundedness
    • Answer relevance
    • Context relevance
    • Conciseness
    • Coherence

    Safety Metrics

    • PII
    • Toxicity
    • Jailbreak
    • Sentiment
    • Profanity
    • Regex match
    • Topic
    • Banned keywords
    • Language detection

    Fiddler Trust Service: The Solution for LLM Applications in Production

    Protect and Secure with Guardrails

    • Safeguard your LLM applications with guardrails that instantly moderate risky prompts and responses.
    • Customize guardrails to your organization’s risk standards by defining and adjusting thresholds for key LLM metrics specific to your use case.
    • Proactively mitigate harmful and costly risks, such as hallucinations, toxicity, safety violations, and prompt injection attacks, before they impact your enterprise or users.

    Monitor and Diagnose LLM Applications

    • Use Fiddler’s Root Cause Analysis to uncover the full set of moderated prompts and responses within a specific time period.
    • Fiddler’s 3D UMAP visualization enables in-depth data exploration to isolate problematic prompts and responses.
    • Share this list with Model Development and Application teams to review and enhance the LLM application, preventing future issues.

    Analyze Key LLM Insights for Mission Impact

    • Analyze LLM metrics with customized dashboards and reports.
    • Track the key LLM metrics that matter most for your use case and stakeholders, driving mission-critical KPIs.
    • Gain oversight to meet AI regulations and compliance standards.
  • ML Observability

    Optimize MLOps With AI Observability

    Efficiently operationalize the entire ML workflow, trust model outcomes, and align your AI solutions to dynamic agency contexts with the Fiddler AI Observability platform.

    MLOps for the Entire ML Lifecycle

    AI Observability is the foundation of good MLOps practices, enabling you to gain full visibility of your models in each stage of the ML lifecycle from training to production.

    Your Partner for MLOps

    Accelerate AI time-to-value and scale

    The Fiddler AI Observability platform supports each stage of your MLOps lifecycle. Quickly monitor, explain, and analyze model behaviors and improve model outcomes.

    Gain confidence in AI solutions

    Build trust into your AI solutions. Increase model interpretability and gain greater transparency and visibility on model outcomes with responsible AI.

    Align stakeholders through the ML lifecycle

    Increase positive mission outcomes with streamlined collaboration and processes across teams to deliver high-performing AI solutions.

    Continuous Monitoring

    • Centralized view of all models for MLOps, ML engineers, data scientists, and GRC to collaborate throughout the ML lifecycle.
    • Observe model health by tracking performance, drift, data integrity, or model bias.
    • Monitor structured and unstructured (text and image) models.
    • Detect behavior changes in models with highly imbalanced datasets.
    • Receive real-time alerts on model performance, data drift, and data integrity.

    Deep Explainability

    • Enable stakeholders and regulators with human-readable explanations to understand model behavior.
    • Obtain global and local-level explanations of how different attributions contribute to model prediction.
    • Compare and contrast feature values and their impact on model prediction.
    • Bring your own explainers into Fiddler for explanations customized for your AI projects.

    Rich Analytics

    • Validate model before deployment with out-of-the-box performance metrics.
    • Uncover underlying reasons causing model performance or drift issues with root cause analysis.
    • Drill down on slices of data to understand underperforming segments.
    • Improve models with actionable insights from powerful dashboards showing feature impact, correlation, or distribution.

    Trust and Fairness

    • Assess models with model and dataset fairness checks at any point in the ML lifecycle.
    • Increase confidence in model outcomes by detecting intersectional unfairness and algorithmic biases.
    • Leverage Fiddler’s out-of-the-box fairness metrics including disparate impact, group benefit, equal opportunity, and demographic parity.

    Model Governance

    • Evaluate model performance and ensure all models are compliant for audit purposes.
    • Provide stakeholders with fine-grained control and visibility into models, enabling them with deep model interpretations.
    • Reduce risks from model degradation or model bias.
  • Explainable AI

    Model performance is reliant not only on metrics but also on how well a model can be explained when something eventually goes wrong.

    Explainable AI Techniques at Enterprise Scale

    The Fiddler AI Observability platform delivers the best interpretability methods available by combining top explainable AI principles, including Shapley Values and Integrated Gradients, with proprietary explainable AI methods. Obtain fast model explanations and understand your ML model predictions quickly with Fiddler Shap, an explainable method born from our award-winning AI research.

    Deploy responsibly

    Gain Visibility into Your Most Deeply Complex Models

    To ensure continuous transparency, Fiddler automates documentation of explainable AI projects and delivers prediction explanations for future model governance and review requirements. You’ll always know what’s in your training data, models, or production inferences.

    • Understand every single prediction made by your AI solution and spot discrepancies using out-of-the-box explanation methods.
    • Simulate ‘What If’ scenarios in tabular and text models to validate and build trust.
    • Bring in data and models from any platform and gain faithful explanations.

    Minimize risk

    Increase Coverage, Efficiency, and Confidence in Your Models

    You can deploy AI governance and model risk management processes effectively with Fiddler.

    By proactively identifying and addressing deep-rooted model bias or issues with Fiddler, you not only safeguard against costly fines and penalties but also significantly reduce the risk of negative publicity. Stay ahead of AI disasters and maintain brand reputation.

    • Understand model predictions at a global and local level before it’s put into production.
    • Explain complex recursive inputs, or bring your own explainers specific to your use case.
    • Generate and share any type of report required for MRM and compliance reviews using Fiddler Report Generator.

    Responsible AI Principles

    It’s important to understand model performance and behavior before putting anything into production, which requires complete context and visibility into model behaviors — from training to production.

    • Compare distributions across training, test, and production data.
    • Explain performance discrepancies across groups by slicing data into groups.
    • Find anomalies and data drift by analyzing AI predictions relative to an entire data set or specific groups.

    Explainable AI Features

    • On demand explanations – Perform counterfactual analysis in real-time.
    • Gradient-based explanations – Get faster explanations than perturbation-based explanations.
    • Advanced explainability – Explain complex recursive inputs for fine-grained attributions.
    • Shapley values (SHAP) and Fiddler SHAP – Increase transparency and interpretability using SHAP values, including Fiddler's award-winning metric.
    • Integrated gradients – Run deep learning models (NLP, CV) faster and understand how features contribute to skew and predictions.
    • ‘What-if’ analysis – Change any value and study the impact on scenario outcomes.
    • Global and local explanations – Understand feature contributions globally and root-cause issues locally.
    • Surrogate models – Improve interpretability using automatically generated surrogate models.
    • Custom explanations – Bring custom explainers via APIs into Fiddler’s patented UI.