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LTM Data And AI Tracer
https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_tracer-16c8d7f3-a630-44e8-aed3-0e729c4a9139/image4_LTM216X216px.png

LTM Data And AI Tracer

LTIMindtree Limited

Tarification Gratuit

Outcreate AI assurance with automated testing, explainable evaluation, and Responsible AI insights across the development lifecycle.

Tracer by LTM is an Azure-powered framework for black-box testing of core and generative artificial intelligence (AI) applications. It helps teams evaluate performance, detect hallucinations, assess risk, and strengthen Responsible AI practices across development, release, and production stages.

Built for iterative enterprise environments, Tracer helps organizations track multiple AI projects across versions through a configurable architecture. It combines automated testing, explainable reporting, and scalable evaluation services so teams can move faster with greater trust in how their AI systems perform.

Key Capabilities

  • Black-box testing for core AI and generative AI applications: Evaluates AI behavior without depending on internal model access, helping teams test faster across diverse use cases and delivery stages.
  • Hallucination detection and vulnerability analysis: Identifies unreliable outputs, model weaknesses, and risk patterns early so teams can reduce downstream business and compliance exposure.
  • Explainable evaluation and reporting: Uses visualized metrics and qualitative and quantitative assessments to make results easier to interpret for technical and business stakeholders.
  • Project and release tracking: Supports multiple AI projects across releases, helping teams monitor consistency, compare results, and govern change more effectively over time.
  • Red teaming and Responsible AI evaluation: Tests for bias, toxicity, and other vulnerabilities so organizations can strengthen trust, safety, and policy readiness before broader deployment.
  • Component-level testing for RAG and agent workflows: Helps teams isolate where quality issues originate, which shortens troubleshooting time and improves remediation.
  • Benchmark generation and compliance support: Enables structured evaluation against internal benchmarks and relevant regulatory expectations, improving confidence in enterprise adoption.
  • Flexible integration architecture: Works with libraries and services such as LangChain, LangGraph, RAGAS, DeepEval, FastAPI, and Azure components.

    • Business Benefits

  • Faster AI validation: Automates evaluation workflows to reduce manual effort.
  • Greater trust in AI systems: Provides clearer evidence on output quality and risk.
  • Better governance: Standardizes testing and reporting across projects.
  • Stronger Responsible AI posture: Integrates ethics and compliance checks.
  • Easier decision-making: Presents results in a usable format.
  • Improved adoption confidence: Enables move to production with fewer blind spots.

    • Azure-led Solution Foundation

  • Azure Web App: Hosts the core application securely.
  • Azure Static Web Apps: Supports dashboards and visualization.
  • Azure Cosmos DB for MongoDB: Stores evaluation data and configurations.
  • Azure Blob Storage: Stores datasets and artifacts.

    • Vue d’ensemble

    https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_tracer-16c8d7f3-a630-44e8-aed3-0e729c4a9139/image3_Screenshot1.png
    https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_tracer-16c8d7f3-a630-44e8-aed3-0e729c4a9139/image5_Screenshot2.png
    https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_dataandai_tracer-16c8d7f3-a630-44e8-aed3-0e729c4a9139/image1_Screenshot3.png