2 minute read

    The Challenge

    Our client faced significant data chaos, with fragmented, duplicated and opaque data pipelines managed manually, leading to inconsistent source tracking, resource waste, and conflicting data truths. The lack of automated pipeline management and provenance tracking resulted in quality issues, compliance risks, delayed reporting and bottlenecks in AI-driven innovation. This compromised operational resilience, eroded customer trust and threatened overall business performance.

    The Solution

    We implemented an automated DevOps-driven data lineage and graph analysis system, integrating foundation models (Claude/GPT via Bedrock), agent-based workflows (LangGraph and Bedrock Agents) and Neptune DB for seamless lineage traversal. The solution automated source tracking, proactive quality checks, and enabled natural language queries, enhancing pipeline reliability, compliance, operational efficiency and supporting scalable AI innovation to strengthen customer trust and business growth.

    The Results

    Automated Data Lineage and Compliance

    Conversational AI

    Continuous and Scalable AI Deployment

    Our Process

    Our DevOps-driven process prioritized continuous delivery and automated releases aligned with customer and business needs, enabling rapid iteration and market responsiveness.

    Discovery

    Identified fragmented data landscape and gaps in automation through stakeholder interviews and system analysis.

    Design and Implementation

    Built and tested scalable GenAI solutions using CI/CD pipelines, foundation models, and Infrastructure as Code practices.

    Outcomes

    Deployed an automated data lineage and graph analysis system, achieving trusted data sources, enhanced compliance, operational efficiency and accelerated innovation metrics.

    Continuous Improvement and Support

    Implemented KPI dashboards, automated incident response playbooks and iterative release cycles to ensure ongoing system resilience and performance enhancements.

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