Data Mesh Architecture Design

We implement domain-driven design (DDD) to build decentralized data products governed by federated teams. Using event streaming and data contracts, our mesh approach enables cross-functional collaboration and ownership.

Interoperable AI Fabric Layer

We architect an AI Fabric layer that unifies model access, orchestration, and monitoring across domains. This includes integration with APIs, model registries, vector databases, and inference engines for seamless AI delivery.

Metadata Management

Deploy cross-domain policy enforcement, lineage tracking, and unified data catalogs with tools like Collibra, Amundsen, and Atlan. Our AI-enhanced governance layer auto-detects schema drift, access violations, and stale data products.

Event-Driven Data Pipelines

We engineer real-time data flows between domains using Kafka, Flink, and Debezium. These pipelines are built for resiliency, observability, and schema evolution to ensure continuous AI-readiness.

Data Productization Frameworks

Create self-serve data products with defined SLAs, APIs, ownership models, and monitoring hooks. We use tools like DataHub, dbt, and Dagster to expose high-quality, discoverable, reusable data assets.

AI Model Orchestration

Enable dynamic model training, versioning, and inference orchestration across domains using tools like MLflow, KServe, and Seldon Core. This allows domain-specific teams to plug-and-play their own ML pipelines.

Unified Observability

Deploy centralized monitoring for data pipelines, AI workloads, and access patterns using Grafana, OpenTelemetry, and WhyLabs. We automate feedback loops and integrate AI/ML monitoring into platform-level alerts.