Datai Analytics
Services

Engineer. Operate. Govern. Apply.

End-to-end delivery across data engineering, operations, governance, and AI applications. Five service lines, covered by senior engineers, on the platforms our team works in every day.

— 01

Data Engineering

We design and build the platforms that turn raw data into governed, query-ready assets. Whether you're modernizing a legacy warehouse or starting greenfield on the lakehouse, we deliver architectures that scale with your business.

  • Lakehouse architecture and Delta Lake design on Databricks
  • Snowflake and Microsoft Fabric warehouse modeling and performance tuning
  • Medallion architecture (bronze / silver / gold) with quality gates
  • Streaming and batch ingestion (Confluent, Kafka, Auto Loader, Snowpipe)
  • dbt-based transformation frameworks with testing and documentation
  • Migration from legacy platforms (Hadoop, on-prem warehouses) to cloud-native
  • API and system integration across operational and analytical systems
— 02

DataOps & Governance

The operational discipline that makes data trustworthy. We build the quality, lineage, access, and observability layers that hold production pipelines accountable — and keep them that way.

  • Unity Catalog and Snowflake Horizon governance setup and policy design
  • Data quality frameworks with Great Expectations, dbt tests, and custom checks
  • End-to-end lineage tracking across ingestion, transformation, and BI consumption
  • Access control, data classification, and PII protection policies
  • Pipeline observability — freshness, volume, schema-drift, and SLA alerting
  • Incident runbooks and on-call patterns for production data products
  • Data contracts between producing and consuming teams
— 03

DevOps & CI/CD

Software-engineering rigor for data and AI systems. Versioned, tested, repeatably deployed — every day, not every quarter.

  • Git-based workflows for notebooks, SQL, dbt, and ML projects
  • Automated testing for data transformations and ML pipelines
  • Environment promotion across dev, staging, and production
  • Infrastructure-as-Code with Terraform for cloud data platforms
  • GitHub Actions, Azure DevOps, and GitLab CI pipeline implementations
  • Databricks Asset Bundles and Snowflake schema-change management
  • Pre-commit hooks, code review automation, and quality gates
— 04

Machine Learning

From feasibility through production, we deliver ML systems that earn their place in the business. We focus on outcomes — not models on a shelf.

  • Use-case discovery and feasibility assessment
  • Feature engineering and feature store design
  • Model development across classical ML, deep learning, and forecasting
  • MLflow tracking, model registry, and experiment management
  • Production deployment, batch and real-time inference
  • Model monitoring, drift detection, and retraining workflows
  • Generative AI and LLM integrations using Databricks Mosaic AI and OpenAI
— 05

AI Applications

Models in a registry deliver no value alone. We build the applications that put predictions, recommendations, and decisions in front of the people who act on them.

  • Decision-support applications surfacing model output to business users
  • Internal data tools and self-serve interfaces for analysts
  • RAG systems over enterprise documents, knowledge bases, and tickets
  • LLM copilots embedded in existing workflows (Slack, Teams, ServiceNow)
  • Streamlit, Dash, and React front-ends for ML systems
  • Backend APIs and orchestration for AI-enabled features
  • Human-in-the-loop interfaces for review, override, and feedback collection
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