Partnership means production delivery — not certification alone — with 80+ enterprise projects, certified team members, standardized patterns, and reusable accelerators.

DS Stream has 80+ enterprise Databricks projects completed, a Databricks-certified team (Data Engineer, Data Engineer Professional, Machine Learning Professional), standardized delivery patterns for Databricks architecture, security and CI/CD, and reusable accelerators built on the platform. Partnership is grounded in outcomes, not vendor alignment.

The hardest challenges are the operating model, governance, and MLOps layer — not the platform itself — and DS Stream brings 80+ projects of production delivery patterns to fill those gaps.

Platform access is table stakes. DS Stream brings 80+ projects of production delivery patterns, reusable accelerators, ISO 42001-aligned governance frameworks, and an AI Factory operating model that compresses time-to-value. We would welcome a conversation to compare approaches and identify where there is a gap we can fill.

Unity Catalog is non-negotiable on every engagement with centralized metadata management, column-level access control, full data lineage, and immutable audit logging from day one.

Every engagement includes Unity Catalog for centralized metadata management, column-level access control, full data lineage, and immutable audit logging. MLflow model registry tracks every model version, experiment, and deployment. The result: 100% model visibility and governance coverage, as demonstrated in a leading CPG AI Engineering Platform engagement.

CPG is DS Stream's deepest credential, with secondary focus on Financial Services, Retail, and Manufacturing using CPG proof points.

DS Stream's engagement model is phased and value-gated — Phase 1 MVP in 8 weeks for AI Factory, 4-week PoC for Agentic CPG Data Operations.

For the AI Factory, Phase 1 (MVP, 8 weeks) delivers a governance framework, Databricks platform foundation, and the first two use cases in production — with ROI baseline captured before committing to Phase 2. For Agentic CPG Data Operations, a 4-week PoC (credited toward full implementation) delivers a working prototype with 5 automated sources. Clients only proceed when each stage delivers validated results.

Pilot phase is the ideal time to engage — an architecture conversation at the pilot stage can eliminate months of rework and technical debt later.

DS Stream is deliberately focused on CPG AI Factory depth on Databricks — multiple years of ongoing platform partnership, 80+ projects of production patterns, and reusable accelerators that generalist SIs cannot replicate.

Our CPG AI Factory depth on Databricks is specific — multiple years of ongoing platform partnership, 80+ projects of hard-won production patterns, and reusable accelerators that generalist SIs cannot replicate. We build alongside client teams with genuine knowledge transfer as a design objective — not a dependency model. Clients leave every engagement more capable, not more reliant on DS Stream.

Retailer & distributor data automation on Lakeflow, marketing budget optimization with MLflow, AI/ML Center of Excellence on Databricks, GenieOps AIOps for IT ticket automation, customer service voicebot & chatbot deployment.

Lakehouse & Delta Lake foundations, MLflow MLOps pipeline implementation, Unity Catalog data governance rollout, Lakeflow & DLT declarative pipeline automation, centralized AI unification platforms.

Multi-agent AIOps orchestration with Agent Bricks, RAG for enterprise knowledge bases on Databricks, AI copilots and assistant deployment, agentic data pipeline self-healing, MCP/A2A standards implementation.

Fraud detection on Delta Lake, MLflow model governance & audit trails, ISO 42001-aligned regulatory AI compliance, risk scoring MLOps pipelines.

Clinical AI platform governance on Databricks, predictive ML pipelines, secure data Lakehouse implementation, HIPAA-compliant AI deployment.