Objective evaluation and technical clarity
Choosing a data platform for enterprise AI involves navigating vendor hype, legacy dependencies, and strict security requirements. Without an independent structural evaluation, organizations risk committing to expensive platforms that fail to support target AI use cases.
Navigating Platform Trade-Offs
Evaluating platforms like SAP HANA, Databricks, Snowflake, and open-source frameworks against your actual requirements.
Structuring Enterprise Data Architecture
Designing target data pipelines, storage formats, and access controls tailored to your operational constraints.
Securing Board Approval
Producing an objective, vendor-agnostic decision matrix that technical leaders can confidently present to executive boards.
Board-ready design and engineering briefs
Target Architecture Design
Complete technical blueprints detailing your future-state data platform, data flows, and security boundaries.
AI Platform Selection & Decision Matrix
A comprehensive evaluation documenting trade-offs, licensing costs, scaling capabilities, and ecosystem fit across leading tools.
Phased Data Migration Roadmap
A structured, risk-mitigated plan for transitioning legacy systems to your modern target environment.
Actionable Build Brief
Detailed technical specifications ready for execution by internal engineering teams or external build partners.
From constraints to blueprints
Weeks 1–2: Requirements & Constraints
Deep-dive technical sessions with enterprise architects to map security rules, latency needs, data volume, and legacy integration points.
Weeks 3–5: Platform Evaluation & Benchmarking
Rigorous testing of target platform options against your organization’s specific technical constraints.
Weeks 6–8: Architecture & Handoff
Finalizing target architecture diagrams, mapping out the migration roadmap, and assembling the execution brief for deployment.
Next steps for deployment
Once your target architecture design is finalized, Ekipa provides clear pathways for technical execution.
Execute platform build
Transition directly into production platform engineering via Data Platform Implementation.
Deploy AI models
Connect operational AI applications to your newly designed data stack through AI Delivery.
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