Databases
Relational and NoSQL design, optimization, migration and managed operations for mission-critical workloads.
Learn moreMedulae helps enterprises design, build and operate modern data platforms — from databases and analytics to data science, machine learning and AI.
Five practice areas, one integrated team. We take you from raw data to production-grade intelligence.
Relational and NoSQL design, optimization, migration and managed operations for mission-critical workloads.
Learn moreBI platforms, data warehouses and self-service dashboards that turn data into decisions.
Learn moreStatistical modeling, experimentation and research that answer hard business questions with evidence.
Learn moreEnd-to-end ML systems: from feature engineering to MLOps, deployed and monitored in production.
Learn moreLLMs, generative AI and intelligent agents integrated securely into your products and processes.
Learn moreWe combine deep technical craft with business context so every engagement produces measurable value.
Every engagement is led by senior engineers and consultants. No hand-offs, no junior benches.
We start from your KPIs, not from technology. Solutions are designed around outcomes you can measure.
Testing, documentation, security and monitoring are part of the delivery — not an afterthought.
We upskill your teams as we build, so capability stays in-house after we leave.
Training delivery runs through everything we do. We upskill your teams in databases, analytics, data science, ML and AI — and leave the capability in-house.
Curricula tailored to your stack, data and maturity level — from SQL fundamentals to production MLOps and generative AI.
Practical sessions run on your own data and tooling, so learning transfers directly to daily work.
Focused sessions for leadership: strategy, risk, opportunity and adoption roadmaps for data & AI.
Side-by-side support for in-house teams during and after delivery, in English or Spanish.
A transparent four-phase framework adapted to your context — from first workshop to ongoing operation.
Workshops and audits to map your data landscape, goals and constraints into a clear roadmap.
Architecture, data models and delivery plan, validated with your teams before a line of code.
Agile iterations with regular demos, robust engineering practices and continuous feedback.
Monitoring, MLOps and knowledge transfer so solutions keep delivering value in production.
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