Data engineering & analytics
Intelligence that works in production
Overview
Data engineering & analytics — part of RAEY's AI, Data & Emerging Tech practice. We deliver with documented processes, measurable outcomes, and teams that combine local insight with global engineering standards.
Why choose it
- Decisions backed by trusted data
- Automation that reduces manual workload
Features
- →Machine learning & AI agents
- →Data engineering & analytics
- →Computer vision & speech AI
- →IoT, blockchain & robotics
- →AI research laboratories
Technologies
Development workflow
Discovery
Stakeholder workshops, goals, and success metrics.
Requirement analysis
Functional, non-functional, and compliance requirements documented.
Planning
Sprint plan, milestones, and resource allocation.
UI/UX design
Wireframes, prototypes, and design system.
System architecture
Scalable architecture, APIs, and security model.
Development
Agile sprints with weekly demos and code reviews.
Quality assurance
Automated and manual testing across devices.
Security testing
Penetration testing, OWASP checks, and hardening.
Deployment
CI/CD pipelines, staging, and production rollout.
Monitoring
Observability, alerts, and performance dashboards.
Continuous maintenance
SLA support, updates, and iterative improvements.
Timeline & estimates
Small project
2–4 weeks
Medium project
1–3 months
Enterprise project
3–12 months
Deliverables
- Trained models with evaluation reports
- MLOps pipelines & monitoring
- Data dashboards & BI suites
Pricing models
Fixed scope
Defined deliverables with a fixed timeline and budget.
Time & materials
Flexible engagement billed per sprint or month.
Dedicated team
Embedded squad working as an extension of your team.
