Make Data Talk

AI Enterprise Platform: From Data to Strategic Decisions

A unified platform combining analytics, service automation, data governance, and intelligent archiving—powered by a retrainable model and aligned with Saudi Vision 2030.

90-Day Implementation Roadmap

Fast deployment with measurable results at every milestone

1

Weeks 0-2: Data Preparation

Foundation phase focused on assessment and planning to ensure successful deployment.

Activities

  • Readiness Assessment: Evaluate current infrastructure, data maturity, and organizational capabilities
  • Use Case Selection: Identify 2-3 quick-impact use cases with clear business value and feasibility
  • Data Collection: Gather initial datasets and establish data quality baselines
  • Stakeholder Alignment: Secure executive sponsorship and define success metrics

Deliverables

  • Detailed implementation plan with timelines and milestones
  • Data quality assessment report with remediation priorities
  • Selected use cases with ROI projections and KPIs
2

Weeks 3-8: Pilot Implementation

Controlled deployment phase with continuous measurement and optimization.

Activities

  • Initial Deployment: Deploy platform components for selected use cases in controlled environment
  • KPI Measurement: Track cycle time, error rates, time to insight, and completion rates
  • User Feedback: Systematic collection of user experiences and pain points
  • Iterative Improvements: Rapid adjustments based on performance data and user input
  • Training Programs: User training sessions and documentation development

Deliverables

  • Functional pilot system with validated use cases
  • Performance metrics dashboard with baseline comparisons
  • Trained user base with adoption tracking
  • Lessons learned documentation for production rollout
3

Weeks 9-16: Production Rollout

Enterprise-wide deployment with full governance and expansion planning.

Activities

  • Component Scaling: Expand infrastructure to support full user base and data volumes
  • Production Hardening: Implement enterprise security, monitoring, and disaster recovery
  • Production Deployment: Phased rollout to all user groups with support systems
  • Governance Automation: Activate automated compliance controls and monitoring
  • Expansion Planning: Identify next wave of use cases and optimization opportunities

Deliverables

  • Fully operational production system with enterprise SLAs
  • Automated governance and compliance framework
  • Comprehensive user documentation and support portal
  • ROI measurement framework with ongoing tracking
  • 3-year roadmap for platform evolution and expansion

Enterprise-Grade Security & Compliance

Data sovereignty, regulatory compliance, and comprehensive risk management

Local Compliance Framework

Comprehensive adherence to regional and international data management standards.

  • PDPL (SDAIA): Full compliance with Saudi Personal Data Protection Law requirements and SDAIA guidelines
  • NDMO Frameworks: Implementation of National Data Management Office controls and governance standards
  • DAMA-DMBOK: Based on international best practices for data management from DAMA International
  • ISO Standards: Alignment with ISO 27001 (Information Security) and ISO 8000 (Data Quality)

Security & Sovereignty

Complete control over your data with flexible deployment options and robust security.

  • On-Premises Deployment: Full air-gapped installation for maximum security and data sovereignty
  • Private Cloud: Dedicated infrastructure with isolated networking and storage
  • End-to-End Encryption: AES-256 encryption at rest, TLS 1.3 in transit
  • Granular Access Control: Role-based permissions with multi-factor authentication
  • Comprehensive Audit Logs: Immutable logging of all data access and system actions

Risk Mitigation Strategy

Proactive management of data, privacy, and AI risks through systematic controls.

Data Quality Risks

  • NDMO controls and continuous quality checks
  • Automated validation and cleansing rules
  • Real-time monitoring with alerting
  • Data quality scorecards and dashboards

Privacy Risks

  • Data minimization principles applied throughout
  • Privacy-by-design in all system components
  • Regular PDPL compliance reviews and audits
  • Consent management and data subject rights

Model Bias Risks

  • Regular testing for algorithmic fairness
  • Explainability features in AI models
  • Model governance and version control
  • Bias detection and mitigation protocols