Process
How We Work
Our Research-to-Production Loop ensures every deployment is planned, built, validated, and continuously improved with measurable outcomes.
How We Work
Align
Define business objectives, success metrics, and stakeholder alignment. Establish baselines and ROI targets.
Assess
Evaluate technical feasibility, data availability, integration requirements, and risk factors. Select optimal approach.
Design
Architecture design, system integration plan, evaluation framework, and governance structure. Define quality gates.
Build
Implement AI system with evaluation harness, monitoring infrastructure, and integration components. Follow best practices.
Validate
Run evaluation suite, regression tests, and quality gates. Validate against baselines and success criteria.
Launch
Deploy to production with rollout playbook, monitoring active, and operational runbooks in place.
Operate & Improve
Monitor performance, optimize cost and quality, conduct A/B tests, and deliver monthly executive readouts.
Process Details
Align
- Stakeholder alignment on business objectives and success metrics
- Define ROI targets and baseline measurements
- Establish project governance and communication cadence
- Identify key decision-makers and approval processes
Assess
- Evaluate technical feasibility and data availability
- Assess integration requirements and system architecture
- Identify risk factors and mitigation strategies
- Select optimal approach (RAG, agents, fine-tuning, classic ML)
- Define evaluation framework and quality gates
Design
- System architecture and integration design
- Data pipeline and processing workflows
- Evaluation harness and test suite design
- AI Control Tower configuration and metrics definition
- Governance framework and compliance documentation
- Rollout playbook and risk mitigation protocols
Build
- Implement AI system with best practices and patterns
- Build evaluation harness and regression test suite
- Configure monitoring infrastructure and AI Control Tower
- Develop integration components and data pipelines
- Create user documentation and training materials
Validate
- Run evaluation suite and quality gates
- Validate against baselines and success criteria
- Conduct integration testing and user acceptance testing
- Perform security and compliance validation
- Complete launch readiness assessment
Launch
- Deploy to production with rollout playbook
- Activate monitoring and alerting
- Execute phased rollout (if applicable)
- Conduct user training and change management
- Establish operational runbooks and incident response
Operate & Improve
- Monitor performance via AI Control Tower
- Conduct weekly performance reviews
- Optimize cost, quality, and business impact
- Run A/B tests and deploy improvements
- Deliver monthly executive readouts
- Continuously improve based on metrics and feedback
Project Governance
Weekly Check-ins
Weekly status meetings with project stakeholders to review progress, metrics, and blockers. Updates include AI Control Tower metrics and quality gate status.
Executive Readouts
Monthly executive readouts summarize business impact, cost performance, risk status, and optimization opportunities. Delivered via AI Control Tower dashboards and presentation.
Quality Gates
Defined quality gates at each phase prevent progression until success criteria are met. Gates include evaluation suite passing, baseline metrics established, and risk assessments completed.
Launch Readiness Checklist
- ✓ Evaluation suite passing all regression tests
- ✓ AI Control Tower operational with baseline metrics established
- ✓ Integration tested and validated with existing systems
- ✓ Security and compliance validation completed
- ✓ Rollout playbook and risk mitigation protocols documented
- ✓ Operational runbooks and incident response procedures in place
- ✓ User documentation and training materials complete
- ✓ Stakeholder sign-off on launch readiness
Ready to start your AI deployment?
Book a call to discuss your project and how our process can deliver measurable results.