Two Consulting Paths

One Governance Foundation.
Applied Throughout CRM, Data and AI Delivery.

For enterprises and consulting partners working across platform-agnostic CRM, data, AI, and software delivery—and for businesses, partners, and teams across the Salesforce ecosystem—FlyWheel applies AI and SDLC governance grounded in stewardship across solution and delivery integrity; data grounding and output integrity; security, safety, and operational resilience; Human Decision Authority; and verified learning and continuous improvement.

That governance foundation supports the full delivery lifecycle through:

• Strategic Advisory and Engagement Direction—providing leaders with practical direction before major commitments are made, helping teams prioritize investment, resolve material choices, and enter discovery or design with shared expectations.
• Discovery, Readiness, and Trade-off Evaluation—connecting business needs, affected users, dependencies, risks, and decision authority early to establish a sound basis for scope, governance, and investment.
• Holistic Solution Architecture and Design Integrity—aligning business processes, user experience, data, security, integrations, and support operations through a phased architecture and delivery roadmap that keeps each increment aligned with business outcomes, dependencies, and safeguards, reducing gaps between business expectations and the solution teams must use and sustain.
• Data Integration, Migration, Grounding, and Output Integrity—establishing clear system responsibilities, Integration Boundaries, exchange requirements, and recovery paths while preserving trusted data, traceable sources, and outputs aligned with approved business intent.
• AI-Assisted Build—applying FlyWheel’s AI-Assisted to Autonomous Agent Build & Code Delivery Governance throughout development. Generated code, configuration, tests, and documentation remain subject to engineering review and release requirements. FlyWheel’s AI-augmented acceptance criteria make expected behavior observable, testable, and verifiable.
• QA, UAT, and Regression Testing—applying rigorous quality assurance, user acceptance testing, regression testing, and evidence-supported release decisions.
• Hypercare and Continuous Monitoring—stabilizing releases, monitoring performance and deviations, and connecting corrective work to continuous improvement.
• KPI Instrumentation and Reporting—measuring AI value after accounting for technology and integration costs, Human review, exceptions, rework, remediation, silent failures, technical debt, quality, speed, and realized business outcomes. Leaders can use this evidence to evaluate which AI capabilities are creating measurable value and which require correction, limited use, or further investment before they scale.

Together, these capabilities connect business intent, solution design, governed development, testing, release, monitoring, and measurable results across the full lifecycle.

Choose the platform-agnostic enterprise path or the Salesforce-focused path. Each engagement brings senior judgment, clear decision authority, delivery discipline, and evidence into the work from the start.