migration-work

          <h2 id="patterns-title">Judgment before machinery.</h2>

        </header>

        <ol class="pattern-list reveal">

          <li data-operating-step><div><h3>Bound business risk.</h3><p>Define continuity, customer, revenue, and data constraints before choosing target architecture.</p><p class="case-proof" data-case-proof><span>Proof:</span> AWS continuity, HealthTech checkout, YUI coexistence.</p></div></li>

          <li data-operating-step><div><h3>Bound business risk.</h3><p>Define continuity, customer, revenue, and data constraints before choosing target architecture.</p><p class="case-proof" data-case-proof><span>Proof:</span> AWS continuity, OneStudyTeam workflow sequencing, YUI coexistence.</p></div></li>

          <li data-operating-step><div><h3>Map contracts and dependencies.</h3><p>Identify consumers, readers, writers, wire formats, and invariants that must survive.</p><p class="case-proof" data-case-proof><span>Proof:</span> Celerity mobile contracts, OneStudyTeam data contracts.</p></div></li>

          <li data-operating-step><div><h3>Create reversible paths.</h3><p>Use coexistence, dual operation, staged cohorts, and versioned opt-in instead of one-way cutovers.</p><p class="case-proof" data-case-proof><span>Proof:</span> YugabyteDB dual paths, AWS cohorts, shared SDK opt-in.</p></div></li>

          <li data-operating-step><div><h3>Accelerate bounded work with AI.</h3><p>Let AI analyze, implement, and test only after humans define architecture, acceptance gates, and release safety.</p><p class="case-proof" data-case-proof><span>Proof:</span> Celerity and OneStudyTeam.</p></div></li>

              <p class="case-ai"><span>AI-assisted?</span>Yes. AI analyzed the 2,000-column relational schema and existing codebase, then generated synthetic data and property-based tests to fuzz data combinations.</p>

              <p class="case-judgment"><span>Human judgment</span>2 senior engineers owned workflow sequencing, integration and data-contract boundaries, continuity gates, cross-team decisions, stakeholder alignment, and a warehouse redesign to improve performance.</p>

              <p class="case-source"><span>Reference</span><a href="https://www.linkedin.com/in/radlakha/" data-case-reference="raman-adlakha" rel="noopener">Raman Adlakha, India Head and Director of Engineering, OneStudyTeam</a></p>

            </div>

          </details>

        </li>

        <li class="migration-case reveal" data-case="healthtech-angular-to-react-checkout" data-evidence="confirmed" data-sort="2024">

          <div class="case-year">2024</div>

          <div class="case-node" aria-hidden="true"></div>

          <details class="case-disclosure">

            <summary class="case-summary" aria-controls="healthtech-angular-to-react-checkout-details">

              <span class="case-title" role="heading" aria-level="3">HealthTech<br>Angular checkout → React</span>

              <span class="case-preview">Checkout conversion increased 6 to 8%, and disruption from navigation or payment windows fell 30%.</span>

              <span class="case-meta">About 3 months · Medium complexity · AI-assisted · Anonymous client</span>

            </summary>

            <div class="case-body case-content" id="healthtech-angular-to-react-checkout-details">

              <header class="case-header">

                <span class="case-status">Anonymous client</span>

              </header>

              <div class="case-metrics">

                <p class="metric" data-field="impact"><span class="metric-label">Business impact</span>Checkout conversion increased 6 to 8%. Disruptions caused by navigation or payment-window closure fell 30%.</p>

                <p class="metric" data-metric="duration"><span class="metric-label">Duration</span>About 3 months elapsed.</p>

                <p class="metric" data-metric="complexity"><span class="metric-label">Complexity</span>Medium. Revenue-critical flow, navigation and payment state risk, bounded frontend scope, and one cutover.</p>

              </div>

              <div class="case-facts">

                <section><h4>Why it was hard</h4><p>A revenue-critical Angular checkout had to become React without losing customer progress or transaction completion through refreshes, back navigation, or payment-window closure.</p></section>

                <section><h4>How risk was controlled</h4><ul><li>Persist early checkout state in local storage.</li><li>Use a reopenable payment popup.</li><li>Release through one controlled cutover.</li></ul></section>

              </div>

              <p class="case-ai"><span>AI-assisted?</span>Yes.</p>

            </div>

          </details>

        </li>