{"api":{"name":"api.sb","description":"Business-as-Code surface for Startups.Studio","home":"https://api.sb","docs":"https://api.sb/docs","version":"1.0.0"},"$context":"https://api.sb/$context","$type":"FoundingHypothesis","$id":"https://api.sb/founding-hypotheses/fh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1","links":{"self":"https://api.sb/v1/founding-hypotheses/fh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1","canonical":"https://api.sb/founding-hypotheses/fh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1","pool":"https://api.sb/v1/founding-hypotheses"},"foundingHypothesis":{"id":"fh:w3-53-7065-dwa-using-relevant-knowledge:v1","lens":"AIService","type":"founding-hypothesis","click":{"rubricScores":{"C8_lensFit":1,"C7_magicLensFit":1,"C4_competitorHonesty":1,"C6_crossSlotCoherence":1,"C1_customerSpecificity":1,"C2_problemFrictionRealism":1,"C9_killCriteriaAttestability":1,"C3_approachEngineCoverability":1,"C5_differentiationLoservilleEscape":1},"upperRightLoserville":true},"cellRef":{"id":"work-contexts.org.ai/w3-53-7065-dwa-using-relevant-knowledge","stableHash":"wcc:53-7065:dwa-using-relevant-knowledge:pure-info:w3:v1"},"problem":{"slotStatement":"Stockers and order fillers hit hundreds of micro-decisions per shift — wrong planogram slot, mislabeled backroom tote, substitution rules for out-of-stocks, hazmat/age-restricted handling, expiry rotation — and today they either guess, radio a supervisor (who is busy), or leave the task parked, producing shelf gaps, mispicks, and customer escalations that leadership can't trace back to the decision point."},"approach":{"oneSentence":"An AI service embedded in the handheld scanner/voice-pick headset that, when a stocker hits a rule-ambiguous situation, retrieves the store's own SOPs, planograms, and substitution matrices and returns a short spoken answer plus a traceable record of which policy clause was cited, who asked, and what they did next — so every edge-case resolution is review-ready for ops and loss-prevention."},"customer":{"icpShape":"Regional grocery and big-box retail chains (50-500 stores, e.g., regional supermarket banners and hardline retailers) where the buyer is the VP of Store Operations and the daily user is the store-level Stocker/Order Filler and their Front-Line Supervisor","beachheadShape":"EarlyMajorityWorkflow: 50-300-store regional grocery chains running shelf-replenishment and backroom pick workflows with high seasonal turnover"},"archetype":"startup-archetypes.org.ai/AIService-MoneyOnDelivery","beachhead":"EarlyMajorityWorkflow: 50-300-store regional grocery chains running shelf-replenishment and backroom pick workflows with high seasonal turnover","competitors":{"substitutes":[{"name":"Radio/phone escalation to the on-duty supervisor or department lead","category":"status-quo"},{"name":"Zebra Reflexis / Workcloud task-management + static SOP PDFs on the handheld","category":"incumbent"},{"name":"ChatGPT / Copilot on a break-room tablet","category":"AI-native horizontal"},{"name":"YOOBIC / Axonify frontline training & checklist apps","category":"adjacent vertical"}]},"studioThesis":"T-DEV-INFRA","killThreshold":{"K":8,"M":30,"N":7,"rubricItemSet":["C1_customerSpecificity","C2_problemFrictionRealism","C3_approachEngineCoverability","C4_competitorHonesty","C5_differentiationLoservilleEscape","C6_crossSlotCoherence","C7_magicLensFit","C8_lensFit","C9_killCriteriaAttestability"],"verdictPolicy":"all-load-bearing-pass-and-overall-ge-X","loadBearingItemSet":["C1_customerSpecificity","C2_problemFrictionRealism","C3_approachEngineCoverability","C4_competitorHonesty","C5_differentiationLoservilleEscape","C6_crossSlotCoherence"],"verdictPolicyVerbatim":"KILL unless every load-bearing rubric item passes per workbook AND overall pass-rate ≥ 7/9 (CASCADE.md §4 Stage 9 commit threshold)."},"lifecycleState":"Active","differentiation":{"twoByTwo":{"xAxis":"Depth of integration with THIS chain's live planogram, SOP, and substitution data (generic answers ↔ store-specific, SKU-aware answers)","yAxis":"Per-decision auditability (no record of why the stocker did X ↔ every resolution links to the cited policy clause, user, timestamp, and outcome)","winningQuadrant":"High store-specific grounding + high per-decision auditability: answers cite the chain's own planogram/SOP and leave a traceable record ops and LP can review by SKU, aisle, or associate","loservilleEscape":true,"loservilleQuadrant":"Generic answers with no audit trail — occupied today by ChatGPT on a break-room tablet (no planogram grounding, no logged citation) and by radio escalations (no written record of what rule was applied), which is exactly why shelf-gap root-cause analysis fails today"}},"unmetRequirements":[],"pricingArchitecture":"usage-meter"},"actions":{},"options":{},"relationships":{"runtimeUnit":"https://api.sb/v1/runtime-units?startupRef=startup%3Afh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1","brand":"https://api.sb/v1/brands?startupId=startup%3Afh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1","listing":"https://api.services/listings?foundingHypothesisRef=fh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1","cell":"https://api.sb/v1/cells/work-contexts.org.ai/w3-53-7065-dwa-using-relevant-knowledge","thesis":"https://api.sb/v1/theses/T-DEV-INFRA"},"meta":{"level":"L0","scopes":[]},"user":{"requestId":"a057599928e8994c","edgeLocation":"a057599928e8994c","geo":{"country":"US"},"ua":{"browser":"Claude"}},"references":{"total":0,"limit":25,"page":1,"links":{"self":"https://api.sb/v1/founding-hypotheses/fh%3Aw3-53-7065-dwa-using-relevant-knowledge%3Av1/references"},"items":[]}}