{"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%3Aw4-325-inventory-audit%3Av1","links":{"self":"https://api.sb/v1/founding-hypotheses/fh%3Aw4-325-inventory-audit%3Av1","canonical":"https://api.sb/founding-hypotheses/fh%3Aw4-325-inventory-audit%3Av1","pool":"https://api.sb/v1/founding-hypotheses"},"foundingHypothesis":{"id":"fh:w4-325-inventory-audit: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/w4-325-inventory-audit","stableHash":"wcc:w4:325:inventory-audit:v1"},"problem":{"slotStatement":"Cycle counts in chemical plants constantly drift from ERP book quantity because tank-farm volumes, partial drums, reactor WIP, and quarantine lots are reconciled from paper tickets, DCS readings, and weigh-scale logs that no one can tie together fast enough before auditors arrive, so controllers write off variances they cannot explain."},"approach":{"oneSentence":"An AI service that ingests ERP stock snapshots, tank-gauge and weigh-scale telemetry, lot genealogy, and count-sheet photos, then produces a review-ready reconciliation packet for each cycle count — every variance traced line-by-line to its source document so the controller and external auditor can sign off without a spreadsheet rebuild."},"customer":{"icpShape":"North American specialty and fine-chemical manufacturing plants ($100M–$1B revenue, 1–5 sites) running SAP or Oracle ERP, where the buyer is the VP of Supply Chain or Plant Controller and the daily user is the Inventory Control / Materials Manager running cycle counts and reconciling WIP against ERP.","beachheadShape":"EarlyAdopterJTBD: specialty chemical plants preparing for annual financial audit or SOX inventory attestation where cycle-count variances above materiality thresholds are already triggering auditor findings."},"archetype":"startup-archetypes.org.ai/AIService-MoneyOnDelivery","beachhead":"EarlyAdopterJTBD: specialty chemical plants preparing for annual financial audit or SOX inventory attestation where cycle-count variances above materiality thresholds are already triggering auditor findings.","competitors":{"substitutes":[{"name":"Excel + SAP MI07 cycle-count transactions run by the plant inventory clerk","category":"status-quo"},{"name":"RF Smart / Vinculum / Tecsys inventory modules bolted onto ERP","category":"incumbent"},{"name":"Big 4 audit staff manually sampling and recounting during year-end fieldwork","category":"human alternative"},{"name":"ChatGPT Enterprise / Copilot used ad-hoc by controllers to summarize variance spreadsheets","category":"AI-native horizontal"}]},"studioThesis":"T-HSA","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 primary-source linkage (each variance line traceable to tank gauge, scale ticket, or lot record vs. aggregated totals only)","yAxis":"Native fit to chemical-specific inventory objects (tank heels, batch WIP, quarantine/retest lots, density-corrected volumes)","winningQuadrant":"High primary-source linkage + High chemical-native fit: every cycle-count variance arrives with a traceable record pointing at the specific DCS reading, scale ticket, or COA that caused it, and the reconciliation understands tank heels and density corrections out of the box — uncopyable by horizontal tools within 6 months because it requires DCS/historian connectors, lot-genealogy mapping, and chemical-inventory domain rules.","loservilleEscape":true,"loservilleQuadrant":"Low linkage + Low chemical fit: ChatGPT Enterprise summarizing a variance spreadsheet pasted in by the controller — it produces a plausible narrative but cannot point at the source ticket, does not know a tank heel from finished goods, and auditors reject the output as unsupported."}},"unmetRequirements":[],"pricingArchitecture":"usage-meter"},"actions":{},"options":{},"relationships":{"runtimeUnit":"https://api.sb/v1/runtime-units?startupRef=startup%3Afh%3Aw4-325-inventory-audit%3Av1","brand":"https://api.sb/v1/brands?startupId=startup%3Afh%3Aw4-325-inventory-audit%3Av1","listing":"https://api.services/listings?foundingHypothesisRef=fh%3Aw4-325-inventory-audit%3Av1","cell":"https://api.sb/v1/cells/work-contexts.org.ai/w4-325-inventory-audit","thesis":"https://api.sb/v1/theses/T-HSA"},"meta":{"level":"L0","scopes":[]},"user":{"requestId":"a057596a7a4e994c","edgeLocation":"a057596a7a4e994c","geo":{"country":"US"},"ua":{"browser":"Claude"}},"references":{"total":0,"limit":25,"page":1,"links":{"self":"https://api.sb/v1/founding-hypotheses/fh%3Aw4-325-inventory-audit%3Av1/references"},"items":[]}}