# Commercial priorities after live statistical and scheduling comparisons

The catalog contains 41 software/business dossiers, all 372 family assessments and 719 inventoried manuscripts. The leading hypothesis remains one shared evidence and model-assurance platform. There are 27 runnable utilities, 16 documentary contracts and 7,727 passed checks in 36 saved reports. These are research and integration evidence; no buyer demand, profitable deployment or independent proof acceptance has been established.

## Build and earning queue

| Priority | Workflow and candidate buyer | Concrete capability now | Evidence needed before a commercial claim |
| --- | --- | --- | --- |
| 1 | Research evidence/model assurance for technical reviewers | Pinned source/hash reports, withdrawal dependencies, explicit model and implementation obligations | Performed expert comparison, useful recurring decisions and repeatable preparation/support cost |
| 2 | Aggregate-data law/constraint review for statistical engineering teams | Exact tiny laws, fixed-event comparison, explicit bound conditioning; live SciPy agreement | Valid actual experiment design, material errors caught and advantage over existing practice |
| 3 | Allocation certificates and flexibility review for operations teams | Existing-solver matching witness checks, attaining cardinality bounds and tiny counts | Real mandatory constraints, independently accepted certificates and actionable delivery value |
| 4 | Bounded-flow scenario/model review for planning teams | Binary signed-bound reduction, tiny exact arc-vector diagnostics and checked existing-solver proposals | Useful scenario law/units, practical large-support backend and planning decisions improved |
| 5 | Scientific/quantum numerical assurance for domain teams | Exact geometry, correlation, spectral and aliasing controls | Domain fidelity, numerical/program bridges and buyer budget |
| 6 | Sparse permutation framework research for data-access teams | Shared-switch replay, decimal-wire provider, tiny-law and entropy checks | Universal numerical threshold, compact storage, realistic sparse/batched workload and fair-bit/PRG distinction |
| 7 | Three-machine scheduling assurance for narrow unit-job workflows | Direct witness checks, tiny optima/deadline evidence and live CP-SAT adapter | Repeated value beyond existing solver output and accurate real-model correspondence |

These overlap within one platform; do not sum them as independent customers or forecast total revenue from the number of modules. [Complete catalog](opportunities/CATALOG-2026-10-09-v16.md), [family directory](FAMILY-ASSESSMENTS-2026-10-09-v11.md).

## What the new comparisons establish

The public fixed-margin event has exact probability 5/143 under intended conditional independence versus 3/7 under uniform feasible tables. It crosses a declared 1/20 threshold because the law changes. A fresh isolated SciPy 1.18.1 run now agrees across 284 margin pairs/494 observed tables and five fixture cases, with 586 controls. Existing statistical tools already provide the base law; potential value is a traceable law/constraint/event review, not replacing an erroneous RNG. [Updated data dossier](opportunities/004-fixed-margin-data-sandbox/2026-10-09-v4.md), [public report](prototypes/public-contingency-law-review-2026-10-09-v2.md).

The scheduling source supplies a complexity classification and bounded global-description proof with exponent 150020, not a practical speedup. The new tiny reference passes 3,346 controls and finds an exact three-slot schedule where one priority heuristic takes four. OR-Tools reaches the same independently checked optima on all eight generated cases. In one case its FEASIBLE proposal attains an elementary lower bound; in another, OPTIMAL status alone does not fill an exhausted independent reference. Keep the direct scheduler deferred and evaluate only a narrow assurance adapter. [Scheduling dossier](opportunities/009-three-machine-scheduling/2026-10-09-v2.md).

## Direct backends still deferred

The literal general table sampler, accelerated matcher and perfect-matching FPRAS have extreme schedules or prerequisites. The switch-chain claim concerns a specified complete-host kernel and all steps; thin-tree/spectral constructions have unselected thresholds or expensive schedules. Uniform k-server activation can exceed a deployment horizon, and language-class derandomization does not preserve arbitrary random outputs. The current JSON permutation provider loses the measured storage comparison against a packed array, while the universal source sweep constant is unselected. Tiny finite references must not be relabeled as those unrestricted source backends. [Feasibility evidence](FEASIBILITY-2026-10-09-v11.md).

The positive-basis algebra audit remains a low-confidence specialist opportunity: an existing CAS supplies much of the underlying computation and no recurring buyer decision is shown. Identifiability/uniqueness theorems and asymptotic physics claims also require separate numerical, measurement, hardware and finite-instance bridges.

## Price and delivery hypotheses

The shared review pilot uses an illustrative AUD 10,000 integration fee and AUD 180 per specialist hour. At 35 hours direct labor is AUD 6,300, leaving AUD 3,700 before sales, support and overhead; 56 hours already cost AUD 10,080. A scheduling module hypothesis is AUD 5,000 with 20 hours of direct labor, leaving AUD 1,400 before other costs; 30 hours exceed the fee. These are assumptions for testing, not quotes, revenue or profitable delivery evidence.

The [ten-case expert packet](prototypes/ten-claim-review-packet-2026-10-09-v1/README.md) is prepared and unperformed. Its eight scope-error cases and two synthetic documentary controls retain their archived thirteen-contract edition, separate from the current sixteen-contract registry. Independently measure material gaps, false alarms, correction effort and preparation time; repeat-reading effects are not report benefit. A recurring subscription requires demonstrated repeated decision value and low support cost. [Current pilot specification](plans/evidence-platform-pilot-2026-10-09-v8.md).

## Next business decision

Continue technical and source reviews autonomously, but keep earning claims conditional. Select a realistic repeated data, allocation, flow or scheduling decision, identify which exact report changes that decision, and estimate actual delivery effort before pursuing a fee. No external outreach, purchase, paid compute, publication or deployment occurred.