A contribution-margin workspace for AI-native software that reconciles provider usage, feature traces, customer identity, subscription revenue, credits, refunds, expansion, contraction, and churn into versioned account economics.
Genesis score7.38/10
Make Valuetrace real.0/500
500 more votes and Valuetrace is authorized for build.
0%500 to authorize
Backing is the vote. When an idea crosses 500, we pull it into the build pipeline and ship it for real — the votes decide what gets built next, not an editor.
The opportunity
14+Spend providers cited
0Direct revenue-join peers found
2Verified proposed APIs
The case
Current tools validate provider-spend attribution, traces, evaluation, and quality-adjusted pricing, while no product in the supplied scan joins AI cost to account-level expansion and churn. Valuetrace's immediate value is contribution margin, not causal attribution: an AI interaction preceding expansion or churn does not prove it caused the outcome. The product should preserve cost sources, allocation policy, revenue state, identity confidence, cohort definitions, and missingness, then use experiments or qualified analysis for causal claims.
Who pays — and why
The founder, finance, data, platform, or product leader at an AI-native SaaS company managing gross margin and feature economics.
What it unlocks
A reconciled usage ledger with provider, model, feature, trace, customer, time, units, retries, cache, list or contract rate, credits, currency, and invoice adjustment
An account economics ledger with subscription, usage revenue, discount, tax, refund, dispute, expansion, contraction, churn, support and allocated infrastructure policy
Versioned contribution-margin and retention cohorts plus experiments and scenarios that keep observation, correlation, attribution, and causal evidence separate
How Genesis scored it
7.38across seven criteria
9
Asymmetric leverage
Ingestion, reconciliation, cohorts, and scenarios scale through software.
8
Productive tension
Useful unit economics must preserve billing truth, allocation policy, identity uncertainty, privacy, and causal limits.
5
Convergence
Several adjacent signals support the join.
Why it scored well
Live spend tooling, traces, outcome pricing, a clear founder buyer, and an unoccupied account-economics join support the product.
What's holding it back
The join is copyable, identity and allocation are difficult, benchmarks create privacy risk, and observed sequences cannot prove retention causality.
Signals detected5 sources crossed
SignalSuperPenguin research carried in Genesis
SignalLangfuse research carried in Genesis
SignalTouchmark research carried in Genesis
SignalHelicone status research carried in Genesis
Signalmarket scan carried in Genesis
Direction briefvaluetrace.md
valuetrace.md
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