MarginSplit
A per-customer, per-feature margin workspace for AI software teams that joins tagged model usage to recognized revenue, explains allocation and recommends bounded changes with approval and outcome measurement.
The supplied research confirms that model-observability products can track user-level cost but do not necessarily join it to billing revenue. It also identifies a direct early-stage competitor explicitly offering per-customer profit and loss, contradicting the original empty-market claim. MarginSplit therefore needs a sharper wedge: feature-level allocation, reproducible margin policy, scenario comparison, approved enforcement and actual post-change reconciliation. A dashboard alone is thin, while autonomous model downgrades or customer caps would be unsafe.
The founder, finance lead, product leader, or engineering owner at an AI software company that cannot explain gross margin by customer and feature.
One cross-reference and five inbound links provide unusually strong internal convergence.
Margin optimization must avoid degrading quality, fairness, contractual entitlements or customer trust.
Observability and billing data exist, but the record does not establish a newly removed barrier.
Multiple neighboring ideas and inbound links reinforce the pain, and the customer-feature grain produces actionable unit economics.
A direct competitor exists, revenue and cost allocation are messy, timing is not isolated, and the useful enforcement layer may require substantial human and integration work.
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