Connect AI Spend to Product and Margin

Adoption metrics don't say "profitable"

AI features can look successful on adoption metrics while quietly eroding gross margin. A total-spend dashboard can't answer what finance actually asks: is this feature worth what it costs, and will that hold as usage scales?

• Token totals don't say whether an AI feature is profitable
• No agreed way to weigh cost against the value of a resolved ticket
• ROI math redone by hand every quarter, with no consistent baseline
• A model swap or new rate silently breaks last quarter's numbers

Cost measured against the outcome it produced

Usage is grouped by customer, workflow, feature, and the transaction fields your SDKs and gateway calls already carry, so you measure cost per unit of value, such as cost per resolved ticket.

• Transaction fields flow straight into unit economics
• ROI calibration: value per success and a pre-LLM baseline
• Calibration is versioned, so a change never rewrites past reports
• Unit costs compared directly with operational and revenue metrics

Pick one or two core units

Define one or two core units, such as cost per ticket or cost per customer workspace, set an initial ROI calibration for that transaction type (value per success, and a pre-LLM baseline if one exists), then use model and app breakdowns to find the workflows with the highest margin pressure.

One attribution layer, every cost dimension

Cloptima's attribution layer combines AI usage and business metadata so unit economics can be measured per customer, workflow, and feature. Business-transaction fields are attribution metadata only — they enrich cost analysis but can never override the authenticated tenant identity a request actually belongs to, so ROI reporting can't be spoofed by a caller-supplied field.