Workforce Intelligence Product Pricing

Links a person's GitHub handle, Jira account, and Claude Console identity into one contributor record, so one engineer's AI and human activity rolls up correctly instead of splitting across three logins. Admins can flag bots and manually link or relink a misattributed identity from a Manage Contributors screen – corrections survive future auto-matching instead of getting overwritten.

Pulls Claude Code usage straight from Anthropic's admin API with a ninety-day historical backfill, so a new customer has a populated dashboard on day one instead of waiting a quarter to build a baseline. Token counts, cost, and model version come from the provider directly – it's the customer's own Anthropic invoice, not an estimate.

Adds the enterprise-tier Claude Code analytics surface – org-level OAuth and longer retention – for customers on that plan, closing a gap the standard console connector can't reach.

Pulls daily usage and cost from OpenAI's admin API for organizations that won't deploy OTEL wiring on every machine, with a ninety-day backfill.

One filterable list – by quarter, Jira ID, or contributor – of every work item touched by a human, an AI tool, or both, so a work item user finds what happened without checking Git and Jira separately.

Click any issue and see every human contributor, every AI tool involved, and the compute cost in one read-only panel, answering "who and what actually built this" without leaving the reporting view.

One shared filter model drives every chart and table on a surface at once, so numbers never disagree screen to screen. Drilling into a work item keeps the filter instead of losing your place.

Centralized visibility into AI adoption and the impact AI tools have on efficiency across the organization.

Gives engineering leaders instant visibility into how their AI tools are driving efficiency, comparing cycle times of AI-assisted work to non-AI-assisted work.

Lets admins group contributors into departments or teams, providing an additional layer of granularity for engineering leaders to identify opportunities to improve adoption and efficiency.

An expandable table of spend, adoption band, and cycle time by team, drilling to each contributor, with an "unassigned" row pinned to the bottom so nobody who hasn't been placed on a team yet gets hidden from the count. Also generates visibility into non-engineering AI spend, attributing AI costs to people and departments in finance, product, design, operations, and more.

Provides a single pane of glass view into spend across connected AI tools, without manually aggregating data from multiple reports or AI vendor invoices.

Lets finance and engineering leaders see how AI spend has trended over prior periods, informing budgeting decisions for future periods.

Rolls AI compute cost and cycle times up to the Jira space and epic level – the same structure engineering already plans around – so leaders see where spend concentrates without building a spreadsheet. Connects AI spend directly to an organization's body of work, so engineering leaders can align AI costs with strategic priorities and finance leaders can build a defensible audit trail for AI cost capitalization decisions.