Use case / Engineering
Connect AI coding spend to shipped work.
Join AI consumption with GitHub and Linear so product and engineering leaders can compare coding capacity with delivery throughput, cycle time, and completed work, then make better allocation decisions.
Who it is for
CTO
Own engineering capacity, delivery efficiency, and the decisions that determine where additional AI spend improves product output.
Arbitir joins AI coding consumption with GitHub and Linear delivery signals so engineering leaders can evaluate capacity against throughput, cycle time, and shipped work.
- “Which teams and workflows consume the most AI capital?”
- “Which tools and models are driving the most AI costs across teams?”
- “How can I enforce controls to stay within budget?”
- “How does AI intensity sit alongside PR throughput, issue completion, and cycle time?”
- “Where does additional capacity appear to help, and where does it stop helping?”
- “Which practices should be tested and scaled across engineering?”
Outcomes
What an engineering team is measured on.
No fixed vocabulary for delivery. Each of these is a definition you write against GitHub or Linear.
| Outcome | Horizon | Reads at | 90 days | AI cost per unit |
|---|---|---|---|---|
| Merged pull requestsyour definition | Proximal output | same day | 1,284 | $66 per pull request |
| Issues completedyour definition | Proximal output | same day | 2,106 | $40 per issue |
| Median cycle timeyour definition | Proximal output | +7d | 3.1 days | not a unit cost |
| Change failure rateyour definition | Financial outcome | +30d | 4.2% | not a unit cost |
An economic unit, not a performance measure. The product will not rank people on it.
Value model
Connect capacity to the function's operating outcomes.
Each use case runs the same Arbitir loop: observe capital, define output, measure what changes, move the allocation, and scale what works.
- 01
GitHub delivery signals
Put AI spend alongside pull requests, review activity, merged work, and repository context.
- 02
Linear execution signals
Connect capacity to issue progression, cycle time, completed work, and product initiatives.
- 03
Team-level economics
Compare spend and delivery signals across teams, roles, tools, and use cases.
- 04
Test before automating
Use controlled allocation changes to build evidence before recommending broader budget shifts.
How Arbitir helps
Developer AI economics
Track AI consumption by developer, team, tool, model, and project to understand where coding-assistant and model capacity is actually being consumed.
Delivery outcome mapping
Connect AI investment with GitHub and Linear signals such as pull requests, cycle time, issue completion, review activity, and shipped work to establish comparable productivity measures.
Capacity recommendations
Identify teams, workflows, or spend levels where additional AI capacity appears productive, where returns flatten, and where budget could be reduced or tested differently.
Engineering outcomes
Use GitHub and Linear as the delivery context.
AI coding usage alone is not the target metric. Arbitir puts usage next to the work engineering already records, then helps teams test whether incremental capacity changes delivery output.
Code delivery signals
Connect provider spend and usage to repository, team, and delivery context.
- Pull requests
- Merged work
- Review activity
- Repository context
Product execution signals
Put AI capacity alongside issue flow and product work without reducing productivity to token volume.
- Issue completion
- Cycle time
- Initiative progress
- Team throughput
Start allocating AI spend toward what works.
We grant access to a select number of companies each month, based on fit. Describe how AI spend is governed today and we will respond within two business days.