Return on spend
129×
AI productivity measurement
Token volume measures what AI consumes. It says nothing about what your team ships. Ship Math traces one agent or MCP call all the way to recovered engineering dollars. Move the sliders and watch the chain resolve. It loads with a real-shaped ticket, ENG‑4821, a COBOL premium routine ported to Java.
Instrument every call and tag it to what it worked on. Then bind the cluster to the ticket it advanced and stamp the ticket's story points across it. Each call gains an outcome and a size.
Ask several models the manual baseline: how long does this ticket take with no agent help? Take consensus, scored for confidence. Subtract the assisted time to get hours saved.
Convert hours saved to loaded engineering cost, and price the token spend. The chain closes on dollars recovered per dollar spent. Call it ratiomaxxing if you want. The ratios are the whole point.
Ship Math gives you the full chain. Tag every call. Bind it to the ticket. Baseline the manual hours. Credit the calls that earned the close. Bank the result in dollars. The calls that move tickets are the ones retrieving verified context, which is why grounded context recovers more hours per call than raw generation volume. CoreStory is that context layer, and it is the reason agent task resolution improves by 44% with the right context in place.
Figures are illustrative and default to a representative ticket (ENG‑4821). The return-on-spend multiple divides recovered dollars by token cost alone, which is why it reads high. Add your platform and license costs to the denominator for a full picture. The ranking it produces across tickets and call types holds either way.