The ROI layer for AI engineering.

Every coding-agent run — priced, attributed, and tied to what it shipped. Across the tools your team already runs on, with metadata only.

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Metadata-only by design — prompts never leave your machines.

Output vs. spend

This week

Live

Output Spend

+38% shipped

spike /spʌɪk/ — a sharp rise worth investigating.

Works with the frontier

Integrations

One graph over the stack you already run on.

spikes reads the signals your team already produces — agents, repositories, issue trackers, reviews — and resolves them into one attributed view. No new workflow to adopt, no prompt data collected, nothing to migrate.

What you finally get to see

Three questions, answered every standup.

01 · ROI, observable

Is the spend actually paying off?

Watch cost move against velocity and shipped work, week over week — so you know whether engineers are getting faster, the roadmap is getting closer, and every dollar of agent spend is earning its place.

Open the threads ledger

Threads · this week$257.25

feat/auth-migration

claude-opus

$128.40

fix/rate-limiter

cursor

$42.10

refactor/payments-api

codex

$86.75

02 · Performance, in context

How is the team really using AI?

See which tools each engineer reaches for, on which projects, and how much leverage they get — so reviews and calibration rest on evidence, not anecdotes.

03 · Spend, governed

Where is spend leaking out?

Surface usage that falls outside sanctioned work before it compounds — company keys on side projects, drift across teams, tools no one is tracking.

personal-blog-redesign

claude-opus · weekend

Off-book

Attribution engine

Many runs in. One ledger out.

Scattered agent activity collapses into spend you can actually read — resolved by engineer, project, and model, and classified by the kind of work it was: feature, fix, refactor, or spike.

Metadata-only by designWork auto-classifiedDeep-links to PR & issueWeek-over-week velocityPersona briefingsModel-level pricing

How it works

Scattered runs in. A standup out.

Capture

A local collector logs each run — model, tokens, cost, repo. Metadata only; prompts never leave the machine.

Attribute

Every thread is tied to an engineer, a project, and the PR or issue it actually moved.

Brief

A persona-scoped standup — spend, velocity, and what shipped — ready before the meeting starts.

Privacy is the default. spikes captures metadata only — model, tokens, cost, repo. Raw prompts and responses never leave your machines.

Watch your next spike.

Measure your spend. Understand the impact.

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