The enterprise case

Stop paying for AI mistakes.

The barrier to generating code has dropped to zero. So has the barrier to burning a budget doing it. Bill-y aligns AI coding spend with engineering discipline, so leadership can see waste, stop runaway loops, and know what the spend actually bought.

The primary pain

The premature-build loop

When it costs nothing to ask, planning gets skipped. A vague instruction goes straight into an expensive reasoning model, the first output misses an unstated requirement, and the developer starts prompting fix it, again and again. Every fix re-sends the whole codebase and history, so the cost per turn climbs while the code goes in circles.

One poorly framed task can spin for hours and burn hundreds or thousands of dollars on a single workstation. And a post-facto cost dashboard cannot fix a behavior it only sees on next month's invoice.

Vague promptno planSkip to buildexpensive modelBroken outputmissed intentFix it, againresend everythingBURNING$214one workstation, one afternoonBill-y sees itrework loop detectedproject · payments-apibuild 71% · design 0% · review 0%flagged while it is still happening
01

Skip the plan

A vague ask goes straight to code generation, with no understanding and no design behind it.

02

The fix-it trap

The output misses the real requirement, so the developer prompts fix it, over and over, unstructured.

03

Context escalation

Every fix re-sends the whole codebase and history through the model. Cost per turn keeps rising.

04

The financial bleed

A single task spins for hours and burns real money on one machine, invisible until the bill lands.

Root cause, not the receipt

From bookkeeping to guidance

Generic cost tools read last month's bill. By then the money is gone and the loop has already run a hundred times. Bill-y works at the edge, where the behavior actually happens.

It attributes every unit of spend to a phase of work as the work happens, so a runaway loop shows up as exactly what it is, a team stuck in build and fix with no design and no review, while there is still something to do about it.

Why leadership buys

One reason each for finance, engineering and security

Hard financial ROI

CFO and Finance

Kill dead-weight spend

Pinpoint the teams and projects stuck in loop cycles, and stop runaway costs before they compound.

Dollars, not tokens

See “$4,200 on the Build phase for Project X this week,” not “150 million tokens.” Spend in language finance can govern.

Engineering discipline, proven

CTO and VP Engineering

Think before you code

Clear evidence of whether teams invest in understanding and design, or jump straight to generation.

Process transparency

An automatic audit trail of how software is actually built across distributed and remote teams.

Enterprise-grade by construction

CISO and Security

Nothing leaves the boundary

Local agents and an on-prem server mean sensitive workflow telemetry never reaches a third-party cloud.

Fits your controls

Deploys through existing device management with centralized policy, in a zero-trust posture.

How Bill-y is different

A category the cost trackers cannot see

Generic cloud FinOpsAI gateways and proxiesBill-y
Data sourceProvider API billsNetwork-layer trafficLocal workflow telemetry
What it measuresTotal dollars spentToken counts and latencyTime and cost per SDLC phase
Primary valuePost-hoc accountingRate limitingBehavioral optimization
DeploymentSaaS onlyCloud proxySecure on-prem device agent

How we build it

A coach, not a camera

Bill-y is built to empower developers, not police them. Analytics is team-level by construction, there is no individual leaderboard, and the same signal that shows leadership where budget bleeds shows a developer when a tool has trapped them in a wasteful loop.

Discipline that helps the people doing the work is the only kind that lasts.

For leadership

“Align AI spend with engineering discipline. See waste, stop runaway loops, and know what the spend bought.”

For developers

“An automated prompt coach. It flags when an AI tool is trapping you in a loop, so you reset, sharpen the prompt, and ship clean code faster.”

Bring discipline to your AI spend

Phase attribution for AI coding, across every major coding tool, on Windows and macOS, entirely on your own infrastructure.

Get started

Get Bill-y

Bill-y is available now for teams coding with AI at scale, on Windows and macOS, with every major coding tool. Tell us a little about your team and we'll get you set up.