You can already see what your AI costs. Skalpel shows what it produced — and what it can't.
The independent, cross-vendor auditor of AI spend. Built to reconcile your spend to the provider invoice, tie it to real outcomes — cost per merged PR, per resolved ticket — and show the share it honestly can't.
“What did all that AI spend actually produce — per merged PR, per resolved ticket, net of what it cost, across all our vendors, at a confidence I can defend in a board meeting?”
| Anthropic | $48,210.18 | $48,955.40 |
| OpenAI | $21,740.06 | $21,512.88 |
| Bedrock | $9,330.51 | $9,401.20 |
By construction, the verified, inferred, and unattributed shares always sum to the full provider-billed invoice, enforced in code. The unattributed slice is shown full-size: never hidden, never rounded away.
You can invoice the spend. You can't defend the return.
Every company now spends real money on LLMs — usually across several providers at once, and AI spend has only now crossed the line into something a board asks about. Three facts leave you unable to answer for the spend:
The spend is real, multi-vendor, and growing — but scattered.
Each provider has its own dashboard. Nothing ties them together, or to your own results.
Seeing the cost is already a solved, commoditized problem.
Every observability and cloud-cost tool can show “this API call cost $0.04.” That's table stakes — Skalpel never leads with it.
Seeing the return is unsolved — and what passes for solving it is dishonest.
Competitors round to 100% and dress a guess up as a fact. No one — us included — has clean causal proof, so the honest move is to show the share you can't attribute, not bury it.
Two layers on one reconciled foundation — and an honest remainder on top.
Reconcile the spend
Meter AI usage across providers and reconcile it to the actual provider invoice within a tight tolerance (target ± 2%, validated per pilot). “Reconcile” in the accounting sense: tie the measured number out to the authoritative bill, not estimate it from logs. Built to be finance-grade — and by design, a dollar figure isn't meant to render until a reconciliation passes.
Attribute to outcomes
Designed to connect reconciled spend to real business outcomes — starting with merged PRs and resolved tickets — at team and workflow granularity. Never at the individual-employee level; that line is non-negotiable.
Show what you can't explain
Every number is tagged verified or inferred and the two are held apart. The share that can't be honestly attributed is shown in full — a mandatory unattributed bucket, not a rounding error.
A number that survives the board and the auditor — because it shows you its own limits.
Every other tool selling “AI ROI” rounds to 100% and hands you a guess dressed as a fact. Skalpel won't. Two guarantees are enforced in code, not promised in a footnote:
Verified and inferred can never blend.
Every figure is typed at the source — a closed chain of shared identifiers, or an inferred link — and the two are held apart in code, not by a disclaimer. No one can quietly average a guess and a fact into a rounder, prettier total.
What it can't explain stays on screen.
The exact share Skalpel can't honestly attribute is shown in full — never rounded away, never buried under “other.” A tool that claims to explain all your AI spend is lying; this one shows you the gap — the most honest number on the page.
We looked at eleven tools selling “AI ROI.” Not one is honest about what it can't see:
- No reconciliation tolerance to the invoice
- No line between a measured number and a guessed one
- No honest remainder — they round to 100% by construction
And it compounds: the cross-vendor reconciliation graph gets more accurate — and more switching-costly — every invoice cycle.
Numbers built to look complete. This one's built to be defensible.
The board-defensible numbers a cost dashboard can't produce.
Cost per merged PR / resolved ticket
Reconciled spend over real shipped outcomes — the headline ratio. Target tier, wired when the §16 connecting step lands.
Net-of-cost value (estimated)
Reconciled spend subtracted from the value credited to it — reported as a wide-banded estimate, with its assumptions shown.
Low-yield hotspots
Where AI spend runs ahead of the outcomes it's credited with — the places to look first.
Model-mix efficiency
Where you're paying for a frontier model on a task a cheaper one would close.
AI-leverage by team
The share of shipped work that was AI-assisted, at team and workflow granularity.
AI spend as a share of loaded team cost
The comparison the board actually asks for, per team — reported as an estimate, with its assumptions shown.
API-first teams with real, multi-vendor AI spend.
- You build on LLMs — direct API keys and/or Bedrock, Vertex, Azure OpenAI — not just chat seats.
- AI spend is material and multi-vendor — roughly $10k–$1M+/month across two or more providers (or adding a second) — i.e. large enough that the number has to be defensible, not estimated.
- Someone internal has been asked “what's our AI ROI / where's the waste” and couldn't answer cleanly.
The buyer is whoever owns AI spend and its return — Head of Engineering / Platform / Infra, a CTO or technical co-founder, an emerging FinOps-for-AI owner, sometimes a Finance partner handed the AI-cost question.
Not a fit (yet): seat-only orgs with no API keys; single-vendor with trivial spend; pre-product with nothing to attribute.
Put your AI spend through an independent reconciliation.
We're onboarding a small set of design-partner teams. Join the waitlist, or book a 20-minute pilot conversation — a candid look at how you reconcile and defend your AI spend today, not a pitch.
Book a pilot call