Most teams are burning a large slice of their AI budget on spend no one can see. AskLedger normalizes cost across every provider, attributes it to the team, app and use case, finds the waste, and then proves what you actually saved against a signed baseline.
Every recommendation is deterministic and explainable, based on your real usage, pricing and policy data rather than a black-box guess. An AI may summarize the findings; the math is always traceable.
Seats with no qualifying use in 45 days. Downgrade or remove at renewal.
Premium models running low-complexity tasks. Test and route to a cheaper approved model.
Repeated calls with the same failure class. Fix the workflow, cap automatic retries.
Multiple subscriptions serving the same role. Consolidate the vendor footprint.
Stable output despite large repeated context. Use retrieval, caching or compression.
Abnormal agent action rates, or high cost with weak outcomes. Cap, redesign, or retire.
OpenAI, Anthropic, Bedrock, Azure, Google, your gateways and your invoices all price differently. AskLedger normalizes tokens, requests, tool calls and subscriptions into a single cost model, then attributes every dollar to a person, team, application and use case, so "unallocated AI spend" stops being a line on your budget.
"Potential savings" is easy to claim and impossible to trust. AskLedger runs a closed loop and publishes each realized saving with the assumptions, the math, and the signed evidence behind it, so a saving survives a finance review.
Normalized cost and business volume, before any change.
The calculation, projected saving, confidence and effort.
Owner, risk review and the expected change window.
Config, routing or license change, linked to a signed event.
Post-change period vs. baseline: the realized saving, with evidence.
North-star metric: verified customer savings. We count savings actually realized, not recommendations generated or potential savings estimated.
One SaaS company, June to July. They moved two light, high-volume workloads off a premium model onto cheaper same-family models and left the rest alone. Here is the before and after, and the signed proof that comes out.
| Workload | June (before) | July (after) |
|---|---|---|
| support-bot | gpt-5 | gpt-5-nano |
| doc-summarizer | gpt-5 | gpt-4o-mini |
| internal-copilot | gpt-4o | gpt-4o |
Here is the part that matters: the saving is signed against the June baseline, and anyone recomputes it from the baseline and the proof with the public key alone. Inflate the headline number and verification fails on the spot, the signature and the math both break. That is the difference between a proven saving and a claimed one. Figures are illustrative, repriced from sample usage exactly the way a real provider bill is. Reproduce every number yourself: npm run demo:savings
Connect two or three production applications, one billing source and your identity data. We deliver a normalized baseline, your top savings recommendations, coverage gaps, and a signed evidence sample, so you see real, attributable value before committing to anything.