AI GOVERNANCE

Govern the spend. Not the people.

Your AI spend is climbing and someone has to answer for it, without surveilling the people behind it. EngLedger surfaces the few exceptions worth acting on, dollars attached, with reporting kept honest and team-level by default.

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This period · the exception ranked first
$2,480/mo
correlational · this period
evidence
High-cost models are carrying work cheaper ones could. Ranked first by impact: the sessions behind it and the next best action are attached to the line.

Governance here is a short ranked list of lines like this: dollars attached, the caveat kept on the number, not a wall of charts. You act on the top one and move on.

BUILT FOR
FintechHealthtechGovtechDefenceRegulated SaaSScale-ups
What needs attention this month

Ranked issues with dollars attached,
not walls of charts.

01
Model mix
When high-cost models carry work cheaper ones could, the exception states the headroom in dollars per month.
02
Reliability drag
Spend burned on failed requests and retries, surfaced as a cost rather than buried in an error-rate chart.
03
Cache waste
Shallow cache reuse costs real money at volume. The exception shows what better reuse is worth.
04
Unattributed spend
Spend not tied to delivery is listed on its own line: the honest remainder most tools hide, with a next best action to shrink it.
Worked end to end · the exception ranked first
$2,480/mo
est. headroom · correlational
evidence
Long-context summarisation is running on a top-tier model where a mid-tier model returns comparable output.
1,240 summarisation sessions, last 30 daysmeasured
61% on the top-tier modelmeasured
Comparable quality on sampled rerunsmodelled
// Next best action
Route long-context summarisation to the mid-tier model;
review the three highest-cost sessions first.

The $2,480 is an estimate from observed usage, not a guaranteed saving, the caveat stays on the number, all the way to the decision.

Exceptions4 open
High-cost model mix$2,480/mo headroom
Retry burn on failed requestsflagged
Shallow cache reusereview queued
Spend not tied to delivery14% of total
// Next best action
Shift long-context summarisation to a
lower-cost model. Review affected sessions →
Govern responsibly

Numbers you can defend, privacy you can prove,
and an audit trail behind both.

01
Measured, modelled, correlational: labelled, never blended
Observed comparisons on your own data lead the value story. Assumption-based estimates sit behind a toggle, labelled modelled. When a finding is correlational, the report says so. No vendor maths presented as fact.
02
Team-level by default, individual detail opt-in
Org and team views come first. Individual detail is opt-in and framed as coaching, and every person has a page showing exactly what their org can see, a stance that fits works-council and privacy-regime obligations rather than fighting them.
03
Audit trail and read-only access
An append-only activity log and full change history: every classification and override carries who changed it, when, and why. EngLedger ingests no source code, diffs or filenames and connects through read-only scoped OAuth. Numbers that survive an audit, not decorate a slide.

New here? See how AI metering works. The full detail of what EngLedger stores and what it never sees is on the security and privacy page; the governance model (exceptions, privacy and evidence posture) is documented in the docs.

Governance postureAudit-ready
Audit trail & change history✓
No source-code ingestion✓
Read-only scoped access✓
Individual detailopt-in
// Labelling
Measured leads. Modelled stays behind a toggle.
Correlational findings say so on the number.
The governance reports

Who is using AI, how well,
and where it needs attention.

🧑‍🤝‍🧑
People & Adoption
Adoption, team and per-person usage, and MCP adoption. Team-level first, with individual detail opt-in rather than default.
🩺
Operations & Health
Error rates and latency, cache ROI, and model breakdown. The operational side of the same spend, for the people running the tooling.
Looking for spend, forecast and the AI Tax? See AI spend →
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Govern your AI spend. Without watching your people.

Connect your tools and read your first governance readout today.

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