Sample report · synthetic demo data

The AI Spend Snapshot, un-gated.

This is the exact Snapshot a customer sees, computed live on Meridian Software, our synthetic 420-person demo company. Every dollar carries a proof level, and every finding opens its evidence: inputs, formula, and the rules version that produced it.

Meridian is deliberately untidy, so the audit has something to show. A typical audit recovers 10 to 15% of seat and subscription spend, not what you see here.

AI spend, month ending 30 Jun 2026Countedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.
$47,297
the last complete month in this dataset
Paid-seat utilizationCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.
64%
208 of 323 paid seats active
Recurring recoverable /moCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.
$6,361
open findings, each dollar counted once
Spend by categoryCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.
$9K
seats · $35K usage · $3K other · $0 infra
Top findings by dollar
  • OpenAI API spend spiked the week of 2026-05-11
    criticalCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    OpenAI API spend spiked the week of 2026-05-11

    criticalCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.$9,214

    Proof level

    Counted — read straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    Source of record

    Spend facts from card, invoice, and usage-API sources

    Inputs

    Z score
    34.75
    Week iso
    2026-05-11
    Weekly cents
    1,226,404
    Baseline weeks
    8
    Sigma threshold
    2.5
    Baseline mean cents
    305,006

    Formula

    Δ = weeklyCents − trailing-8-week mean

    Result: $9,214/mo

    Source rows

    Rules v2.0.0 · methodology 2026.07. Thresholds are editable in Settings → Assumptions.

    $9,214
  • Teams using Cursor heavily merge 21% more PRs
    criticalEstimatedworked out from assumptions you can see and change. Always shown as a range, never a single number.

    Teams using Cursor heavily merge 21% more PRs

    criticalEstimatedworked out from assumptions you can see and change. Always shown as a range, never a single number.$9,060

    Proof level

    Estimated — worked out from assumptions you can see and change. Always shown as a range, never a single number.

    Source of record

    Team usage intensity + delivery telemetry (PRs merged)

    Inputs

    Cap
    0.5
    Metric
    prs_merged
    Window
    As of: 2026-06-30 · Month start: 2026-06-01
    Delta pct
    0.2088
    Tool slug
    cursor
    Threshold
    0.1
    Assumptions
    Quality adjustment: 85% · Hours per PR (baseline): 6 · Verification tax: 15% · Loaded cost / eng-hour: 110
    Cohort sizes
    Low-AI cohort: 15 · High-AI cohort: 43
    Low band mean
    18.2
    High band mean
    22
    Min cohort size
    8
    Low band monthly volume
    91

    Formula

    modeled = deltaPct × lowBandMonthlyPRs × hours_per_pr_baseline × loaded_cost_per_eng_hour × quality_adjustment × (1 − verification_tax_pct)

    Editable assumptions — the band recomputes live

    $7,248 – $10,872 /mo (midpoint $9,060) — a modeled projection is a range, never a point.

    Source rows

    ⚠ self-selection: teams chose their tools — correlation, not proof

    Rules v2.0.0 · methodology 2026.07. Thresholds are editable in Settings → Assumptions.

    $9,060
  • ChatGPT Enterprise renews in 45 days with 62% of paid seats idle
    mediumCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    ChatGPT Enterprise renews in 45 days with 62% of paid seats idle

    mediumCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.$1,860

    Proof level

    Counted — read straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    Source of record

    Contract terms (settings) + seat roster + active-day telemetry

    Inputs

    Renews on
    2026-08-14
    Tool slug
    chatgpt
    Total seats
    100
    Active seats
    38
    Active share
    0.38
    Days to renewal
    45
    Seat snapshot as of
    2026-06-30
    Utilization basis
    usage_active_days_30d
    Utilization floor
    0.7
    Renewal window days
    60
    Monthly seat cost cents
    300,000

    Formula

    trueDown = (1 − activeShare) × monthly seat cost at latest snapshot

    Result: $1,860/mo

    Source rows

    Rules v2.0.0 · methodology 2026.07. Thresholds are editable in Settings → Assumptions.

    $1,860
  • 62 unused ChatGPT Enterprise seats
    mediumCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    62 unused ChatGPT Enterprise seats

    mediumCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.$1,860

    Proof level

    Counted — read straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    Source of record

    Vendor seat roster (seat_facts snapshot) via connector sync or CSV import

    Inputs

    Inactive days
    60
    Dormancy rungs
    Dormant 30 days: 62 · 60 days: 62 · 90 days: 45 · Never used: 0
    Monthly cost cents
    186,000
    Affected seat count
    62

    Formula

    recoverable = Σ monthlyCostCents over inactive active seats

    Result: $1,860/mo

    Source rows

    Rules v2.0.0 · methodology 2026.07. Thresholds are editable in Settings → Assumptions.

    $1,860
  • 53 unused GitHub Copilot seats
    mediumCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    53 unused GitHub Copilot seats

    mediumCountedread straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.$1,007

    Proof level

    Counted — read straight off a bill, a seat list or a usage report. No guessing. Two people pulling the same numbers would get the same answer.

    Source of record

    Vendor seat roster (seat_facts snapshot) via connector sync or CSV import

    Inputs

    Inactive days
    60
    Dormancy rungs
    Dormant 30 days: 53 · 60 days: 53 · 90 days: 44 · Never used: 0
    Monthly cost cents
    100,700
    Affected seat count
    53

    Formula

    recoverable = Σ monthlyCostCents over inactive active seats

    Result: $1,007/mo

    Source rows

    Rules v2.0.0 · methodology 2026.07. Thresholds are editable in Settings → Assumptions.

    $1,007

These findings mix three kinds of dollar: one-off items (the OpenAI spike is a single week, counted once), a modeled productivity estimate (shown as a range, not money in the bank), and recurring waste. The $6,361/mo recurring recoverable tile above is only the counted waste that repeats every month — each dollar counted once, so two findings on the same seat never add up twice.

How the $6,361/mo recurring recoverable breaks down, each dollar counted once:

  • 62 unused ChatGPT Enterprise seats$1,860
  • 53 unused GitHub Copilot seats$1,007
  • Shadow AI spend: Jasper AI$900
  • Shadow AI spend: Descript$840
  • Shadow AI spend: Otter.ai$660
  • Shadow AI spend: Gamma$600
  • 26 engineers carry redundant coding assistants$494
  • Recurring recoverable per month$6,361

The one-off spike and the modeled productivity estimate are not in this total. Where a renewal finding names the same idle seats as an unused-seat finding, the dollars are counted once.

How we model productivity →
Estimated productivity return
Estimatedworked out from assumptions you can see and change. Always shown as a range, never a single number.estimated — worked out from editable assumptions, shown as a range

The $6,361/mo recoverable above is the counted, bankable win. Productivity is the modeled half: the measured throughput uplift is worth about $6,985/mo against a $47,297/mo AI bill (-85% on a strict dollar-for-dollar basis). A modeled uplift smaller than the whole AI bill is the norm — which is exactly why the recoverable waste, not the productivity math, is where the certain money is today.

How the modeled value is worked out

We compare two similar teams before and after a tool rollout, turn the extra pull requests each week into hours saved, and price those hours — then render the result as a range, never a single number. The exact formula behind it:

Δprs_month = DiD_Δprs_per_eng_week × (workdays_month ÷ 5) × engineers × adoption_extrapolation; net_monthly_value = Δprs_month × hours_per_pr_baseline × loaded_cost_per_eng_hour × quality_adjustment × (1 − verification_tax_pct); netRoi = (net_monthly_value − total_ai_cost) ÷ total_ai_cost
Assumptions & sensitivity

Every modeled number resolves through these editable assumptions and renders as a range, never a point.

  • Modeled share of frontier-class tokens assumed movable to mini-class models0.25 ratio
  • Frontier-class token share above this trips the model-mix finding0.5 ratio
  • Baseline engineering hours per merged PR6 hours
  • A seat is unused after this many days with no activity60 days
  • Fully-loaded engineer cost per hour110 USD
  • Modeled mini-class cost as a ratio of frontier-class cost0.2 ratio
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