Reprice the analyzed org on the other vendor's tiers

CLAUDE.md

@@ -43,7 +43,7 @@ Aggregation (agg/aggOpenAI)
     └─ Consolidates usage buckets by model/key/workspace
     ↓
 Analysis Engine (findIssues/findIssuesOpenAI)
-    └─ Runs the vendor's rule set (9 Anthropic / 13 OpenAI) with confidence
+    └─ Runs the vendor's rule set (10 Anthropic / 14 OpenAI) with confidence
        scoring; optional NIM consensus merges LLM proposals on top, priced
        by lib/{vendor}/costing.ts — the LLM never emits dollar figures
     ↓
@@ -78,7 +78,7 @@ History page → Detail routes → /recommendations, /analytics, /raw-data
 - Service-level analytics: tracks spending by service type (embeddings, completions, audio, images, etc.)
 - Per-project service breakdown charts showing monthly usage across all services
 
-### Analysis Engine (22 rules: 9 Anthropic, 13 OpenAI)
+### Analysis Engine (24 rules: 10 Anthropic, 14 OpenAI)
 
 Located in `lib/anthropic/analysis.ts` and `lib/openai/analysis.ts` — the
 `RULE n:` comment headers in those files are the authoritative list. The core
@@ -110,6 +110,12 @@ categories:
 6. **Legacy Model** (Anthropic only)
    - Outdated generation (g=1) still in use
 
+7. **Cross-Vendor Comparison** (both vendors, one org-level finding per report)
+   - Reprices the whole org on the other vendor's nearest-equivalent tiers
+   - Both sides priced from the pricing tables (never OpenAI's billed `r.cost`)
+   - Always INFO, always `sav: 0` — never contributes to report savings
+   - Gated off below $50/mo org spend or a delta under 10% of that spend
+
 **Confidence Scoring:** Multi-signal weighted system (0-1 scale)
 
 - Signals: usage volume, consistency (CoV), active days, input variance, temporal patterns
@@ -126,6 +132,7 @@ categories:
 - `lib/anthropic/costing.ts` - deterministic optimized-cost formulas per category (OpenAI parallel in `lib/openai/costing.ts`)
 - `lib/nim/analysis.ts` - NIM LLM proposals, deterministic pricing, consensus merge (`source: rules|llm|both`)
 - `lib/savingsCap.ts` - caps cumulative per-row savings at the row's spend, applied by every engine output
+- `lib/crossVendor.ts` - cross-vendor tier mapping + comparison math, imported by both engines (rules-only, never NIM)
 - `lib/anthropic/pricing.ts` - Model pricing table, cost calculation (`tc()` function)
 - `lib/openai/*` - Parallel implementations for OpenAI
 - `lib/formatters.ts` - Currency (`$`) and percentage (`P`) formatters

PRODUCT.md

@@ -11,7 +11,7 @@ Built as part of Nick's Omilia internship; doubles as a portfolio project.
 
 ## Where it stands (2026-08-06)
 
-- **22 detection rules** (9 Anthropic, 13 OpenAI) with weighted-signal
+- **24 detection rules** (10 Anthropic, 14 OpenAI) with weighted-signal
   confidence scoring and per-finding signal trails.
 - **Consensus engine**: optional NVIDIA NIM augmentation runs alongside the
   rules, proposes findings from a fixed category set, and never prices —
@@ -25,7 +25,7 @@ Built as part of Nick's Omilia internship; doubles as a portfolio project.
   incident month) or a lean startup (1-2 minor findings, on purpose). Each
   run is internally coherent across its 6 months; the generators are pure
   and clock-free, reproducible via explicit seeds (`DEMO_SEED` in tests).
-- 117 vitest tests pin the money math; CI runs type-check, lint, format, test.
+- 138 vitest tests pin the money math; CI runs type-check, lint, format, test.
 - AGPL-3.0.
 
 Corrected 2026-08-08: `prOpenAI` matched pricing-table keys in insertion
@@ -47,8 +47,13 @@ Next candidates, in rough order (none committed):
    responses; needs a real key and a real llama round-trip. The demo now
    makes exactly one NIM call per run (current month only), so it doubles
    as the cheapest live test.
-2. **Cross-vendor comparison** — both pricing tables are in the repo; "this
-   workload on the other vendor" is a finding single-vendor tools can't make.
+
+Shipped from this list 2026-08-08: **cross-vendor comparison** — one org-level
+INFO finding per report reprices the whole analyzed org on the other vendor's
+nearest-equivalent tiers (`src/lib/crossVendor.ts`, imported by both engines).
+Both sides are priced from the pricing tables, never from OpenAI's billed
+`r.cost`, which has no Anthropic counterpart. It contributes zero savings by
+construction and gates itself off below $50/mo or a sub-10% delta.
 
 Shipped from this list 2026-08-06: temporal/scheduling rules (weekly-cadence
 5d/4c, steady-batch 5c with a weekend-parity gate that keeps batch advice off

README.md

@@ -16,7 +16,7 @@ _an LLM spend auditor that reads your Admin API and tells you where the money le
 ![telemetry](https://img.shields.io/badge/telemetry-0-3fb950?style=flat-square&labelColor=111111)
 ![keys](https://img.shields.io/badge/your_API_keys-never_leave_the_browser-3fb950?style=flat-square&labelColor=111111)
 ![access](https://img.shields.io/badge/access-read--only._we_touch_nothing-d4a017?style=flat-square&labelColor=111111)
-![rules](https://img.shields.io/badge/detection_rules-22-d4a017?style=flat-square&labelColor=111111)
+![rules](https://img.shields.io/badge/detection_rules-24-d4a017?style=flat-square&labelColor=111111)
 ![engine](https://img.shields.io/badge/AI_augmentation-detects%2C_never_prices-3fb950?style=flat-square&labelColor=111111)
 ![savings](<https://img.shields.io/badge/savings-real._(your_CFO_may_weep)-3fb950?style=flat-square&labelColor=111111>)
 
@@ -32,7 +32,7 @@ _an LLM spend auditor that reads your Admin API and tells you where the money le
 
 Most teams discover their LLM bill the way you discover a parking ticket.
 TokenPilot reads your **Anthropic and OpenAI Admin APIs**, runs your actual
-token volumes through a **22-rule detection engine** (9 Anthropic, 13 OpenAI),
+token volumes through a **24-rule detection engine** (10 Anthropic, 14 OpenAI),
 and hands back confidence-scored savings recommendations in about 60 seconds.
 No theoretical benchmarks — every dollar figure is computed from _your_ usage.
 
@@ -41,7 +41,7 @@ the tab does, and never gets sent anywhere except the vendor it belongs to.
 
 ```console
 nick@tokenpilot:~$ audit --vendor anthropic
-[✓] 9 rules executed. 4 findings. estimated waste: not zero.
+[✓] 10 rules executed. 4 findings. estimated waste: not zero.
 [i] your keys stayed in this tab. as is tradition.
 ```
 
@@ -58,6 +58,7 @@ nick@tokenpilot:~$ audit --vendor anthropic
 | 07  | **reasoning-model overkill** | o-series reasoning premiums spent on non-reasoning work        |
 | 08  | **quality upgrade**          | the reverse case — places a smarter model would pay for itself |
 | 09  | **legacy model usage**       | deprecated models quietly costing more for less                |
+| 10  | **cross-vendor comparison**  | your exact token volumes repriced on the other vendor's tiers  |
 
 Each finding gets **multi-signal confidence scoring** — volume, consistency,
 active days, temporal patterns — and carries its **signal trail** ("based on: