增加收入分布快照报告与公司明细直达链接

README.md

@@ -119,6 +119,8 @@ MRR×12仅作为显示单位转换，不改写为公司ARR；公司ARR、估算
 页面本身从同一SQLite中的公司与观测记录计算，不读取第二个数据库。
 
 平台同步时间、连接过期状态、业务描述的可编辑性和产品/法律主体未去重限制均保留。
+收入快照报告位于 `/docs/other-research/ai-company-research/income-snapshot`，样本名称链接 `overview?company=<id>` 的公司明细。
+`python3 scripts/summarize-ai-income.py --check` 独立复核统计与报告一致性；不触发编译或构建。数据变更后先审查方法，再运行不带参数的命令更新固定快照。
 默认7日样本14个产品；90日窗口新增Gojiberry的8月历史点，营销样本的均值和CV随之明显变化。
 这提示先核查时间与样本构成，不能将高离散度直接解释为造假。两个近名营养应用按不同App Store ID分别建档。
 

apps/docs/src/components/AICompanyResearch.tsx

@@ -1,4 +1,5 @@
 import { useEffect, useMemo, useRef, useState } from "react";
+import { useRouter } from "next/router";
 import IncomeDistribution from "./IncomeDistribution";
 import {
   categorySummary, evidenceUrl, technicalReviewLabels, type ResearchEvidence, type ResearchSnapshot,
@@ -30,6 +31,7 @@ function Evidence({ item, prefix = "evidence" }: { item: ResearchEvidence; prefi
 }
 
 export default function AICompanyResearch() {
+  const router = useRouter();
   const [data, setData] = useState<ResearchSnapshot | null>(null);
   const [error, setError] = useState(false);
   const [attempt, setAttempt] = useState(0);
@@ -42,7 +44,12 @@ export default function AICompanyResearch() {
   const [depth, setDepth] = useState("");
   const [teamBand, setTeamBand] = useState("");
   const [technicalLevel, setTechnicalLevel] = useState("");
-  const [selected, setSelected] = useState<string | null>(null);
+  const [selection, setSelection] = useState<{ route: string; id: string | null } | null>(null);
+  const linkedCompany = router.query.company;
+  const selected = selection && selection.route === router.asPath ? selection.id
+    : router.isReady && typeof linkedCompany === "string" && data?.companies.some((c) => c.id === linkedCompany)
+      ? linkedCompany : null;
+  function setSelected(id: string | null) { setSelection({ route: router.asPath, id }); }
   const [tab, setTab] = useState<"companies" | "revenue" | "sources" | "reviews">("companies");
   const companySection = useRef<HTMLElement>(null);
   const detailSection = useRef<HTMLElement>(null);

content/catalog.json

@@ -219,6 +219,13 @@
     "pretext/12-chapter": {
       "slug": "source-studies/pretext/applying-the-design",
       "category": "源码研究"
+    },
+    "ai-company-research/01-income-snapshot": {
+      "collection": "AI 公司研究",
+      "group": "公司与市场",
+      "description": "查看14个产品的平台MRR分布、均值与离散度、历史点敏感性和逐项证据入口。",
+      "slug": "other-research/ai-company-research/income-snapshot",
+      "category": "其他研究"
     }
   }
 }

content/docs/ai-company-research/00-overview.md

@@ -4,6 +4,8 @@
 
 以下工作台读取研究数据库。首批为公开资料定向试样，成立、公开发布和转型发布分别记录。未披露收入的公司继续保留，避免把收入资料缺失误当成零收入。
 
+[收入样本分布快照](/docs/other-research/ai-company-research/income-snapshot)列出当前平台MRR的分组结果、逐项证据入口和历史样本敏感性。
+
 ## 商业模式初评：截至 2026-10-30
 
 以下为研究假设，用于设计付费试验，尚不足以给行业排收益名次。规模、交付证据和收入来源必须一起看；目录条目数量不是市场需求，平台样本也存在自愿公开与幸存者偏差。

content/docs/ai-company-research/01-income-snapshot.md

@@ -0,0 +1,64 @@
+# AI 公司收入样本分布快照
+
+截至 2026-10-30，7日窗口内有 14 个产品的美元平台MRR可纳入分组描述。创意软件订阅组的样本均值较高、CV也较大；营销软件组与消费者个人应用组的CV接近。各组只有1至5个产品，尚不足以估计行业总体或造假概率。
+
+这是已采集资料的固定快照。[动态工作台](/docs/other-research/ai-company-research/overview)提供筛选与原始证据。窗口按最后同步日计算，不保证所有点同日，也不是1,013条公司/产品记录的收入普查。
+
+## 7日窗口的分组结果
+
+MRR单位为美元每月。CV为样本标准差除以均值；最大值剔除降幅表示移除最高收入产品后均值下降的比例。单个样本不计算离散度。
+
+| 业务及收费模式 | 平台 | 样本数 | 均值 | 中位数 | CV | 最大值剔除降幅 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 客户与收入运营 · 软件订阅 | Stripe | 4 | 34,815 | 36,723 | 0.469 | 16.9% |
+| 内容与创意 · 消费者订阅 | RevenueCat | 1 | 53,101 | 53,101 | 不计算 | 不计算 |
+| 内容与创意 · 内容制作服务 | Stripe | 1 | 62,423 | 62,423 | 不计算 | 不计算 |
+| 内容与创意 · 软件订阅 | Stripe | 3 | 41,693 | 49,069 | 0.674 | 28.4% |
+| 消费者个人应用 · 消费者订阅 | RevenueCat | 5 | 25,349 | 20,483 | 0.471 | 20.9% |
+
+在至少3个样本的组内，内容创意软件订阅的均值较高，但移除最大样本后的下降也最大。营销软件与个人应用的CV仅有微小差别，不据此宣称哪个领域更稳定。两个内容类单样本组虽有更高数值，不能作为典型收入或领域排名。
+
+均值和中位数接近只描述当前采集点，并不证明样本具有代表性。平台公开者、成立年、规模、获客渠道与收费方式均可能影响结果。
+
+## 样本与证据入口
+
+点击产品名称可打开公司明细，再查看平台记录及其限制。连接已过期的近期快照仍按所标日期保留，并不表示连接当前有效。
+
+| 产品 | 成立年 | MRR美元每月 | 最后同步 | 平台 | 连接状态 |
+| --- | --- | --- | --- | --- | --- |
+| [Boothie](/docs/other-research/ai-company-research/overview?company=MR001#research-company-detail) | 2026 | 53,101 | 2026-10-29 | RevenueCat | 采集时未标过期 |
+| [White Glove Content](/docs/other-research/ai-company-research/overview?company=MR002#research-company-detail) | 2026 | 62,423 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [Speel.co](/docs/other-research/ai-company-research/overview?company=MR003#research-company-detail) | 2025 | 65,381 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [Laper](/docs/other-research/ai-company-research/overview?company=MR004#research-company-detail) | 2025 | 49,069 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [GETebook.ai](/docs/other-research/ai-company-research/overview?company=MR005#research-company-detail) | 2025 | 10,629 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [TrackAI](/docs/other-research/ai-company-research/overview?company=MR006#research-company-detail) | 2025 | 19,437 | 2026-10-27 | RevenueCat | 已过期的日期快照 |
+| [nutrIA（Nutria Inc）](/docs/other-research/ai-company-research/overview?company=MR007#research-company-detail) | 2025 | 18,198 | 2026-10-29 | RevenueCat | 采集时未标过期 |
+| [Calyra（平台名Nutria 9e3eaa）](/docs/other-research/ai-company-research/overview?company=MR008#research-company-detail) | 2025 | 46,562 | 2026-10-29 | RevenueCat | 采集时未标过期 |
+| [Verbi](/docs/other-research/ai-company-research/overview?company=MR009#research-company-detail) | 2026 | 22,066 | 2026-10-29 | RevenueCat | 采集时未标过期 |
+| [AbMaxx](/docs/other-research/ai-company-research/overview?company=MR010#research-company-detail) | 2026 | 20,483 | 2026-10-29 | RevenueCat | 采集时未标过期 |
+| [traxy.ai](/docs/other-research/ai-company-research/overview?company=MR011#research-company-detail) | 2026 | 52,438 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [NotFair](/docs/other-research/ai-company-research/overview?company=MR012#research-company-detail) | 2026 | 13,376 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [Rank Prompt](/docs/other-research/ai-company-research/overview?company=MR015#research-company-detail) | 2025 | 40,112 | 2026-10-29 | Stripe | 采集时未标过期 |
+| [ChatSEO](/docs/other-research/ai-company-research/overview?company=MR016#research-company-detail) | 2025 | 33,334 | 2026-10-29 | Stripe | 采集时未标过期 |
+
+## 扩大窗口后的敏感性
+
+营销软件订阅组从7日窗口的4个产品扩大到90日窗口的5个产品，均值从34,815升至112,726美元每月，中位数从36,723变为40,112，CV从0.469升至1.551。
+
+新增点来自[Gojiberry AI](/docs/other-research/ai-company-research/overview?company=AI016#research-company-detail)的8月历史快照，连接已过期。移除该最大值后，均值回到34,815美元，下降69.1%。这是日期与样本构成的敏感性，不能解释为行业风险变化。
+
+## 默认窗口排除的历史记录
+
+| 产品 | MRR美元每月 | 最后同步 | 排除原因 |
+| --- | --- | --- | --- |
+| [Adspirer](/docs/other-research/ai-company-research/overview?company=MR013#research-company-detail) | 54,684 | 2026-06-29 | 超过7日窗口，保留历史观测 |
+| [PROSP](/docs/other-research/ai-company-research/overview?company=MR014#research-company-detail) | 128,005 | 2026-02-05 | 超过7日窗口，保留历史观测 |
+| [Gojiberry AI](/docs/other-research/ai-company-research/overview?company=AI016#research-company-detail) | 424,368 | 2026-08-14 | 超过7日窗口，保留历史观测 |
+
+## 对 ARR 与商业模式的含义
+
+MRR乘12只是参考年化。公司声明的ARR、合同管线、近30日收款、一次性额度和服务交付收入仍需分开；支付平台重建指标也不等于财务审计。方法与平台限制见[TrustMRR FAQ](https://trustmrr.com/faq)。
+
+当前更适合选择访谈对象和设计收费试验，不能据最高均值直接决定业务方向。软件订阅优先核留存与推理成本，服务交付优先核人工工时和返工，消费者应用优先核渠道成本、退款和平台抽成。
+
+行业ARR比较仍需补齐同一业务截止日、币种、经常性定义及业务边界。收入日期不完整或同页数值冲突的声明，继续保留在[核查工作台](/docs/other-research/ai-company-research/overview)，不补成零值或精确数字。

scripts/ci.py

@@ -14,7 +14,7 @@ ROOT = Path(__file__).resolve().parents[1]
 APP = "endorphin-tabularium-docs"
 INPUTS = ["Dockerfile", "Dockerfile.ci", ".dockerignore", ".npmrc", "package.json",
           "pnpm-lock.yaml", "pnpm-workspace.yaml", "apps/docs", "content", "tools/figures",
-          "scripts/check-container.sh", "scripts/ci.py"]
+          "scripts/check-container.sh", "scripts/ci.py", "scripts/summarize-ai-income.py"]
 
 
 def run(*args: str, capture: bool = False) -> str:
@@ -63,6 +63,7 @@ def smoke_image(image: str, name: str) -> None:
 
 def main() -> None:
     os.chdir(ROOT)
+    run(sys.executable, "scripts/summarize-ai-income.py", "--check")
     run(sys.executable, "-m", "unittest", "discover", "-s", "scripts", "-p", "test_*.py")
     scope = os.environ.get("CI_CACHE_SCOPE", "main")
     if not re.fullmatch(r"main|pr-[0-9]+", scope):

scripts/summarize-ai-income.py

@@ -0,0 +1,153 @@
+"""Render a fixed research snapshot; --check performs data analysis only, no build."""
+import argparse
+import datetime as dt
+import json
+from pathlib import Path
+import statistics as st
+import sys
+
+ROOT = Path(__file__).resolve().parents[1]
+DATA = json.loads((ROOT / 'content/research/ai-companies.seed.json').read_text())
+REFERENCE = json.loads((ROOT / 'content/research/ai-income-analysis.json').read_text())
+TARGET = ROOT / 'content/docs/ai-company-research/01-income-snapshot.md'
+BASE = '/docs/other-research/ai-company-research/overview'
+COMPANIES = {c['id']: c for c in DATA['companies']}
+CATEGORIES = {c['id']: c['name'] for c in DATA['categories']}
+END = dt.date.fromisoformat(DATA['asOf'])
+
+
+def sample(days):
+    latest, excluded = {}, []
+    for o in DATA['observations']:
+        if o.get('selection') != 'platform' or o.get('metric') != 'mrr':
+            continue
+        c = COMPANIES[o['company']]
+        day = o.get('observedAt')
+        try:
+            parsed = dt.date.fromisoformat(day)
+            valid_date = parsed.isoformat() == day
+        except (TypeError, ValueError):
+            valid_date = False
+        valid = (c['year'] in (2025, 2026) and o.get('origin') == 'payment-platform'
+                 and o['unit'] == 'USD' and o['bound'] == '='
+                 and o.get('comparisonGroup') == 'trustmrr-usd-mrr'
+                 and o.get('provider') and isinstance(o['value'], (int, float))
+                 and 0 <= o['value'] < float('inf') and valid_date
+                 and 0 <= (END - parsed).days <= days)
+        if not valid:
+            excluded.append(o)
+            continue
+        prior = latest.get(c['id'])
+        if prior is None or day > prior['observedAt']:
+            latest[c['id']] = o
+    return latest, excluded
+
+
+def groups(points):
+    result = {}
+    for cid, o in points.items():
+        c = COMPANIES[cid]
+        key = (c['cat'], o['provider'], c.get('businessModel', '收费模式未核验'))
+        result.setdefault(key, []).append(o['value'])
+    return result
+
+
+def stats(values):
+    mean = st.mean(values)
+    sd = st.stdev(values) if len(values) > 1 else None
+    reduced = st.mean(sorted(values)[:-1]) if len(values) > 1 else None
+    return dict(n=len(values), mean=mean, median=st.median(values),
+                sampleDeviation=sd, cv=sd / mean if sd is not None and mean else None,
+                meanWithoutLargest=reduced,
+                largestRemovalDrop=(mean - reduced) / mean if reduced is not None and mean else None)
+
+
+def verify():
+    assert REFERENCE['sourceVersion'] == DATA['version'], 'Reference revision mismatch'
+    for window in REFERENCE['windows']:
+        points, _ = sample(window['days'])
+        assert set(points) == set(window['products']), 'Sample selection mismatch'
+        computed = groups(points)
+        assert len(computed) == len(window['groups'])
+        for group in window['groups']:
+            values = computed[(group['category'], group['provider'], group['model'])]
+            for key, value in stats(values).items():
+                expected = group[key]
+                assert (value is None and expected is None) or (
+                    value is not None and expected is not None
+                    and abs(value - expected) <= 1e-8 * max(1, abs(expected))), key
+
+
+def money(value):
+    return f'{value:,.0f}' if value is not None else '不计算'
+
+
+def link(cid):
+    return f"[{COMPANIES[cid]['name']}]({BASE}?company={cid}#research-company-detail)"
+
+
+def render():
+    points, excluded = sample(7)
+    lines = [
+        '# AI 公司收入样本分布快照', '',
+        f"截至 {DATA['asOf']}，7日窗口内有 {len(points)} 个产品的美元平台MRR可纳入分组描述。创意软件订阅组的样本均值较高、CV也较大；营销软件组与消费者个人应用组的CV接近。各组只有1至5个产品，尚不足以估计行业总体或造假概率。", '',
+        f"这是已采集资料的固定快照。[动态工作台]({BASE})提供筛选与原始证据。窗口按最后同步日计算，不保证所有点同日，也不是{len(DATA['companies']):,}条公司/产品记录的收入普查。", '',
+        '## 7日窗口的分组结果', '',
+        'MRR单位为美元每月。CV为样本标准差除以均值；最大值剔除降幅表示移除最高收入产品后均值下降的比例。单个样本不计算离散度。', '',
+        '| 业务及收费模式 | 平台 | 样本数 | 均值 | 中位数 | CV | 最大值剔除降幅 |',
+        '| --- | --- | --- | --- | --- | --- | --- |',
+    ]
+    for key, values in sorted(groups(points).items()):
+        cat, provider, model = key
+        v = stats(values)
+        cv = f"{v['cv']:.3f}" if v['cv'] is not None else '不计算'
+        drop = f"{v['largestRemovalDrop']:.1%}" if v['largestRemovalDrop'] is not None else '不计算'
+        lines.append(f"| {CATEGORIES[cat]} · {model} | {provider} | {v['n']} | {money(v['mean'])} | {money(v['median'])} | {cv} | {drop} |")
+    lines += ['',
+        '在至少3个样本的组内，内容创意软件订阅的均值较高，但移除最大样本后的下降也最大。营销软件与个人应用的CV仅有微小差别，不据此宣称哪个领域更稳定。两个内容类单样本组虽有更高数值，不能作为典型收入或领域排名。', '',
+        '均值和中位数接近只描述当前采集点，并不证明样本具有代表性。平台公开者、成立年、规模、获客渠道与收费方式均可能影响结果。', '',
+        '## 样本与证据入口', '',
+        '点击产品名称可打开公司明细，再查看平台记录及其限制。连接已过期的近期快照仍按所标日期保留，并不表示连接当前有效。', '',
+        '| 产品 | 成立年 | MRR美元每月 | 最后同步 | 平台 | 连接状态 |',
+        '| --- | --- | --- | --- | --- | --- |',
+    ]
+    for cid, o in sorted(points.items()):
+        status = '已过期的日期快照' if o.get('connectionExpired') else '采集时未标过期'
+        lines.append(f"| {link(cid)} | {COMPANIES[cid]['year']} | {money(o['value'])} | {o['observedAt']} | {o['provider']} | {status} |")
+    fresh = stats(groups(points)[('C06', 'Stripe', '软件订阅')])
+    broad, _ = sample(90)
+    older = stats(groups(broad)[('C06', 'Stripe', '软件订阅')])
+    lines += ['', '## 扩大窗口后的敏感性', '',
+        f"营销软件订阅组从7日窗口的{fresh['n']}个产品扩大到90日窗口的{older['n']}个产品，均值从{money(fresh['mean'])}升至{money(older['mean'])}美元每月，中位数从{money(fresh['median'])}变为{money(older['median'])}，CV从{fresh['cv']:.3f}升至{older['cv']:.3f}。", '',
+        f"新增点来自{link('AI016')}的8月历史快照，连接已过期。移除该最大值后，均值回到{money(older['meanWithoutLargest'])}美元，下降{older['largestRemovalDrop']:.1%}。这是日期与样本构成的敏感性，不能解释为行业风险变化。", '',
+        '## 默认窗口排除的历史记录', '',
+        '| 产品 | MRR美元每月 | 最后同步 | 排除原因 |',
+        '| --- | --- | --- | --- |',
+    ]
+    for o in excluded:
+        lines.append(f"| {link(o['company'])} | {money(o['value'])} | {o.get('observedAt', '未知')} | 超过7日窗口，保留历史观测 |")
+    lines += ['', '## 对 ARR 与商业模式的含义', '',
+        'MRR乘12只是参考年化。公司声明的ARR、合同管线、近30日收款、一次性额度和服务交付收入仍需分开；支付平台重建指标也不等于财务审计。方法与平台限制见[TrustMRR FAQ](https://trustmrr.com/faq)。', '',
+        '当前更适合选择访谈对象和设计收费试验，不能据最高均值直接决定业务方向。软件订阅优先核留存与推理成本，服务交付优先核人工工时和返工，消费者应用优先核渠道成本、退款和平台抽成。', '',
+        f"行业ARR比较仍需补齐同一业务截止日、币种、经常性定义及业务边界。收入日期不完整或同页数值冲突的声明，继续保留在[核查工作台]({BASE})，不补成零值或精确数字。", '',
+    ]
+    return '\n'.join(lines)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--check', action='store_true')
+    args = parser.parse_args()
+    verify()
+    content = render()
+    if args.check:
+        if not TARGET.exists() or TARGET.read_text() != content:
+            sys.exit('Income snapshot differs; regenerate after reviewing data changes')
+        print('Income reference, samples and report are consistent')
+    else:
+        TARGET.write_text(content)
+        print(TARGET.relative_to(ROOT))
+
+
+if __name__ == '__main__':
+    main()