下面 6 个使用场景对应我们能立刻交付的能力，3 个项目案例是已经完成或正在交付的真实工程。客户身份均已匿名化处理。 Six scenarios below describe capabilities we can ship today. Three project cases are real engagements already delivered or in flight. Client identities have been anonymized.
把分散的产品规格书、元器件 datasheet、工艺 SOP、客诉处置记录融成一张图。研发查"某板卡用了哪些芯片 / 同等替代有哪些 / 上次客诉处理结论"——一次问到位。
Fuse scattered product specs, component datasheets, SOPs and field-issue logs into a single graph. R&D can ask "which chips are on this board / what are the equivalents / what was the resolution last time" in one shot.
SKU 资料、Listing 文案、客诉记录、退换政策散落在 5+ 平台后台。一个 Agent 拿到全量上下文，回复客户问题 / 写新品文案的口径终于一致。
SKU info, listings, support tickets and policies live across 5+ platform backends. One agent with full context — finally a consistent voice across customer replies and new-product copy.
判例库 + 法规更新 + 内部论证笔记 + 已结案件复盘。律师写 brief 时，AI 给出"本所历史相似案件 + 法条引用 + 反方常见抗辩"——但每条结论都能溯源到具体段落。
Case law + regulatory updates + internal memos + closed-case retros. When drafting a brief, AI surfaces "similar firm cases + statutes + common counter-arguments" — every claim traceable to source.
注册申报材料、临床试验数据、不良事件、售后维修记录全部融通。监管事件追溯不再翻几个 G 的 PDF——结构化路径直接拉出来。
Regulatory filings, clinical trial data, adverse events, field service logs — all linked. Tracing a regulatory event no longer means scanning gigabytes of PDFs; the structured path surfaces in seconds.
院校数据、历年录取案例、文书素材、学员信息——顾问跟一个学员时能瞬间看到"过去 50 个相似学员投了哪 3 所校最稳"。文书初稿从 4 小时缩到 30 分钟。
School data, historical admits, essay corpora, student records. When an advisor opens a student file, "the 3 safest targets among 50 similar past admits" surfaces instantly. Essay drafts compress from 4 hours to 30 minutes.
DD 资料、行业研究、被投/拟投公司动态、内部投决会议纪要全部对齐。Sourcing 阶段问"过去三年我们看过的同赛道项目都死在哪一步"——直接拿到结构化复盘。
DD materials, industry research, portfolio/pipeline updates, internal IC notes — all aligned. At sourcing, ask "where did the same-vertical deals we've reviewed in the past 3 years die" — get a structured retro back.
客户主营机车控制系统板卡，每一份图纸、工艺、芯片型号都要经得起运行追溯。我们用时光脉络把分散在多个文件夹的产品规格、研发报告、销售档案融通成一张知识图谱，研发查"某板卡用了哪些主芯片"从需要翻文件到秒级返回带置信度的答案。
The client makes locomotive control boards — every drawing, SOP and chip spec must survive operational traceability. We used Shiguangmailuo to fuse product specs, R&D reports and sales records (scattered across many folders) into one knowledge graph. R&D queries like "what main chips are on board X" went from manual search to a second-level answer with confidence scores.
客户在 Amazon US/EU、独立站、TikTok Shop、Shopee 多地运营 4500+ SKU。同一商品在不同站点的文案、规格、退换政策版本不一，客服跨平台回复客户问题经常踩自家坑。我们把全部 SKU、12K 客诉历史、退换政策融到一个知识库，所有客服 / 文案 Agent 拉的是同一份事实底座。
Client runs 4,500+ SKUs across Amazon US/EU, DTC site, TikTok Shop and Shopee. Listings, specs and return policies for the same item drift across stores; support reps regularly contradict their own brand. We fused all SKUs, 12K support history, and policies into one KB — every support / copy agent now queries the same fact base.
律师写一份并购合规 brief 平均 4 小时，其中近半时间在翻历史案件。客户的 8K+ 判例、近三年内部论证笔记、监管解读分散在文件服务器和个人电脑里——经验没法系统沉淀。POC 范围是把判例 + 笔记入库，律师写新 brief 时 Agent 提供"本所历史相似案件 3 个 + 关键法条 + 反方常见抗辩"作为底稿。
A new compliance brief takes 4 hours on average; nearly half is digging through prior cases. The firm's 8K+ cases, 3 years of internal memos, and regulatory reads were scattered across file servers and personal laptops — experience never accreted. POC scope: ingest cases + memos, then surface "3 similar firm cases + key statutes + common counter-arguments" as a draft scaffold for new briefs.
我们目前只承接 3-5 家深度客户。如果你那边有真实业务数据 + 决策人愿意做反馈，可能正好对路。 We only run 3-5 deep engagements at a time. If you have real internal data and a decision-maker willing to give honest feedback, we might be a fit.