竞争态势实时感知
聚焦具体业务/竞品监控竞争厂商、市场、客户动态,宏观外部环境全景扫描感知市场动态,实时竞争威胁预警与分级管控

热点市场赛道竞争态势深入分析报告

2026 上半年新型储能累计 168.3GW/448.7GWh;114 号文容量电价时长折算机制;8h+ 长时技术路线经济性交叉点;竞争价值链四主体 × 十二项分析工具。
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2026 年 1—7 月新能源汽车销量 901.4 万辆、占新车 51.2%;出口 290.9 万辆(+1.2 倍);动力电池 CR2 达 54.6%;竞争价值链四主体 × 十二项分析工具。
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2026 年 8 月发布;以关键情报问题(KIQ)定义决策;MCC 市场—竞品—客户三维框架、四层证据结构与四道决策门;12 周出海验证冲刺;安克创新、海尔智家、名创优品公开案例与全套可复用模板。
立即申请下载 →帮创业企业发现市场商机,助守业企业扩大竞争优势
聚焦具体业务/竞品监控竞争厂商、市场、客户动态,宏观外部环境全景扫描感知市场动态,实时竞争威胁预警与分级管控
采用SWOT、PESTEL、BCG Matrix、VRIO Framework等体系化竞争分析工具分析竞争情报,判断行业盈利潜力与竞争格局,扫描宏观环境与政策风险,评估核心竞争力与持续优势
自研Neural-Causal AI竞争情报长链因果推理引擎实现竞争策略效果预判,支持开放世界推理发现策略潜在长链因果关系,封闭世界推理预判量化策略执行效果

宏观环境→行业格局→客户需求→企业自身→竞品动态→风险预警→量化决策

决策支持信源竞争情报可靠性
降低竞争情报(CI)采集分析人工
竞品对标分析过程效率提升
信源动态实时跟踪探查商机
构建数据驱动的精益决策体系
从全球整车集团、央国企到双一流高校,CaSee 凯见帮助不同行业的团队把分散的竞争信息转化为可复用的决策依据。
挑战
多市场、多品牌的竞品信息分散在官网、媒体与社交渠道,区域团队难以共享同一套事实。
结果
建立跨市场竞品监测与定期简报机制
挑战
招投标、政策与竞品方案变化快,售前团队需要在短时间内拿到可追溯的竞争情报。
结果
建立重点行业招投标与竞品动态的常态化监测
挑战
多行业赛道并行,竞品发布、行业方案与客户案例信息分散,市场与产研协同成本高。
结果
形成跨赛道竞品情报底稿与定期简报
客户证言
“CaSee 把分散的竞品信息变成了团队每天都会打开的情报视图。”

客户 Logo 按识别度与行业排序,排序不分先后。















客户 Logo 与案例内容经客户授权使用;案例描述以客户确认的项目材料为准。 · 最后更新:2026-11
预约同行业案例演示与合作伙伴一起,把情报嵌入企业工作流
从 AI 算力、智能体平台到产学研联合实验室,CaSee 与生态伙伴共同交付可集成、可落地的竞争情报能力。
AI 算力生态合作围绕大模型推理与向量检索场景,开展 AI 算力适配与优化合作。
智能体生态CaSee 竞争情报专家智能体已接入 WorkBuddy 开放平台,可在企业工作流中直接调用。
产学研合作与东北大学共建“产业竞争情报分析技术联合实验室”,聚焦多源情报采集、异构数据融合、空间认知图谱与因果推理,推动竞争情报从被动监测走向主动预判。
渠道代理、实施交付、技术集成,欢迎一起服务企业竞争情报场景。
联系我们 →Logo 排序不分先后;合作范围以双方公开确认的信息为准。





CaSee 凯见是 Neural-Causal AI 驱动的企业竞争情报与市场洞察平台,面向市场、销售、产品与战略团队,提供可信信源情报采集、竞争态势感知、竞争威胁量化、风险预警与竞争策略预判。
平台覆盖 500+ 可信信源,支持企业官网、新闻媒体、行业报告、招投标公告等公开信息源,并通过 T-Score 评估信源可信度。
帮助团队实时监测竞品动态、发现市场与客户需求变化、量化竞争威胁、预判竞争策略效果,减少人工收集与碎片化分析。
提供开放 API 与 MCP 接入能力,开发者可在 API 文档查看接口说明;具体权限与计费以商务约定为准。
点击页面“申请试用”,填写企业与联系方式,我们会与您联系开通试用。
联系邮箱:alex@xcasee.com · 联系电话:13654986127 · 地址:杭州市西湖区 · 微信公众号:casee_me
产业实践与学术研究深度融合的产品研发团队
CaSee 凯见竞争情报站由一支产业实践与学术研究深度融合的产品研发团队打造。团队以“将前沿数据科学技术能力转化为企业精准决策竞争力”为使命:产品研发层汇聚了来自阿里云、字节跳动、东软集团基础软件事业部、中国电子科技集团等机构的产品策划与核心研发专家,核心成员为拥有 20 余年经验的产品、技术与 AI 专家。
技术研究层联合大连理工大学、东北大学、中南大学、鲁汶大学等高校,围绕 Causal AI 因果推理、本体语义分析、谓词逻辑推理、AI 智能体协同组网等方向开展前沿研究,形成由教授、博导、博士组成的学术梯队。目前团队拥有教授级高工 2 人、博导 1 人、博士 3 人、硕士 12 人,依托校企联合实验室模式持续推进前沿技术的产品化落地。

Deliver trusted competitive dynamics, reason and predict strategy effects, build sustainable competitive advantage
In-Depth Competitive Landscape Analysis Report of Hot Market Tracks

H1 2026 new-type energy storage reached 168.3GW/448.7GWh; Document No.114 capacity-price duration-based scaling; economic crossover for 8h+ technologies; CVC four entities × twelve analytical tools.
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Jan–Jul 2026 NEV sales hit 9.014 million units, 51.2% of new-car sales; exports 2.909 million (+120%); battery CR2 at 54.6%; CVC four entities × twelve analytical tools.
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Released August 2026; decision framing via Key Intelligence Questions (KIQ); the MCC market–competition–customer framework with a four-layer evidence structure and four decision gates; a 12-week globalization validation sprint; public case studies of Anker, Haier Smart Home and MINISO with a full set of reusable templates.
Request Download →Help startups discover market opportunities, help established enterprises expand competitive advantages
Focus on specific business/competitor monitoring of vendors, markets, customer dynamics, full scan of macro external environment for market awareness, real-time competitive threat alerts and graded management
Systematic competitive analysis tools like SWOT, PESTEL, BCG Matrix, VRIO Framework to analyze competitive intelligence, assess industry profit potential and competitive landscape, scan macro environment and policy risks, evaluate core competitiveness and sustainable advantages
Self-developed Neural-Causal AI long-chain causal reasoning engine for competitive strategy effect prediction, supports open-world reasoning to discover potential long-chain causal relationships, closed-world reasoning to predict quantitative strategy execution effects

Macro Environment → Industry Landscape → Customer Needs → Enterprise → Competitor Dynamics → Risk Alerts → Quantitative Decisions

Decision support source competitive intelligence reliability
Reduced competitive intelligence (CI) collection and analysis labor
Improved competitor benchmarking analysis efficiency
Real-time tracking of source dynamics for business opportunities
Building a Data-Driven, Lean Decision System
From global automotive groups and state-owned enterprises to leading universities, CaSee helps teams turn scattered competitive signals into reusable decision inputs.
Challenge
Competitor information across markets and brands is scattered across websites, media, and social channels, making it hard for regional teams to share a single version of the facts.
Result
Established cross-market competitor monitoring and recurring briefs
Challenge
Tender notices, policies, and competitor solutions change quickly; pre-sales teams need traceable intelligence at short notice.
Result
Established continuous monitoring of tenders and competitor moves in priority industries
Challenge
With multiple industry tracks in parallel, competitor launches, solutions, and customer stories are scattered, raising coordination costs between marketing and product teams.
Result
Created a cross-track competitor intelligence base and recurring briefs
Customer testimonial
“CaSee turned scattered competitor information into an intelligence view our team opens every day.”

Customer logos are ordered by recognition and industry; listing order does not imply ranking.















Customer logos and case content are used with permission; case descriptions are based on customer-confirmed project materials. · Last updated: Nov 2026
Book a demo for your industryEmbedding intelligence into enterprise workflows with our partners
From AI compute and agent platforms to industry-academia research, CaSee works with ecosystem partners to deliver competitive intelligence that integrates into real workflows.
AI Compute Ecosystem PartnerCollaborating on AI compute optimization for LLM inference and vector retrieval scenarios.
Agent EcosystemCaSee competitive intelligence expert agents are available on the WorkBuddy open platform and can be invoked directly in enterprise workflows.
Industry-Academia CollaborationCo-built with Northeastern University, the Joint Laboratory for Industrial Competitive Intelligence Analysis focuses on multi-source collection, heterogeneous data fusion, spatial cognitive graphs, and causal reasoning—moving intelligence from reactive monitoring to proactive anticipation.
Channel, delivery, and technology integration partners are welcome.
Contact us →Partner logos are listed without implying ranking; scope of cooperation is subject to public confirmation.





CaSee is a Neural-Causal AI-driven enterprise competitive intelligence and market insight platform for marketing, sales, product, and strategy teams. It provides trusted-source intelligence collection, competitive situation awareness, threat quantification, risk alerts, and competitive strategy prediction.
The platform covers 500+ trusted sources, including corporate websites, news media, industry reports, and tender announcements, and evaluates source credibility with T-Score.
It helps teams monitor competitor moves in real time, identify changes in market and customer demand, quantify competitive threats, predict the effects of competitive strategies, and reduce manual collection and fragmented analysis.
Yes. Open API and MCP access are available. Developers can find interface documentation in the API docs; permissions and pricing are subject to commercial agreement.
Click "Request a trial", fill in your company and contact details, and we will contact you to activate the trial.
Email: alex@xcasee.com · Phone: 13654986127 · Address: Xihu District, Hangzhou · WeChat: casee_me
A Product R&D Team Where Industry Practice Meets Academic Research
CaSee's competitive intelligence station is built by a product R&D team that deeply integrates industry practice with academic research. With the mission of "turning cutting-edge data science capabilities into a precise decision-making advantage for enterprises," the product R&D layer brings together product planning and core R&D experts from Alibaba Cloud, ByteDance, the Basic Software Business Unit of Neusoft, China Electronics Technology Group and other organizations; core members are product, technology and AI experts with more than 20 years of experience.
The technology research layer collaborates with Dalian University of Technology, Northeastern University, Central South University, KU Leuven and other universities on frontier research in Causal AI inference, ontology semantic analysis, predicate logic reasoning and AI agent collaborative networking, forming an academic echelon of professors, doctoral supervisors and PhDs. The team currently includes 2 professor-level senior engineers, 1 doctoral supervisor, 3 PhD holders and 12 master's degree holders, and continues to move frontier technologies into products through the university-industry joint laboratory model.