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商业摘要已提供本语言版本。产品详细流程和技术证据以英语维护。

产品与投资决策

Kairos VC

让投资决策背后的推理保持可审查。

交易研究、评分假设和委员会的判断理由彼此分散。决策需要可追溯的尽职调查记录。

投资团队和创业机构

开发中的原型

A diligence decision trail
Opportunity
A fictional company
Research
Licensed sources and missing evidence
Criteria
Reviewer-defined assumptions
Decision
Committee judgement and monitoring questions
Decision logic and control flow

Explore the reasoning

Frame a clearly fictional opportunity and the diligence question.

Engage now
A diligence-workflow review covering source provenance, criteria, assumptions and the committee record.
Inspect
Development prototype
FastAPI and Next.js application surfaces with PostgreSQL, vector-search, and Redis boundaries.
Still to validate
No investment return, predictive accuracy, or decision-quality outcome is asserted.
Validate licensed data coverage, model and score behaviour, reviewer consistency, and operating controls without inferring investment performance.

源码审查日期

The decision behind the system

Can the investment committee follow the reasoning?

A diligence-workflow prototype connecting deal intake, research, scoring, scenarios, investment-committee reporting and portfolio monitoring.

Kairos focuses on investment diligence and committee records. Scores and scenarios support human judgement; they do not establish predictive accuracy or returns.

One diligence path

Move from intake and triage into research, scoring and scenario work without losing the decision context.

Review and reporting

Investment-committee, portfolio and LP reporting surfaces organise findings for human judgement.

Replaceable providers

Data and model adapters, including a deterministic mock path, separate workflow evaluation from live-provider configuration.

Read the complete system walkthrough
  1. Triage

    Frame a clearly fictional opportunity and the diligence question.

  2. Research

    Assemble authorised research, making provenance and missing sources visible.

  3. Challenge

    Review scenarios and criteria without presenting a score as a calibrated forecast.

  4. Record

    Preserve the committee’s reasoning and monitoring questions. Live data needs separate configuration and rights.

See all evidence and limits

Inspect the source, test conditions and operating limits in the technical record.

查看技术记录与证据

业务 ↔ 技术桥梁 · Diligence decision-support experiment

Connect external signals to an investment decision without turning a score into authority.

Explores a reviewable full-stack diligence workflow with visible assumptions and replaceable model providers.

这是对项目组合与源代码的解读,并非客户、部署、认证或成果声明。

  1. 01

    业务压力

    Deal, market, founder, technical, portfolio, and reporting evidence is assembled across disconnected data sources and individual judgement.

  2. 02

    产品决策

    Separate workflow, APIs, persistence, retrieval, scoring, and model providers, including a deterministic mock path for repeatable testing.

  3. 03

    技术记录

    Deal, research, diligence, scoring, portfolio, and report surfaces across FastAPI, Next.js, data, retrieval, and provider-adapter boundaries.

  4. 04

    证据回流

    Missing sources, conflicting signals, score assumptions, and reviewer decisions return to thesis, diligence scope, and portfolio monitoring.

仓库/运行时边界

本页解读的是独立项目仓库中的源代码。源代码存在,并不表示该项目作为服务运行在 hyperion-consulting.io 内。

访客及客户数据的 AI 处理限于通过受治理的欧盟端点使用 Mistral。所有者授权的 Helio、搜索及 Product Council 流程已启用。独立的公开来源编辑流程使用 Anthropic,并以受治理的 Mistral 作为备用;该流程不接收网站咨询、客户文档或访客对话。远程 AI 不可用时,确定性工具仍可使用。

源码证据

源码实际能够证明什么

A full-stack diligence-workflow prototype with deal, research, scoring, portfolio, and reporting surfaces. Its source demonstrates workflow architecture and provider abstraction; it does not establish investment performance.

详细技术快照以英文维护。

成熟度
Development prototype
证据审查
源码快照截至

产品与架构决策

  • Separate the browser workflow, API modules, persistence, retrieval, and model-provider layer.
  • Use a deterministic mock provider so workflow and scoring behaviour can be exercised without a live model.
  • Position model-assisted scoring as decision support whose assumptions remain visible to an investor.

可核验的证据

  • FastAPI and Next.js application surfaces with PostgreSQL, vector-search, and Redis boundaries.
  • Backend tests spanning deal, intelligence, diligence, portfolio, and report workflows.
  • Provider adapters and local container configuration for repeatable development setup.

声明边界

  • No investment return, predictive accuracy, or decision-quality outcome is asserted.
  • External data coverage and model output depend on the services and credentials configured by an operator.
  • The repository does not establish a live customer deployment or a production operating record.

代码仓库证据不等同于客户、部署、认证或成果证据。

架构

从创始人冷邮件到投资组合监控的工作流界面

三个源代码边界:工作流 UI、模块化 API,以及带有确定性测试支持的可替换模型提供方层。

仅为源代码图:一层可能代表已实现代码、适配器边界或文档设计,并不证明已配置生产服务。

  1. 01

    第 1 层 — 交易工作流 UI

    Next.js 源代码呈现流程看板、创始人评审、评分、竞争格局图谱和投资组合监控界面。源代码存在,并不证明具备实时数据覆盖或已被投资人采用。

  2. 02

    第 2 层 — 模块化 API

    FastAPI 源代码呈现用于交易寻源、尽职调查、创始人评分、技术评估、竞争情报、条款清单起草和投后监控的模块。按阶段校准的逻辑——种子期看重的并不等于 Series C 看重的。

  3. 03

    第 3 层 — 模块化 LLM 后端

    提供方适配器呈现 Anthropic、Mistral、Ollama、Hugging Face 和确定性 mock 路径。模型行为、所在地和数据处理取决于所选提供方与部署配置。

8 个评分维度

该原型评什么、为什么评、数据怎么说

该原型将评分维度和评分标准明确列出,使投资人能够审查其中的假设,而不是把模型输出当作定论。

仅为仓库表面:可用性、完整性、性能与运营适用性需要另行验证。

DIM 1

团队质量

01

领域深度 × 执行速度 × 团队契合信号(联合创始人间的协作动态、过往交付证据、招聘记录)。源代码中呈现的输入:LinkedIn 数据补充、公开提交节奏和访谈记录。列出某个来源的名称,并不证明能够实际访问该来源。

DIM 2

市场规模

02

带来源的自下而上 TAM、自上而下的合理性校验、五年增长论点、监管顺风/逆风。拟议评分规则要求提供结构化的『为何是现在』依据,否则会对『整个欧盟都是我们的市场』的说法扣分。

DIM 3

产品契合度

03

记录用户研究成果、可获得时的留存证据,以及演示与可运营产品之间的差距。结论仍由投资人负责。

DIM 4

增长强度

04

营收曲线质量(而非仅看绝对数字)、净收入留存、销售周期随时间的缩短、自然增长与付费增长的占比。按阶段校准——种子期看重的并非 Series A 看重的。

DIM 5

资本效率

05

相对阶段的烧钱倍数、CAC 回收周期、跑道清晰度、上一轮的估值与质量比。拟议评分规则会标记『为已交付的里程碑投入了过多资本』的情况。

DIM 6

技术深度

06

按领域进行的技术评估,由 LLM 后端起草,种子期与 Series C 采用不同的评分标准。它是供投资人审查的一项输入,而不是对创始人所宣称新颖性的定论。

DIM 7

防御力(护城河)

07

数据护城河深度、切换成本分析、适用时的监管护城河(如临床、国防)、网络效应曲线形态。回答『这家公司为什么不会被科技巨头碾压?』。

DIM 8

GTM 速度

08

销售打法清晰度、ICP 纪律、渠道质量、由创始人主导销售向规模化的过渡计划。拟议评分规则会对『我们会招一位销售 VP 来想办法』之类的回答扣分。

技术栈

技术构建

所列技术仅标识仓库依赖项或适配器表面,并不证明存在活动集成或部署。

后端
Python, FastAPI, PostgreSQL + pgvector, Redis, SQLAlchemy, Alembic
AI / ML
Anthropic、Mistral、Ollama、Hugging Face 和确定性 mock 适配器源代码;可用性、所在地和数据处理取决于独立仓库中的运营方配置。
前端
Next.js 16, React 19, Tailwind CSS, Radix UI, Recharts, Zustand
数据采集
Playwright, Selenium, BeautifulSoup, httpx, LinkedIn, GitHub, F6S
基础设施
Docker Compose, JWT auth, API keys, structlog, rate limiting
测试
Pytest, Playwright E2E, Vitest, TanStack Query
Technical documents & controlled source access

源代码

探索代码

Kairos VC 原型源代码可应要求提供,用于评估与合作目的。

需要 NDA

如需访问源代码,请签署保密协议。这是一项标准的法律手续。

与 Hyperion 合作

审查一条尽职调查决策记录

审查尽职调查流程,涵盖信息来源、标准、假设和委员会记录。

请简要说明您的情况、限制条件和待作决策。开始工作前,我们会商定范围、责任和报价。