Built on a complete data model

16-module Truth Layer. Data model complete from day one, UI minimal from day one.

Most analytics platforms add features by adding tables. Gurulu starts with the canonical data model and grows the UI around it.

现已可用

今天即可在 Beta 中使用

你现在就能用的能力——核心四件套、四个通道、隐私与每日 AI 摘要。

Core four, always on

The four pillars of the Truth Layer

Identity, registry, health and attribution are mandatory in every tier and every workspace. They are how Gurulu earns the word truth.

Identity engine

已上线

Explainable merge with a four-tier confidence ledger. Every join, split and reassignment is recorded with the evidence that triggered it. Reversible by design.

  • Four-tier confidence: deterministic, probabilistic, AI-suggested, manual
  • Reversible merge ledger with full audit trail
  • Cross-domain resolution — open to all

Event registry

已上线

Events are typed contracts. Schemas live in the registry, ingestion enforces them, so drift becomes impossible. SDKs and AI agents generate code against the registry — no freestyle event names.

  • Source-of-truth schema with validation gate at ingestion
  • Accept / Warn / Quarantine / Reject ingestion outcomes
  • Code-gen typed SDKs for every workspace

Event health

已上线

Multi-selector matchers + 5-layer fail-safe + ML anomaly detection. Tracking regressions are caught before the dashboard lies — usually within minutes of deploy.

  • Multi-selector resilience for autocapture rules
  • ML anomaly detection per event / property / cohort
  • CAPI dedup so downstream destinations stay clean

Attribution engine

已上线

Customer-defined policy. Pick your model, mix multiple models side by side, and see the full trace behind every credited touchpoint. No black-box numbers, ever.

  • Multi-model: last-touch, first-touch, linear, time-decay, position-based
  • Customer-defined attribution policy per workspace
  • Full provenance trace for every conversion

Four channels, four jobs

Observation, tagging, verification and contract — never blurred

Most platforms hide one SDK behind every feature. Gurulu separates the four jobs on purpose — every channel has a single, narrow responsibility.

01 · Script

已上线

Observe

Browser autocapture only. Records raw signals — never invents outcomes, never makes up event names.

Package: @gurulu/web

02 · Playground

已上线

Tag

Heap-style visual picker writes semantic rules into the registry. No events are created here — only rules that match observed signals to typed contracts.

Package: @gurulu/playground

03 · SDK

已上线

Verify

Server-side outcome verification. Conversions, refunds, signups — all backend-bound so they can't be spoofed. Web identify/track is registry-bound too.

Packages: @gurulu/node + @gurulu/web

04 · Agent · CLI · MCP

已上线

Contract

AI authors against the registry. Types are code-generated, freestyle event names are blocked at CI. The agent never invents a new event without a contract.

Packages: @gurulu/cli + @gurulu/mcp-server

开发者工具

在你写代码的地方运行

从浏览器到终端再到 AI 编辑器——每个工具只做一件事，并遵守契约。

MCP 服务器 · Cursor · Claude Code · Lovable

已上线

你的 AI 编辑器对照 registry 写入

MCP 服务器把你的 AI 助手连接到事件 registry。智能体在发送新事件前会用 validate_event 列出、搜索并校验——无法编造随意的名称。在 AI 时代你的契约依然牢固。

  • 读取 registry：list / search / get event
  • 先校验：validate_event → add_event
  • 没有随意命名——遵守契约
文档 →
claude mcp add gurulu -- npx -y @gurulu/mcp-server

SDK · Web (@gurulu/web)

已上线

浏览器：自动捕获 + identify/track。零依赖，约 8 KB。

gurulu.track('signup_completed', { plan: 'pro' })
文档 →

SDK · 服务器 (@gurulu/node)

已上线

服务器：可信结果 + Stripe/Shopify webhook 助手。

await gurulu.track('purchase_completed', { amount: 149 })
文档 →

Playground

已上线

Heap 式点选。无需写代码即可向 registry 添加规则。

文档 →

CLI (@gurulu/cli)

已上线

init / pull / push / validate / doctor。生成类型化事件代码。

gurulu pull   # typed events → code-gen
文档 →

Three event classes

Interaction, intent and outcome — split from day one

Mixing browse, signal and conversion in one table is the original sin of product analytics. Gurulu separates them in the schema, not just in queries.

Class 01

已上线

Interaction

Clicks, scrolls, views — the raw stream of human behavior. High volume, low semantics.

Class 02

即将 · 第 2 阶段

Intent

Inferred signal — a search query, a hesitation, an abandoned cart. Modeled in Phase 2+ with consent-aware enrichment.

Class 03

已上线

Outcome

Backend-verified result — a purchase, a refund, a paid signup. The only class that can credit attribution.

Privacy by default

EU-native, compliance-first, multi-tenant safe

Privacy is enforced in the data model, not bolted on. Every row knows its consent state and its tenant — no leaks possible.

已上线

EU residency default

Hosted in EU data centers with automatic failover. No US data transfer needed for GDPR-bound customers.

已上线

4-category GCM v2

Google Consent Mode v2 wired into every event — analytics, advertising, personalization and security consent surface independently.

已上线

DSR-ready exports

GDPR / CCPA / KVKK subject-access and erasure requests resolve through a single API. Audit trail included.

已上线

Tenant isolation + RLS

Postgres row-level security on every table, ClickHouse partition-per-tenant. Cross-tenant queries are physically impossible.

AI layer

AI that explains itself — and never sees raw PII

Pseudonymize everything before it touches a model. Show the prompt, the citations and the confidence behind every AI suggestion.

已上线

Morning summary

Phase 1 minimum: every workspace gets a daily digest of what changed, what broke and what to investigate. Cited back to the source events.

即将 · 第 2 阶段

Intent inference

Phase 2+: classify raw behavior into intent classes (researching, comparing, ready-to-buy). Consent-aware and pseudonymized end-to-end.

即将 · 第 3 阶段

Advanced reasoning

Phase 3+: cross-event causal reasoning, hypothesis generation, experiment suggestion. Always paired with provenance and confidence.

Multi-model AI pipeline · region-aware automatic fallback

接下来

路线图

清晰标注，尚未上线。每个阶段都有公开的“完成”定义。

Phased delivery

From Phase 0 to Phase 5 — the public roadmap

MVP is Phases 0–3 (~9–12 months). Phases 4 and 5 are post-MVP. Every phase has a public Done definition.

  1. 00
    Phase 0 — FoundationAuth, storage, observability, consent. 4 modules.
  2. 01
    Phase 1 — Core four + data flow + 4 channels + UIBeta opens here. 16 modules total.
  3. 02
    Phase 2 — Intent inference + integrationsIntent class lights up, CAPI destinations expand.
  4. 03
    Phase 3 — Advanced attribution + monetizationOfficial launch + paid pricing turns on.
  5. 04
    Phase 4 — Cross-tenant intelligenceBehavioral benchmarks opt-in for the Pay-as-you-go tier.
  6. 05
    Phase 5 — Native observability platformGurulu monitoring Gurulu — our own native observability platform.
Features — Gurulu Truth Layer