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.
Available now
Live in beta today
What you can use right now — the core four, four channels, privacy, and the daily AI digest.
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
LiveExplainable 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
LiveEvents 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
LiveMulti-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
LiveCustomer-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
LiveObserve
Browser autocapture only. Records raw signals — never invents outcomes, never makes up event names.
Package: @gurulu/web
02 · Playground
LiveTag
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
LiveVerify
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
LiveContract
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
Developer tools
It works where you write code
From the browser to the terminal to your AI editor — each tool does one job and respects the contract.
MCP server · Cursor · Claude Code · Lovable
LiveYour AI editor writes against the registry
The MCP server connects your AI assistant to your event registry. The agent lists, searches and validates a new event with validate_event before sending it — it can’t invent freestyle names. Your contract holds in the age of AI too.
- Reads the registry: list / search / get event
- Validates first: validate_event → add_event
- No freestyle names — it follows the contract
claude mcp add gurulu -- npx -y @gurulu/mcp-serverSDK · Web (@gurulu/web)
Browser: autocapture + identify/track. Zero-dep, ~8 KB.
gurulu.track('signup_completed', { plan: 'pro' })SDK · Server (@gurulu/node)
Server: trustworthy outcomes + Stripe/Shopify webhook helpers.
await gurulu.track('purchase_completed', { amount: 149 })CLI (@gurulu/cli)
init / pull / push / validate / doctor. Code-gen typed events.
gurulu pull # typed events → code-genThree 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
Interaction
Clicks, scrolls, views — the raw stream of human behavior. High volume, low semantics.
Class 02
Intent
Inferred signal — a search query, a hesitation, an abandoned cart. Modeled in Phase 2+ with consent-aware enrichment.
Class 03
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.
EU residency default
Hosted in EU data centers with automatic failover. No US data transfer needed for GDPR-bound customers.
4-category GCM v2
Google Consent Mode v2 wired into every event — analytics, advertising, personalization and security consent surface independently.
DSR-ready exports
GDPR / CCPA / KVKK subject-access and erasure requests resolve through a single API. Audit trail included.
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.
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.
Intent inference
Phase 2+: classify raw behavior into intent classes (researching, comparing, ready-to-buy). Consent-aware and pseudonymized end-to-end.
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
What’s next
On the roadmap
Clearly marked, not here yet. Every phase has a public “Done” definition.
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.
- 00Phase 0 — FoundationAuth, storage, observability, consent. 4 modules.
- 01Phase 1 — Core four + data flow + 4 channels + UIBeta opens here. 16 modules total.
- 02Phase 2 — Intent inference + integrationsIntent class lights up, CAPI destinations expand.
- 03Phase 3 — Advanced attribution + monetizationOfficial launch + paid pricing turns on.
- 04Phase 4 — Cross-tenant intelligenceBehavioral benchmarks opt-in for the Pay-as-you-go tier.
- 05Phase 5 — Native observability platformGurulu monitoring Gurulu — our own native observability platform.