Every meaningful state change in any system, expressed in four parts.
Make AI-generated code observable, queryable, and verifiable. Finally know what AI wrote, why it broke, and how to fix it.
Four properties that make EventIDs the universal standard for AI code observability.
payments.charge.failed is instantly understandable. No magic numbers, no cryptic codes. Your entire team can read the event stream without documentation.
Query auth.% for all auth events. Query %.failed:% for all failures. The dot notation enables powerful SQL pattern matching across your entire system.
The structure teaches AI models your domain. They learn that auth contains user and session, that things can be created, failed, completed.
Schema evolves, old events remain parseable. :1 and :2 can coexist. No breaking changes, no migrations, no downtime.
EventIDs close the loop between AI code generation and observable behavior.
Declare the events your system can emit using our DSL. This becomes the contract AI must fulfill.
AI writes code that emits well-defined events from your catalog. Every action becomes observable.
Running system emits typed, queryable events. You can verify correctness in real-time.
"Event X fired 10x more than expected." AI can reason about its own output and improve.
From catalog definition to type-safe emission.
Type-safe, zero-cost abstractions. EventIDs that compile to nothing but correctness.
Whether you want to understand, implement, or experiment.
The formal specification. Grammar, semantics, edge cases, and the claim: what can and can't be modeled.
Read the specBuild EventIDs interactively. Write catalog DSL, see compiled output, query with SQL. No setup required.
Start experimentingWhy we built this. The problem with AI observability today, and why EventIDs are the solution.
Read the storyMake FSD explainable. Every perception, decision, and action — observable and auditable. Answer: "Why did the car do that?"
See the demoMake rocket AI explainable. Every guidance decision, every engine command — observable and auditable. Answer: "Why did the booster abort?"
See the demoMake BCI AI explainable. Every neural decode, every stimulation — observable and FDA-auditable. Answer: "Why did the implant do that?"
See the demoMake constellation AI explainable. Every orbit adjustment, every collision avoidance — observable and FCC-auditable. Answer: "Why did the satellite change orbit?"
See the demoMake tunnel transport AI explainable. Every pod decision, every ventilation action — observable and DOT-auditable. Answer: "Why did the pod stop?"
See the demoMake robot AI explainable. Every manipulation decision, every balance correction — observable and OSHA-auditable. Answer: "Why did the robot stop?"
See the demoMake moderation AI explainable. Every flagging decision, every feed ranking — observable and DSA-auditable. Answer: "Why was this post removed?"
See the demoMake LLM AI explainable. Every inference decision, every safety filter — observable and EU AI Act auditable. Answer: "Why did Grok say that?"
See the demoMake autonomous driving AI explainable. Every perception decision, every trajectory prediction — observable and NHTSA-auditable.
See the demoMake AI research reproducible. Every AlphaFold prediction, every AlphaGo move — observable and EU AI Act auditable.
See the demoMake recommendation AI explainable. Every video ranking, every moderation decision — observable and DSA-compliant.
See the demoMake multimodal AI explainable. Every inference, every grounding source — observable and EU AI Act compliant.
See the demoMake enterprise MLOps explainable. Every deployment, every drift detection — observable and SOC 2 compliant.
See the demoOn-device ML observability
SD.32.06Make on-device AI transparent. Every local inference, every keyboard prediction — observable and GDPR compliant.
See the demoMake smart home AI transparent. Every thermostat decision, every camera detection — observable and privacy-compliant.
See the demoMake enterprise LLMs observable. Every API call, every content filter, every token — queryable and auditable.
See the demoThe Event Model isn't just for runtime telemetry. The same domain.entity.action:version pattern can make any AI artifact observable.
Runtime observability for AI-generated code. Every function call, every test run, every deployment.
agent.code.generated:1
agent.test.failed:1
Content organization using the same semantic structure. Track what AI wrote, when it changed, who approved it.
content.blog.published:1
content.failure.documented:1
Query across all AI artifacts with the same patterns. Find failures everywhere, track evolution, audit everything.
%.failure.% → All failures
content.%:2 → V2 content
We use Syntax Decimal to organize our own AI-generated content with the same domain.entity.action:version structure. Every blog post, every page, every failure is tracked:
Bounded hierarchy (max 10/level) + mandatory AI Failures category (57) + version tracking = self-correcting AI content system.
The specification is open. The platform is ready. Start building with the universal event model today.