> For the complete documentation index, see [llms.txt](https://zeni.gitbook.io/zeni/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://zeni.gitbook.io/zeni/technical-architecture/architecture-overview.md). # Architecture Overview Rather than a rigid multi-tier architecture, ZENi is organized as modular components that map to the data lifecycle: capture, normalize, score, verify, commit, and serve. Deployments can enable or extend components without changing the core interfaces. **A) Signal Capture and Connectors** • On-chain ingestion (permissionless): collect public events via indexers/RPC, normalize event logs and state changes, attach provenance (source, block range). • Online ingestion (consent-first + open where available): collect events through user-authorized connectors and open/public datasets/APIs when accessible. • Pre-processing: dedup hints, timestamp normalization, entity mapping, and lightweight enrichment. **B) Verification and Quality Engine** • Automated checks: anomaly detection, spam/abuse heuristics, consistency checks, and deduplication confidence. • Human-in-the-loop review queues: sampling, dispute resolution, and calibration for high-impact or high-risk signals. • Quality scoring: freshness, uniqueness, consistency, and anomaly risk indicators published per dataset/version. **C) On-chain Commitments and Audit Trail** • Dataset/version commitments: publish hashes/roots for tamper-evident versioning. • Integrity metadata: record proof hashes and quality metrics references for auditability. • Consent state anchors (where applicable): record permission state changes without exposing raw inputs. • Fee and reward settlement: protocol-level accounting for access and incentive distribution. **D) Access Interfaces (Developer and Partner)** • APIs and SDKs: query verified signals, subscribe to webhooks, and retrieve dataset versions. • Policy controls: select integrity tiers and sampling policies based on use case needs. • Output formats: AI-ready exports for training/evaluation and application-ready feeds for analytics/automation. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://zeni.gitbook.io/zeni/technical-architecture/architecture-overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.