> For the complete documentation index, see [llms.txt](https://docs.loci-dev.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.loci-dev.net/welcome-to-loci/loci-product-platform-architecture-of-the-customer-facing-ecosystem.md). # LOCI Product Platform - Architecture of the Customer-Facing Ecosystem ### How is LOCI delivered to users? The CI/CD surfaces a customer wires into their own pipelines, the command-line client that carries every programmatic interaction, the central backend that holds the business logic and the source-control integrations, the web dashboard where analyses are explored, and the AI agent layer that turns analysis results into written summaries and conversation. Together these are the product around LOCI's analysis engine — how binaries get in, and how insight gets back to the engineer. What happens *inside* the analysis — static analysis, model inference, and insight generation — is the binary processing pipeline and is described in the separate pipeline document; here it is a downstream boundary. The customer contract of the GitHub CI/CD surface is described in depth in the CI/CD integration document; here the surfaces appear at ecosystem level. The internals of the AI agent layer are described in the separate agent document; here it appears at the interface level. *** ### System overview LOCI is delivered as a hosted, multi-tenant platform, and a customer reaches it in two ways: through their **development workflow** — a CI/CD integration builds and uploads the compiled binaries on every change, and the results return to the pull request as a comment and a performance check — and through the **web dashboard**, where binaries ready for processing are uploaded manually, projects and versions are managed, and results are explored interactively. Both paths converge on the platform backend, which owns the business objects (companies, users, projects, versions, analyses), hands uploaded binaries to the processing pipeline, stores and serves the resulting insights, and brokers the AI agent layer. The unit of work throughout is a **version** — a named upload of a project's compiled binaries — and on a pull request the platform's central object is the comparison of the change's version against its base. *The ecosystem. Both customer paths converge on the platform backend; the numbered flows are explained in the panel beside the diagram. The pipeline's stages are shown for orientation only — they are the pipeline document's subject.*