PointSav GIS engine
fix(editorial): §7 violations — GIS engine lede rewrite + forbidden terms; professional-centres frontmatter
@@ -12,7 +12,7 @@ bcsc_class: public-disclosure-safe language_protocol: PROSE-TOPIC last_edited: 2026-05-08 editor: pointsav-engineering short_description: "The PointSav GIS Engine is a high-performance, customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from traditional geographic information systems that rely on centralised database instances and continuous network connectivity." short_description: "The PointSav GIS Engine is a customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from geographic information systems that rely on centralised database instances and continuous network connectivity." paired_with: pointsav-gis-engine.es.md cites: - maplibre-gl-js @@ -20,20 +20,20 @@ cites: - tippecanoe-tool --- The PointSav GIS Engine is a high-performance, customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from traditional geographic information systems that rely on centralised database instances and continuous network connectivity. The engine reads from a static PMTiles archive on the customer's own [[service-fs-data-lake|filesystem]], renders interactively through MapLibre GL JS in the browser, and serves every query without an external dependency. The map data lives in the customer's archive; nothing leaves the deployment unless the operator explicitly chooses to publish. A location-intelligence platform that depends on a central database and a live network is a platform a customer rents, not owns — outages, per-seat cloud cost, and air-gap ineligibility follow. The PointSav GIS Engine runs offline-first from flat files: it reads from a static PMTiles archive on the customer's own [[service-fs-data-lake|filesystem]], renders interactively through MapLibre GL JS in the browser, and serves every query without an external dependency. For a regulated buyer, the spatial record is auditable, portable, and never leaves the building. ## Architectural Principles The engine is engineered to operate as a stateless application surface, adhering to the PointSav principle of complete data sovereignty. The engine operates as a stateless application surface, decoupling the data layer from the runtime so either can be updated or replaced independently. ### Flat-File Substrate Unlike traditional GIS stacks (e.g., PostGIS, Esri) which require persistent database management, the PointSav engine uses a flat-file substrate. It consumes geographic data directly from `JSONL`, `GeoParquet`, and `YAML` formats versioned within a Totebox Archive. This architecture ensures the data layer remains entirely decoupled from the application logic, eliminating database maintenance overhead and preventing vendor lock-in. Unlike centralised GIS stacks that require persistent database management, the PointSav engine uses a flat-file substrate. It consumes geographic data directly from `JSONL`, `GeoParquet`, and `YAML` formats versioned within a Totebox Archive. This architecture ensures the data layer remains entirely decoupled from the application logic, eliminating database maintenance overhead and preventing vendor lock-in. ### Sovereign Rendering Stack The platform avoids commercial SaaS mapping dependencies by using a high-performance, open-source rendering stack: The platform avoids commercial SaaS mapping dependencies by using an open-source rendering stack: - **PMTiles:** A single-file archive format for tiled data that enables maps to be served directly from standard web servers (Nginx) or blob storage without a dedicated tile server. [pmtiles-spec] - **MapLibre GL JS:** A WebGL-based library for rendering interactive vector maps with high client-side performance. [maplibre-gl-js] - **MapLibre GL JS:** A WebGL-based library for rendering interactive vector maps in the browser. [maplibre-gl-js] - **Tippecanoe:** A tool used to compile massive flat-file datasets into optimized vector tiles, ensuring rapid delivery of complex co-location clusters. [tippecanoe-tool] ## Spatial Processing and Orchestration