It's like having a tireless city planner who has memorized every zoning rule in town and can review your building plans in minutes instead of weeks.

AutoSitu's core use case deploys large language models fine-tuned on municipal zoning ordinances, land-use codes, and historical approval records to perform end-to-end compliance checks on incoming development applications. The system ingests regulatory documents—often hundreds of pages of dense, jurisdiction-specific legal text—and builds structured representations of setback requirements, height limits, use permissions, parking ratios, FAR calculations, and conditional overlays. When a developer or planner submits a site plan, the AI agents cross-reference extracted plan attributes (lot dimensions, proposed use, building footprint) against the applicable regulatory framework, flagging violations, ambiguities, and conditional approvals with cited code references. By encoding precedent-based reasoning from thousands of prior decisions, the platform achieves human-level accuracy while compressing review cycles from weeks to minutes, freeing municipal staff to focus on discretionary and community-facing work rather than rote code lookups.

It's like replacing a room full of lawyers reading a phone book with a single paralegal who has photographic memory and never needs a coffee break.

It's like teaching a computer to read blueprints the way an experienced architect does—spotting every building edge, parking space, and property line instantly.

AutoSitu applies document AI and computer vision models to ingest site plans, architectural drawings, and survey plats submitted in varied formats (PDF, CAD, scanned images) and automatically extract spatial features critical to zoning review. The system uses object detection and semantic segmentation to identify building footprints, lot boundaries, driveways, parking stalls, landscaping buffers, and setback lines. Extracted features are converted into structured geospatial data and overlaid against parcel geometries and zoning district maps. This eliminates the manual, error-prone process of planners visually measuring distances on paper plans and cross-referencing them against code requirements. The extracted data feeds directly into the compliance engine (Use Case 1), creating a fully automated pipeline from raw plan submission to regulatory verdict. Edge cases—such as hand-drawn annotations, low-resolution scans, or non-standard drawing conventions—are handled via active learning loops where planner corrections retrain the model, continuously improving accuracy across jurisdictions and drawing styles.

It's like giving a building inspector X-ray glasses that instantly measure every wall, setback, and parking spot on a blueprint without ever picking up a ruler.

It's like having a seasoned zoning attorney who has studied every past ruling in your city and can tell you exactly how to tweak your project to get approved on the first try.

AutoSitu's most strategically differentiated ML use case builds a precedent reasoning engine that ingests and structures thousands of historical municipal development decisions—approvals, denials, conditional approvals, variance grants, and appeal outcomes—across multiple jurisdictions. Using a combination of NLP-driven document understanding and graph-based knowledge representation, the system maps relationships between project characteristics (use type, density, neighborhood context, applicant history) and regulatory outcomes. When a new application is prepared, the engine computes a predicted approval probability, surfaces the most analogous historical cases (with outcome explanations), and generates actionable recommendations for modifying the application to improve approval odds—such as adjusting setbacks, adding landscaping buffers, or revising parking counts. This transforms the development review from a reactive, adversarial process into a proactive, data-driven collaboration between applicants and municipalities. Over time, the precedent database creates a powerful network effect: each new decision enriches the model, making predictions more accurate and recommendations more jurisdiction-specific, building a defensible data moat that competitors without municipal partnerships cannot easily replicate.

It's like Moneyball for building permits—using data from every past game to tell you exactly which pitch to throw so the umpire calls it a strike.