Natural-language spatial query, automated QA/QC, deliverable-compliance checking, and audit-trail-ready workflow runs — features that aren't tied to one industry but make every industry workflow better.
Some capabilities don't belong to one vertical — they make every vertical workflow better. NL query lowers activation friction, QA/QC catches deliverable defects before submission, audit trails get you through inspections.
PostGIS SQL required for spatial questions analysts can't write
QA/QC done eyeball-only against agency specs
Deliverable rejections after submission, not before
Audit trails assembled retroactively from emails
Cross-cutting platform workflows that complement industry-specific deliverables.
| Workflow | Name & what it automates | Time saved |
|---|---|---|
| #281 | Natural Language Spatial Query Plain-English question → PostGIS SQL → map result | — |
| #282 | NL Geo Assistant Multi-step pipeline orchestration via natural language with 7 tool functions | — |
| #283 | Automated Geospatial Report from Prompt NL description → complete report with maps + tables + narrative | — |
| #287 | LiDAR QA/QC Suite USGS Lidar Base Specification compliance check — point density, voids, classification | — |
| #290 | Raster QA/QC Suite Per-band statistics, NoData distribution, edge artifacts, projection check | — |
| #292 | Deliverable Compliance Checker Validates package against EPA / FEMA / USACE / agency-specific schema | — |
| #293 | Data Quality Report Card Single-page PDF with pass/fail/warning across all QA/QC metrics | — |
Modeled on a 50-analyst geospatial team adopting NL query and automated QA/QC across all delivery work.
| Metric | Manual / current tooling | GeoDataConverter |
|---|---|---|
| Analyst spatial-query velocity | PostGIS SQL by power users | Plain-English by anyone |
| Pre-submission QA/QC cycle | Hours to days | Minutes |
| Deliverable rejection rate | 10–15% | <2% |
| Audit trail completeness | Reconstructed | Per workflow run |
Workflow #281 lets analysts ask questions like "show me all wetlands within 500 meters of the proposed alignment that intersect FEMA flood zone AE" without writing SQL. The query layer runs Vanna.ai over your schema metadata, generates the SQL, executes against your PostGIS, and returns a map. The model layer is provider-agnostic via OpenRouter — QWEN3, DeepSeek R1, and ERNIE 4.5 are all wired in.
Pick a current project. Run NL query against its data, run QA/QC on the deliverable, and compare against your current process.