Surface NER routing provenance (ModelOrigin, NerModelsInvoked) through the pipeline and WPF harness so English/Tamil routing is observable during POC validation. Consolidate docs into solution-guide and add NER logs, topic filtering, and batch UI fixes in the test harness.

README.md

@@ -11,13 +11,9 @@ Financial and customer-service prompts often contain regulated data (names, gove
 3. Replace values with stable placeholders
 4. Send only the **sanitized** prompt to an LLM (mocked for now)
 
-## Architecture
+## Documentation
 
-For solution design, data-flow diagrams, trust boundaries, and project responsibilities, see **[docs/architecture.md](docs/architecture.md)**.
-
-For English and Tamil ONNX NER model IDs, assets, routing, and reproduction steps, see **[docs/ner-models.md](docs/ner-models.md)**.
-
-**Planned:** Tamil / Tanglish person-name support via dual ONNX NER routing — see **[docs/tamil-tanglish-ner-plan.md](docs/tamil-tanglish-ner-plan.md)**.
+Full solution reference (architecture, NER models, routing, Tamil/Tanglish, Git setup, improvement roadmap): **[docs/solution-guide.md](docs/solution-guide.md)**
 
 ## Why Three Detection Strategies?
 
@@ -59,7 +55,7 @@ models/                             # Optional ONNX model files (gitignored)
 
 ## Build and Run
 
-For pushing this repository to Xenovex Git (`xts.xenovex.com`), see **[docs/git-xenovex-setup.md](docs/git-xenovex-setup.md)**.
+For pushing this repository to Xenovex Git (`xts.xenovex.com`), see **[docs/solution-guide.md § Git remote setup](docs/solution-guide.md#11-git-remote-setup-xenovex)**.
 
 From the repository root:
 
@@ -96,12 +92,12 @@ dotnet run --project src/PiiRedaction.TestHarness.Wpf
 
 **Workflow:**
 
-1. **Select a test prompt** from the left panel (grouped by language: English, Tamil, Mixed, Tanglish) or type your own prompt in the input box.
+1. **Select a category** from the dropdown (e.g. **Career Guidance**, **Banking & Financial**) or leave **All** to see every prompt. Use the search box for finer filtering.
 2. **Click a test prompt** in the left panel to load it into the input box (previous results are cleared automatically).
 3. Click **Redact** to run the full detection pipeline. The status bar shows model availability, script composition (LatinOnly / TamilOnly / Mixed), and elapsed time.
 4. Review **Sanitized Output**, detected entities, and the placeholder map in the right panel. A leak warning appears if any detected value remains in the sanitized text.
 5. Optionally click **Send Mock LLM** to send only the sanitized prompt to the mock LLM.
-6. Click **Run All** to execute all **22 curated scenarios** (16 console samples + 6 harness-only edge cases) and view pass/fail results in the batch panel.
+6. Click **Run All** to execute scenarios in the **selected category** (or all when **All** is chosen) and view pass/fail results in the batch panel.
 
 The harness uses the same DI registrations and `IPromptSanitizer` pipeline as the console app, with thin application services (`IRedactionAppService`, `ITestPromptCatalog`, `IScriptAnalysisService`, `IModelStatusService`) following SOLID principles.