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CAD Intelligence

Operational source:

C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence

This page mirrors the detailed operational tracking for this track so the deployed wiki shows the same score tables as the source TRACKING.md.

Last updated

2026-04-29

Scoring model

This track uses milestone and feature scorecards.

Score guide:

  • 100 means complete, validated, and stable for current scope
  • 90-99 means strong and working, but still carrying meaningful gaps
  • 75-89 means materially working, but still missing important completeness
  • 50-74 means real foundation exists, major parts are still absent
  • below 50 means early, partial, or mostly planned

For this mixed code-and-research track, 100 requires active plan evidence plus working implementation or evaluated benchmark results for the specific milestone.

Evidence sources reviewed

  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\MASTER_PLAN.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim
  • D:\02_Code\49_yolotraining_firstdataset
  • D:\02_Code\50_CVAT_RoboFlow
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260315_design-review-mode-boundary.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260318_candidate-viewer-guardrails.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260407_part-facts-refresh-latency-plan.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260408_rule-driven-review-preparation-pipeline-plan.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260408_cad-intel-perf-accuracy-plan.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260408_dfm-review-pdf-export-plan.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260408_railway-one-service-simplification.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260417_cnc-physics-family-rule-rollout-plan.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260418_dfm-rules-and-localization-guidance.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\handover\260424_dfm-scanner-pipeline-dev-loop.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\docs\validation\injection-molding-dev-loop-review.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\docs\contracts\dfm-scan-pipeline.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\docs\validation\dfm-scan-benchmark.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\docs\contracts\collaboration-local-dev.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\docs\validation\cad-format-import-eval.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\web\src\components\CollaborationWorkspace.tsx
  • D:\02_Code\45_merged_macos_colabui_dfmanim\server\collab_review_store.py
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\04_playbooks\DRAWING_MODEL_TRAINING_AND_CVAT_MODEL_SERVING.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\docs\04_DFM_Research\_INDEX.md

Current overall score

CAD Intelligence overall score: 83 / 100

Why the score is not higher: The integrated runtime is now much more real and pilot-usable than it was in March. The largest new gain is injection molding: the plastics route now has a rules-bar process view, Light/Deep scan control, scan-depth-aware Part Facts, product-vs-tooling gating, richer boss/rib/draft/side-action evidence, and a validated six-sample STEP corpus. The April 24 scanner-pipeline loop also made heavy STEP review more transparent with scanner-level progress, cache state, selected-component scope checks, and a first bounds-aware multipart component resolver. The labeling and training operations around CVAT, Vast.ai, W&B, YOLO, and Roboflow are also documented and materially working enough to support repeatable experiments. The score is not higher because deep scan can still be slow on heavy geometry, Part Facts is still too much of an orchestrator, topology-linked localization is not solved, and bounded geometry edits remain absent.

2026-04-29 share-priority update: CAD Drive collaboration is now strong enough for a small trusted-user trial today. The score only moves modestly because the MVP still relies on short polling, activity is not yet a durable backend stream, carry-forward/re-pin UX is missing, and non-STEP import support is honest in metadata but not fully robust across the runtime path.

2026-04-29 hosted-runtime update: app.rapiddraft.ai is now the Stytch-gated user-facing app domain, pilots.rapiddraft.ai redirects there, and DraftLint is connected to the IONOS CVAT/Nuclio detector gateway through draftlint-gateway.rapiddraft.ai. Detector IDs 10 through 15 are visible from the live app, scan limits are 20/minute and 100/user/month, and the missing production vision_usage_events migration has been deployed. A production-only UI flag to hide pinned standards, Batch Mode, and Design Review has been pushed but was still queued in Railway at last verification.

Milestone scorecard

Milestone Score / 100 Current state What it helps achieve Main remaining gap
1. DFM benchmark foundation 88 Strong and expanding Provides the core manufacturability intelligence and rule discipline. Still needs tighter benchmark reporting alignment across CNC and plastics paths.
2. Feature detection and classification 79 Materially working Identifies manufacturability-relevant evidence families for active review. Accuracy and confidence still vary on harder geometry and assemblies; long OCC detectors still need scanner-unit extraction.
3. Geometry localization 52 Improving but not solved Links findings back to real geometry for review and later remediation. Selected-component scope is safer, but topology-linked targeting is still not trustworthy enough.
4. Injection-molding benchmark 74 Materially working Extends the track beyond CNC with an active plastics rules bar and Light/Deep scan validation path. Still needs broader scored benchmark discipline and hosted runtime verification.
5. CAD Drive collaboration wedge 85 Live small-group rollout path Opens a simpler pilot path around CAD sharing, pinned comments, versions, and documentation without forcing DFM adoption. Strong enough for a small trusted share today on app.rapiddraft.ai; durable backend activity, SSE/WebSocket, carry-forward/re-pin UX, side-by-side compare, email notifications, authenticated external-user smoke testing, and robust non-STEP import routing remain open.
6. Bounded edit loop 18 Very early Enables one trusted AI-assisted geometry edit path. No production-worthy direct edit loop exists yet.
7. Labeling and evaluation operations 77 Materially working Supports repeatable data creation, training, and hosted CVAT/Nuclio detector experiments. IONOS gateway serving is live for DraftLint detector access from Railway, but model quality, dataset scale, and end-to-end drawing-analysis eval coverage still need work.

Feature scorecard

Feature track Score / 100 What it helps achieve Current note
CNC DFM 86 Gives the product a strong manufacturability baseline. Still the deepest and most trusted capability family.
Injection-molding DFM 74 Broadens manufacturing coverage. Now has 12 active plastics rules, five J-book plastics rules, Light/Deep scan, tooling gate, and six-sample validation.
Feature detection 79 Helps turn geometry into actionable engineering facts. Deep scan now extracts richer molded bosses, ribs, draft, side actions, and sink/mass screening evidence; scanner-level timings now expose where heavy work is happening.
Geometry mapping 52 Makes findings spatially trustworthy. Plastics validation localizes rule violations well, and multipart selected-component scans now have bounds/scope guards, but topology-linked remediation targeting is still not solved.
CAD Drive collaboration 85 Gives pilots a low-friction CAD feedback and version-history workflow before they adopt manufacturing intelligence. Dedicated workspace, share-link landing, pinned comments/replies, markdown, revision click-through, version-origin chips, Stytch auth, and short-poll refresh are ready for a small trusted trial. Email invite/admin UI should not be over-promised while the visible share flow is simplified around links first.
Labeling pipeline 77 Supports repeatable data creation and hosted detector serving. CVAT, Vast.ai, W&B, model-serving paths, and the IONOS DraftLint gateway are now reusable enough for Railway to call real detector models, but data coverage and model quality are still thin.
Benchmark reporting 78 Makes progress measurable across runs. Injection molding now has a concrete harness, but cross-process scoring/reporting still need consolidation.
Bounded edit capability 18 Enables AI-assisted geometry modification. Still more aspiration than validated workflow.

Current headline assessment

CAD Intelligence is no longer just a CNC-heavy benchmark foundation. It is now a real integrated review runtime with rule-driven preparation, selective evidence planning, persisted Part Facts warmup, Light/Deep scan control for injection molding, plastics product-vs-tooling gating, scanner-level progress, selected-component scope validation, pilot-facing outputs such as DFM PDF export, and a documented drawing-model operations stack that spans CVAT labeling, Vast.ai training, W&B run tracking, and CVAT-linked model testing.

The track is still held back by geometry-localization trust and the absence of one bounded edit workflow that can be defended end to end.

Active rollout updates

2026-04-29 - DraftLint hosted gateway and scan limits

DraftLint is now a hosted drawing-analysis path. Railway reaches the IONOS CVAT/Nuclio model gateway through https://draftlint-gateway.rapiddraft.ai; the live app reports gateway mode and lists detector IDs 10 through 15.

Scan limits are active for signed-in users: 20 DraftLint/vision calls per minute and 100 per UTC calendar month. The production 500 from the missing quota table was fixed with the 260429_0001 Alembic migration, and the deployed app passed health, readiness, gateway live, and detector-list checks.

2026-04-29 - Production surface polish queued

The app now has a frontend visibility layer that keeps internal surfaces in local development but hides pinned standards, Batch Mode, and Design Review from production builds by default. This is committed as 1af25e4 and locally build-validated, but Railway was still queued at last check, so it is not yet live on app.rapiddraft.ai.

2026-04-29 - Collaboration share-readiness scoring refresh

The current priority for this week is to share CAD Drive with a few trusted people. It is now scored as a share-ready pilot MVP: signed-in workspace, owned/shared access foundation, share links, pinned comments/replies, markdown, revision upload/list/switch, origin-revision chips, and short-poll updates while a part is open. The honest gaps are durable backend activity, SSE/WebSocket, carry-forward/re-pin UX, side-by-side compare, email notifications, and stronger non-STEP import routing.

2026-04-24 - DFM scanner-pipeline modernization loop

The DFM review pipeline is being moved from evidence-family-only progress toward scanner-level transparency and safer selected-component behavior.

Current rollout policy:

  • keep review-v2 and Part Facts payload shapes stable while adding scanner status fields
  • expose planned scanners, active scanner, elapsed time, budget/overtime state, timing, and cache state
  • keep Light scan as the default fast path and use GE_JET_BRACKET_V_3.0.stp as the heavy regression fixture
  • make multipart STEP review selected-component scoped by default
  • reject escaped scanner evidence before it becomes misleading issue cards
  • refactor Part Facts into scanner artifacts only after additive status/scope behavior is validated

Completed April 24 slices:

  • scanner status rows now carry started_at, budget_ms, overtime_ms, and cache state
  • scanner planning has table-driven coverage for CNC light/deep, plastics, sheet metal, drawing-only, and assembly-only routes
  • review orchestration has started moving out of main.py into a dedicated pipeline class
  • CNC corner extraction can reuse the already loaded resolved OCC shape instead of reloading the STEP
  • scanner artifacts are written atomically and expensive wall/molded-signal outputs have cache artifacts
  • selected-component scope metadata prevents multipart escaped evidence from being presented as valid review findings
  • single-component GE scans are exempt from multipart-only scope rejection
  • selected-component resolution now prefers OCC solids whose CAD bounds match the selected scene component before falling back to ordinal component_N

Validation:

  • focused CNC/Part Facts bounds resolver tests: 56 passed
  • broader DFM/canonical scene subset after bounds resolver: 79 passed, 1 skipped
  • previous full touched DFM backend suite: 140 passed
  • frontend build in web: passed with the existing chunk-size warning
  • GE heavy benchmark through local API: Light review 269.4s, Deep review 467.7s, repeat cached Deep review 3.1s

Remaining risk:

The scope guard and bounds-aware resolver improve safety, but Deep scan is still too slow on heavy geometry. The next architecture loops are scanner-unit extraction, Part Facts as a materialized view over scanner artifacts, and progressive/deferred Deep scan.

2026-04-21 - Injection molding Light/Deep scan and plastics rule coverage active

Injection molding now has a credible active review path in the RapidDraft integration repo.

Current rollout policy:

  • plastics and injection_molding resolve to the same Injection Molding rules-bar view
  • 12 plastics rules are active by default: 6 implemented and 6 heuristic screening rules
  • J_BOOK_PLASTICS contributes five boss/rib rules to the plastics route
  • Light scan remains the default fast review path
  • Deep scan adds richer ribs, bosses, collars, pins, gussets, draft, side-action grouping, sink/mass screening, bbox localization, and face-index localization
  • mold/tooling assemblies are gated out of product-part findings and receive a clear selection warning
  • validation harness runs both Light and Deep scan over the six STEP samples, with Deep scan completing 6/6 and localizing all rule violations in the validation summary

2026-04-13 - Drawing-model training and CVAT serving workflow captured

The drawing-analysis side of the track now has a reusable operating path instead of scattered local memory.

Current rollout policy:

  • CVAT on the Fedora mini PC is the shared labeling and model-serving surface
  • Nuclio-backed model functions must preserve the trusted-origin setup for both cvat_server and cvat_worker_annotation
  • Vast.ai is the preferred GPU training path for YOLO experiments
  • W&B is the preferred remote run-history and dashboard surface
  • YOLO and Roboflow experiments must be isolated by branch or worktree, but they share the same CVAT runtime constraints

Highest-value next actions

  1. Run a live two-person CAD Drive validation on the deployed app before sending links.
  2. Share the collaboration feature today with a tiny trusted group using one prepared sample model and a short feedback ask.
  3. Patch only share-blocking collaboration issues this week.
  4. Add durable backend activity plus SSE/WebSocket replacement for short polling, then carry-forward/re-pin comments across versions.
  5. After the collaboration trial is stable, return to topology-linked localization, the first bounded edit class, and consolidated CNC/plastics benchmark reporting.

Sources

  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\MASTER_PLAN.md
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\00_Project_Management_n_skills\01_tracks\cad-intelligence\TRACKING.md
  • D:\02_Code\45_merged_macos_colabui_dfmanim\docs\contracts\dfm-scan-pipeline.md