AI / Computer Vision Developer – Architectural Drawing PDF Intelligence
Rozpočet: $15.0 - $30.0
HOURLY / PART_TIME
⭐ 4.99 (40)
United States
ocr-tesseract, opencv, machine-learning, natural-language-processing, text-recognition, computer-vision, image-processing, python
Preferované kvalifikácie
- Lokalita: Bangladesh, Pakistan, India
- Skúsenosť: Stredne pokročilý
- Angličtina: Konverzačná
- Job Success: 80%+
- preferovaný Rising Talent
We are a commercial millwork engineering company developing an AI-assisted system for interpreting large architectural drawing packages.
We are looking for an experienced AI / Computer Vision / Document Intelligence developer to build a focused paid prototype.
This is NOT a chatbot project.
Our typical input may include a 300–700+ page architectural/interior PDF drawing set plus a scope document identifying a specific millwork or cabinet run.
Example:
Series 2300
Reception Desk
Level 3
Room 312
The prototype needs to identify the architectural information related to that specific scope item, including items such as:
Overall floor plan
Enlarged plan
Interior elevations
Sections
Details
Reflected ceiling plan (RCP)
Finish plans and schedules
Hardware information
Equipment information
Other directly referenced drawings
A major part of the challenge is following architectural drawing references.
For example:
Room 312 → A521 → Elevation 4/A621 → Section 7/A742 → Detail 12/A811
The system must recognize and follow relationships like these through a large multi-page PDF drawing package.
PHASE 1 PROTOTYPE
For this engagement, we are NOT asking for the complete production system.
The prototype should:
Ingest a large architectural PDF drawing set.
Ingest a written scope description identifying a particular cabinet/millwork series.
Index the drawing package by sheet number, sheet title and available drawing content.
Identify candidate sheets relevant to the selected series.
Recognize common architectural references such as 4/A621.
Follow those references to additional relevant sheets, sections and details.
Rank candidate information by relevance/confidence.
Preserve traceability to the original PDF, sheet and page.
Produce structured output that can later feed PDF-generation and AutoCAD-automation systems.
We will provide completed real-world projects for testing where we already know the correct architectural references.
RELEVANT EXPERIENCE
We are particularly interested in experience with some combination of:
Python
Computer Vision
OpenCV
PDF parsing
Vector PDF extraction
OCR / text recognition
Document AI
Multimodal AI models
Machine Learning
Natural Language Processing
Object detection / image analysis
Structured data extraction
Experience with AutoCAD, Autodesk APIs, DWG/DXF, Revit, architectural drawings, construction documents, engineering drawings or construction technology is a major plus.
IMPORTANT TECHNICAL CONSIDERATIONS
Architectural PDFs may be native vector PDFs exported from AutoCAD/Revit, raster scans, or a combination of both.
We are not looking for someone whose entire proposed solution is simply OCR, RAG, embeddings or a chatbot wrapper.
We expect the developer to determine which portions of the problem are best handled using deterministic PDF/vector extraction, OCR, computer vision, rules/geometry, multimodal models and/or LLMs.
FUTURE OPPORTUNITY
If Phase 1 succeeds, future stages may include:
Automatic cropping of individual architectural views
Creation of condensed series-specific PDF drawing packages
Drawing revision comparison
Automated drawing-reference graphs
Finish/equipment coordination
AutoCAD PDF import
Automated scale verification
Creation of series-specific DWG mobilization files
This could become a substantial ongoing development project for the right person.
HIRING PROCESS
We expect to select a small number of finalists for a paid technical test using the same real-world sample drawing package.
The strongest candidate will then be selected to continue development of the Phase 1 prototype.
Please describe the closest project you have personally built involving document AI, computer vision, PDFs, OCR, engineering/construction drawings or similar structured technical documents.
Generic proposals that do not address the actual technical problem will not be considered.
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