Historical client platform / 01
AnalyzTech
A production AI system connecting construction drawings, technical documents, computer vision and agent-assisted verification.
Construction document intelligence · Computer vision · Operational software
Historical work led by Hugo Sequier before State Method · Client: BTP Consultants
- Client
- BTP Consultants
- Delivery
- R&D → MVP → Production
- Team
- Solo developer
- Role
- End-to-end AI Engineer & Data Scientist
Historical client project / no client application images shown
Case overview
Problem and system
- Problem
- Manual review covered documents of more than 40 pages and plan-based checks.
- System
- An R&D-to-production platform combined backend APIs, OCR and computer vision pipelines, polygon visualization, verification skills and AWS deployment. Hugo delivered the work as a solo developer.
Evidence / linked source
Source-reported result
- Productivity gain
- +50%
- Classification precision
- 99%
- Room-mask precision
- 96%
The canonical source reports a 50% productivity gain, 99% classification precision and 96% room-mask precision in this historical project. Detailed baselines are not public.
Detailed baselines and calculation methods are not public. This result belongs only to the historical project context.
Canonical AnalyzTech case study ↗Production architecture
From construction PDF to agent-assisted verification.
This architecture and stack are reproduced from the canonical project source. They describe implemented historical work, not a generic capability diagram.
- 01Receive the construction plan PDF and project context.
- 02Use Kreo to detect walls and doors.
- 03Use YOLO to detect furniture and open doors.
- 04Extract rooms and their geometric relationships.
- 05Use a vision-language model to classify rooms.
- 06Post-process geometry, classifications and project metadata.
- 07Route the structured context to AI-agent verification skills.
- 08Expose plan polygons and verification actions through the review interface.
- 09Run the production APIs and model infrastructure on AWS.
Verified stack and integrations
Application and data
- Python
- FastAPI
- PostgreSQL
- Frontend plan visualization
Document and vision AI
- OCR
- YOLO
- Vision-language models
- Room-mask extraction
Geometry and construction processing
- Kreo integration
- Shapely
- Polygon post-processing
- Verification skills
Production infrastructure
- AWS Lambda
- AWS SageMaker
- AWS ECS
- AWS ECR
- AWS ELB
State Method relevance
Boundary, authority and review.
- System boundary
- This public summary covers processing through polygon visualization and the verification interface, including the backend and deployment named in the source. It does not define or make claims about a wider client workflow.
- Human / control relevance
- Interpretation sits beside structured geometry, application state and a verification interface. The model output is therefore described as one part of operational software, not as a standalone decision.
Do not generalize
Context limits
- 01These results belong to the named historical project context.
- 02Detailed baselines and calculation methods are not public.
- 03The client did not contract with State Method, and this page does not imply endorsement of State Method.
- 04The result does not transfer to a different organization, dataset or workflow.