ORVINUSComputer Vision Systems
Cameras and documents generate data nobody reads. Vision systems turn both into structured records your software can act on.
Computer vision systems are AI models that extract structured information from images and documents — detecting and counting objects, recognizing and classifying what appears in a frame, reading text through OCR, and pulling fields out of scanned paperwork — so visual data becomes database rows instead of unexamined files.
The gap between a vision demo and a vision system is the messy ten percent: bad lighting, skewed scans, occluded objects, and document layouts nobody warned you about. We engineer for that tail deliberately — training data that matches deployment reality, confidence thresholds that route uncertain cases to human review, and serving infrastructure sized for your actual throughput and GPU economics.
The document side of this work runs in production today: the document processing we built for doorlist.ai turns real-estate paperwork into structured data. As with all our models, accuracy is measured on your data before launch — never quoted from someone else's benchmark.
Computer vision systems extract structured information from images and documents through object detection, image recognition, OCR, and field extraction. ORVINUS builds computer vision and image recognition models and AI data extraction pipelines — including the real-estate document processing built for doorlist.ai — with confidence scoring, human review workflows, and accuracy measured on client data.
Computer Vision & Image Recognition
We build vision models fitted to your imagery, not benchmark photos: object detection and counting, image classification, visual similarity search, and quality inspection — trained via transfer learning from strong pretrained backbones, so useful accuracy arrives with hundreds of labeled images rather than millions. The training set is engineered to match deployment reality — your cameras, your lighting, your edge cases — because that gap is where vision projects quietly fail.
Serving is designed around throughput economics: GPU-accelerated batch pipelines for archive-scale processing, real-time inference where latency matters, and model optimization — quantization, pruning — that cuts hardware cost without giving up measured accuracy. Confidence thresholds route uncertain frames to human review, and every prediction is logged, which turns future disputes into simple lookups.
AI Data Extraction Pipelines
Data extraction pipelines turn document piles into database records: OCR tuned for real-world scans, layout-aware models that find fields wherever a template puts them, and validation logic that checks extracted values against business rules — dates that parse, totals that sum, IDs that cross-reference — before anything enters your systems. We built exactly this for doorlist.ai's real-estate document processing.
Every extracted field carries a confidence score, and the pipeline is honest about uncertainty: high-confidence values flow straight through, low-confidence ones queue for human review, and each correction becomes training data that improves the next pass. Structured output lands directly in your database or workflow, with an audit trail linking every field back to its exact location on the source page.
Built With
The technologies we reach for on this work — and why we use each one.
See what we use every tool for in the full technology stack.
What You Get
Trained vision model
Detection, recognition, or extraction models trained on your imagery, with accuracy measured on a held-out set from your data.
Processing pipeline
Ingestion, preprocessing, inference, and validation as one monitored flow — batch or real-time as the workload demands.
Review workflow
Confidence-routed human review queues that keep quality high and turn every correction into training data.
Integration & monitoring
Structured output into your systems, plus accuracy and drift monitoring so performance stays visible after launch.
Other Applied AI Specializations
Machine Learning Development
Custom neural networks and deep learning models designed, trained, and shipped — as production APIs or complete AI SaaS platforms.
Explore ML DevelopmentPredictive Analytics Engines
Forecasting and reporting engines that turn historical data into decisions — demand, churn, risk, and market signals with honest error bars.
Explore Predictive AnalyticsMulti-Agent AI Systems
Teams of specialized AI agents that execute complex workflows — with the orchestration, guardrails, and monitoring to trust them.
Explore Multi-Agent SystemsNLP & Sentiment Analysis
Language systems that read at scale — classification, sentiment, entity extraction — wired into your CRM and content operations.
Explore NLP & SentimentWhere This Work Lands
Operations & Automation
Multi-step workflows orchestrated by AI — with intelligent routing, error handling, and monitoring built in.
AI for Operations & AutomationReal Estate & PropTech
Property matching, lead intelligence, and document automation for teams that move faster than the market.
AI for Real Estate & PropTechE-Commerce
Personalization, automation, and predictive intelligence for stores that compete on experience.
AI for E-CommerceCommon Questions
How many labeled images do we need to start?
Fewer than most teams fear. Transfer learning from pretrained backbones typically reaches useful accuracy with a few hundred well-chosen labeled images per class; thousands make it robust. Discovery includes a data review, and where labels are missing we set up an efficient labeling workflow — often the fastest week of the whole project.
Can it handle poor-quality scans and photos?
That's the job. Pipelines are built around preprocessing — deskewing, denoising, contrast normalization — and trained on your real samples rather than clean examples, so quality issues are represented in training. Documents the model genuinely can't read route to human review by confidence score instead of producing silently wrong data.
What extraction accuracy should we expect?
We don't quote numbers before seeing your documents — per-field accuracy is measured on a held-out sample of your real paperwork during the build. What you control is the automation-versus-review tradeoff: confidence thresholds decide how much flows straight through and how much gets human eyes, so overall pipeline quality is a dial, not a gamble.
Does this run in the cloud or on our hardware?
Either, decided by throughput, latency, and data sensitivity. Cloud GPU serving suits batch document processing and variable load; on-premise or edge deployment fits camera streams and data that can't leave your network. Everything ships containerized, so the same system runs in both places — the choice is sized during discovery.
Ready for Computer Vision?
Free discovery call — we scope the work, name the trade-offs, and respond within 24 hours.