Document vision
Layouts, handwriting, seals, and complex forms beyond plain OCR.
Detection · Document vision · Edge to cloud
See what your operations miss
Production computer vision and multimodal systems — inspection, document vision, visual search, and edge/cloud pipelines integrated with your operations. From defect detection on the factory floor to multimodal copilots that reason over images and text.
EdgeReady
MultiModal
ProdMLOps
OpsIntegrated
Vision for operations
Computer vision that reads, inspects, and acts
We build vision systems that read documents, inspect assets, and understand images and video in context — often combined with LLMs for multimodal reasoning and workflow automation.
Inspection & QA
Defect detection and visual compliance checks on production lines.
Multimodal copilots
Combine images with RAG and agents for decision support.
Edge to cloud
On-device inference when latency or connectivity requires it.
Where vision fits
From capture to action
Vision AI spans the full operational stack — we scope the right model and deployment pattern for each use case.
Quality inspection
Real-time defect detection on assembly lines with human-in-the-loop review queues.
Document processing
Structure extraction from invoices, claims forms, and handwritten submissions.
Video analytics
Event detection, sampling, and alerting from camera feeds and drone footage.
Mobile capture
Guided photo capture with on-device inference for field teams and adjusters.
Decision guide
Classic CV vs multimodal LLMs
Neither alone covers every vision task — we architect the right blend for precision, flexibility, and cost.
Precise localization
- Bounding boxes, segmentation, and class scores
- Fast inference at the edge (YOLO, ViT detectors)
- Deterministic outputs for regulated workflows
- Best for known object classes and environments
Flexible understanding
- Reason over images + text in natural language
- Handle novel layouts and unstructured scenes
- Combine with RAG and agents for workflows
- Often paired with classic detectors for best results
What we deliver
Vision AI capabilities
From custom model training through MLOps and enterprise integration — production vision systems, not research demos.
Detection & Classification
Custom models trained on your classes, environments, and lighting conditions — with active learning to improve from production feedback.
- Object detection & instance segmentation
- Custom class taxonomy for your domain
- Edge-optimized model export (ONNX, TensorRT)
Document Understanding
Structure extraction from complex visual documents — layouts, tables, handwriting, seals, and signatures beyond plain OCR.
Multimodal RAG
Image + text retrieval for grounded answers over visual knowledge bases and document archives.
Video Pipelines
Frame sampling, event detection, and alerting from live feeds and recorded footage.
MLOps for Vision
Dataset versioning, active learning loops, drift monitoring, and model retraining pipelines.
Enterprise Integration
Hooks into claims systems, EHR, CMMS, and ops tools — vision outputs that trigger real workflows.
Our process
Data to deployment
Structured delivery from use-case scoping through model training, edge optimization, and production monitoring.
Use case & data audit
Define success metrics, assess existing imagery, and plan labeling strategy — weak labels, synthetic data, or active learning.
Week 1–2Model development
Train and evaluate detectors, classifiers, or multimodal pipelines on your representative dataset.
Week 2–6Edge & integration
Optimize for target hardware, wire into claims/EHR/CMMS APIs, and build review workflows.
Week 4–8Deploy & monitor
Production inference, drift detection, active learning from edge cases, and continuous model improvement.
Week 8–12
Technology
Vision stack
Models, runtimes, and platform infrastructure — selected for your accuracy, latency, and deployment constraints.
Layer 01
Models
Layer 02
Runtime
Layer 03
Platform
Proof of work
Vision case studies
Production computer vision in insurance — from cloud multimodal pipelines to on-device mobile capture.
AI Insurance Claims Processing
Multi-modal AI claims pipeline — vision transformers for damage assessment, NLP for classification, and fraud scoring.
11→3.5dProcessing time
$1.1MAnnual savings
Mobile-First Insurance Claims
YOLOv8 on-device damage detection with guided photo capture and AI-assisted triage for field adjusters.
94%Complete submissions
54%Faster settlement
EdgeDeployment
HybridCV + VLM
15+Years experience
200+Projects shipped
01Do you use multimodal LLMs or classic CV models?
Both. Classic detectors excel at precise localization; multimodal LLMs excel at flexible understanding. We often combine them — detectors for fast, deterministic bounding boxes and VLMs for reasoning over complex scenes and document layouts.
02Can vision models run on-prem or at the edge?
Yes. We deploy to edge devices or private cloud when bandwidth, privacy, or latency requires it. Models are optimized with ONNX, TensorRT, Core ML, or TFLite depending on your hardware targets.
03What data do we need to start?
A representative labeled set helps. We can also bootstrap with weak labels, synthetic data, and active learning — collecting edge cases from production to continuously improve accuracy.
Next step
Ready to build vision AI?
Tell us about your use case — we'll design a vision architecture and provide a detailed estimate within 48 hours.