Object Detection & Classification
Detect, locate, and classify objects in images and video in real time. We build custom models using YOLO, Detectron2, and vision transformers trained on your specific domain and data.
AI & MACHINE LEARNING
Computer Vision Systems
We build custom computer vision solutions — from real-time object detection and quality inspection to video analytics and document intelligence — deployed to edge devices or cloud at production scale.
40+
CV Systems Deployed
99.2%
Detection Accuracy
Real-Time
Processing Capability
Fixed scope, fixed price
A number before we write code
Your IP from day one
Full ownership on final payment
Senior engineers only
No juniors on your budget
Reply within 1 working day
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Our Capabilities
Image Segmentation
Pixel-level understanding of scenes with semantic and instance segmentation. Ideal for medical imaging, autonomous systems, satellite imagery analysis, and industrial quality control.
Video Analytics
Extract intelligence from video streams — activity recognition, object tracking, crowd counting, anomaly detection, and behavioral analysis for security, retail, and industrial applications.
OCR & Document Intelligence
Extract and structure text from scanned documents, handwriting, receipts, and ID cards using deep learning OCR. Goes beyond text extraction to understand document layout and relationships.
Quality Inspection Systems
Automated visual inspection for manufacturing — detect surface defects, dimensional errors, assembly mistakes, and contamination with superhuman consistency at production line speeds.
Facial Recognition & Biometrics
Compliant face detection, recognition, and liveness detection for access control, identity verification, and attendance systems. Built with privacy-by-design and regulatory compliance in mind.
How we work
Fixed milestones, working software every fortnight, and no surprises on the invoice.
- 01
Data Assessment
We evaluate your existing image/video data, identify annotation requirements, assess data quality, and determine if additional collection or augmentation is needed to train a reliable model.
- 02
Model Selection
We select the optimal architecture — YOLO variants for real-time detection, ViT for high-accuracy classification, SAM for segmentation — and choose between training from scratch or fine-tuning foundation models.
- 03
Training & Validation
Rigorous model training with cross-validation, precision/recall optimization, and domain-specific augmentation. We benchmark against your accuracy and latency requirements before signing off.
- 04
Edge / Cloud Deploy
Optimized deployment to your target environment — NVIDIA Jetson and Raspberry Pi for edge, or GPU cloud for scale. Model quantization and TensorRT optimization for maximum throughput.
Work we have shipped
Accountable partners, not outsourced vendors
How we price, who we staff and what happens after launch — the parts that decide whether a project actually lands.
Fixed scope, fixed price
You know the cost and the timeline before a line of code is written. No hourly billing, no scope-creep surprises.
You own everything
Full source code, credentials and intellectual property transfer to you on final payment. We sign the IP assignment on day one.
Senior engineers only
Every project is staffed by engineers with years of production experience — no juniors learning on your budget.
Canadian data residency
Built for PIPEDA and provincial privacy law, with data kept on Canadian infrastructure where it matters.
8-week MVP programme
A focused, fixed-price path from idea to a launched product, refined across dozens of startup engagements.
Support after launch
Tiered SLAs from monitoring and bug fixes to a dedicated team with 24/7 response. Most clients stay for years.
Recognition
Independent awards and partner tiers, each one earned on delivered work.
Top Generative AI Company
Top App Development Company
Webby Honoree
Best App Award, Personal Growth
Advanced Tier Services Partner
Top 100 North America
Compliance
Regulation is a design constraint here, accounted for from the first architectural decision rather than audited in at the end.
Privacy
Personal data handled to the letter of Canadian and international law.
PIPEDAGDPRHIPAACCPA
Security
Threat modelling, least-privilege access and audited controls.
SOC 2 Type IIISO 27001Zero trust
AI governance
Model evaluation, human oversight and an audit trail.
ISO 42001AIDA-readyModel cards
Sector
The regulations your industry answers to, designed in from day one.
PCI DSSHL7 / FHIROSFI
Cloud
Regional residency, encryption in transit and at rest, tenant isolation.
Canadian data residencyAWS Well-Architected
Accessibility
Built and tested so the people who need it can actually use it.
WCAG 2.2 AAAODA
Common questions
If your question is not here, a senior engineer will answer it within one working day.
Ask our teamIt varies by task complexity. Simple binary classifiers can work with 500–1,000 labeled images per class. Object detection typically needs 1,000–5,000 annotated images. Complex segmentation may require 10,000+. We always start with a data audit and often use transfer learning from foundation models to dramatically reduce data requirements — sometimes 100–200 examples are enough with the right approach.
Real-time processing (under 100ms latency) is essential for live video surveillance, autonomous systems, and production line inspection. Batch processing is more cost-effective for document analysis, post-event video review, and large-scale image datasets. We match the architecture to your latency and throughput requirements — and design systems that can scale from batch to real-time as needs evolve.
Edge inference (on-device) wins when you need ultra-low latency, have bandwidth constraints, or handle sensitive data that can't leave the facility. Cloud inference is better for high-complexity models, variable workloads, or when edge hardware costs are prohibitive. Many production systems use both — edge for fast initial detection, cloud for detailed analysis of flagged events.
In most cases, we fine-tune pre-trained models (CLIP, SAM, YOLO) on your data — this gives better accuracy with far less data and training time than building from scratch. Custom architectures from scratch are only justified for highly specialized domains where no suitable foundation model exists. We always benchmark both approaches before committing.
We apply preprocessing pipelines specifically designed for your imaging conditions: adaptive histogram equalization for low light, deblurring filters, and noise reduction. During training, we augment data with simulated degradation so the model learns robustness. For critical applications, we also recommend hardware improvements (lighting, optics) alongside software solutions.
Let’s build something worth keeping.
Tell us what you are trying to build. A senior engineer will come back within one working day with a scope, a timeline and a fixed price.