Timeline to MVP: 30-Day Sprint with Vision Microservices
Yes, you can ship a working computer vision MVP in 30 days — without hiring a team of PhDs or building AI from scratch. This week-by-week guide breaks down exactly how to do it using vision microservices like OCR, background removal, object detection and more. Learn how modern dev teams scope tightly, integrate smartly and launch confidently using modular APIs that deliver real image intelligence out of the box. Whether you're validating an idea or racing to demo day, this sprint plan shows you how to move fast and build smart.
When Off-The-Shelf Fails: Signs You Need Custom Models
Off-the-shelf vision APIs are great — until they aren't. When accuracy plateaus, domain drift creeps in, or edge cases pile up, even the best plug-and-play model can become a bottleneck. In this post, we unpack the red flags that signal it's time to go custom and share a phased roadmap to help you transition smoothly — without blowing deadlines or budgets. Whether you're struggling with OCR misreads, misclassified logos, or brittle workarounds, learn how bespoke models can future-proof your computer vision stack.
Cloud vs Edge: Finding the Sweet Spot for Vision
Choosing between cloud, edge or hybrid for computer vision isn’t just about technology — it’s about finding the right balance between speed, cost and control. In this post, we break down the classic Latency–CapEx–Data Gravity triangle, walk through real-world break-even points and offer a practical roadmap from PoC to scalable deployment. Whether you’re tagging products, anonymizing faces, or automating inspections, this guide helps you make smarter architecture decisions — and hit the vision sweet spot in 2025 and beyond.
Talent vs Toolkit: Building an In-House Vision Team or Renting Expertise
Building a computer vision-powered product? You don’t need to choose between hiring expensive AI experts or doing it all in-house. In this guide, we explore the real tradeoffs between recruiting top CV talent, upskilling your existing developers and renting expertise through APIs and AutoML platforms. From salary benchmarks to hidden infrastructure costs, we break down the total cost of ownership (TCO) and help you map the best strategy based on your product’s stage and priorities.