Contact us
Contact us today to learn more about how our automation partnership service might assist you in achieving your technology goals.
Free consultation call
When you build backend systems across dozens of startups, patterns start to emerge.
At TLVTech, speed matters—but so does stability. Startups need to ship fast, iterate without breaking things, and scale without rebuilding their entire backend from scratch.
We’ve refined a tech stack that consistently delivers all three. It’s not about hype—it’s about choosing tools that get out of the way and let teams focus on product.
Here’s the stack we use, why it works, and where we adjust based on use case.
We default to Node.js with TypeScript for most backend services.
Why it works:
We’ll use Python for ML/data pipelines or Go for high-performance cases—but Node/TS is our go-to for API-centric products.
NestJS gives us the best of both worlds: fast setup + enterprise-level structure.
Why it works:
For simpler services, we may go with Express. But NestJS hits the sweet spot for most production backends.
Postgres is rock solid. It's our default unless the use case says otherwise.
Why it works:
We may bring in Redis for caching, MongoDB for unstructured data, or DynamoDB for specific scaling needs—but Postgres carries most of the load.
We build everything container-first.
Why it works:
We adapt based on team size, traffic needs, and deployment maturity—but the principles stay the same.
Simple, integrated, and customizable.
Why it works:
We keep pipelines fast and predictable. Every commit should be shippable. No manual deploys, no broken main branches.
You can’t fix what you can’t see.
Why it works:
We build dashboards that give teams visibility from day one. No waiting for a fire to realize you need alerts.
Startups don’t have time to experiment with unproven tools. This stack lets us move fast, stay clean, and grow without surprises.
If you're building something and want backend speed without technical debt, let’s talk.
AI can elevate your product—if it’s integrated the right way. This post shares practical tips for making AI features feel intuitive, helpful, and natural—not awkward or overengineered.
- SaaS architecture is compared to a high-rise building, handling scalability, user management, and security with a structure of user interface, server, and database. - Each SaaS service has unique features but shares a core structure. Additional sub-layers might be present depending on the service's complexity. - Multi-tenancy allows SaaS to efficiently serve multiple users from one app, providing cost and resource benefits. - Various platforms such as AWS, Azure, Salesforce, and Oracle offer distinct approaches to multi-tenant systems. - Understand SaaS architecture in real life through examples like Dropbox and Salesforce. Business apps like Slack and Trello exhibit SaaS applications in business. - There are SaaS architectural patterns and principles, like AWS multi-tenant SaaS, that can be used in designing SaaS architecture. - Resources, case studies, and literature to navigate architectural complexities are readily available online.
- AI plays a crucial role in computer vision by processing images and recognizing their contents. - It's trained with extensive data to help it recognize various elements in new images. - Real-world applications include spotting defects in production lines, healthcare scans analysis, security enhancements, and more. - Different industries utilize AI vision, like healthcare for disease detection, retail for inventory management, and agriculture for crop monitoring. - Models such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) are utilized in AI vision processing. - Future trends include more accurate image tracking, dark object detection, and faster, detailed understanding of images due to tech advancements like higher resolution and improved processing speeds. - AI's impact on computer vision will improve efficiency, potentially enabling automatic shopping through visual identification.
