Basics
The ideas
behind the work.
Short, plain-language explainers on the concepts that come up when we configure, connect and build AI systems.
Vision
AI setup
Architecture
Edge vs. cloud deploymentWhere a model should run: next to the camera or sensor, or in a data centre.1 minMulti-tenant architectureServing many customers from one system without letting their data meet.1 minClient vs. server: where code runsWhat belongs in the browser or app, and what must stay on a server you control.1 min
From idea to implementation
Have a concept
you need applied?
Tell us which workflow it is for, and we will tell you whether it is the right tool for the job.
---
title: "Basics: Plain-Language AI and Software Explainers"
description: "Short explainers on computer vision, embeddings, fine-tuning, deployment and architecture — the ideas behind the work."
url: https://lidvizion.ai/resources/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---
Basics
# The ideas behind the work.
Short, plain-language explainers on the concepts that come up when we configure, connect and build AI systems.
[Read insights](https://lidvizion.ai/blogs/) · [See the work](https://lidvizion.ai/work/)
## Vision
- [What is computer vision?](https://lidvizion.ai/resources/basics/what-is-computer-vision/): Software that turns images and video into structured information a system can act on.
- [Image vs. video processing](https://lidvizion.ai/resources/basics/image-vs-video-processing/): When a single frame is enough, and when you need motion, sequence and time.
## AI setup
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Fine-tuning vs. pre-trained models](https://lidvizion.ai/resources/basics/model-fine-tuning-vs-pre-trained-models/): Why most teams should start with a capable pre-trained model, and when fine-tuning earns its cost.
## Architecture
- [Edge vs. cloud deployment](https://lidvizion.ai/resources/basics/edge-vs-cloud-deployment/): Where a model should run: next to the camera or sensor, or in a data centre.
- [Multi-tenant architecture](https://lidvizion.ai/resources/basics/multi-tenant-architecture/): Serving many customers from one system without letting their data meet.
- [Client vs. server: where code runs](https://lidvizion.ai/resources/basics/backend-client-vs-server/): What belongs in the browser or app, and what must stay on a server you control.
From idea to implementation
## Have a concept you need applied?
Tell us which workflow it is for, and we will tell you whether it is the right tool for the job.
[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)