AI video

What the AI video service includes

We take the client's task and carry it from idea to a finished clip: we formulate and refine the prompt, run the video generation through a neural network, then edit and post-process the result. As input we accept a description, references, brand materials and the script text; as output we deliver an edited clip of the required length and format. We work in iterations: the first run gives draft frames, then we refine the framing, motion, lighting and scene transitions until the picture matches the task. Separately, we tune the repeatability of the style so that a series of clips looks like a single piece rather than a set of different generations. We don't run physical filming: all the work happens in a software environment on our team's side.

How the technology really works

A video-generation neural network is trained on large sets of "text-frame" and "frame-frame" pairs and predicts how a sequence of images should look from a given description. Modern models are built on diffusion: first from random noise, then step by step this noise is removed until a meaningful frame emerges, consistent with its neighbors over time. The prompt sets the content and style, while service parameters keep a single object and motion across frames so there's no jitter or detail swapping. The model's raw output almost always needs finishing: scene cuts, color grading, stabilization, replacing weak fragments and fitting to the sound. So the real result comes from two parts in roughly equal measure: the generation itself and the engineering post-processing on top of it.

The history of generative video

The starting point of the field is 2014, when Ian Goodfellow and co-authors published the paper "Generative Adversarial Nets" and proposed adversarial GAN networks, where a generator and a discriminator learn against each other. The second foundation was laid in 2015: Sohl-Dickstein and co-authors, in the paper "Deep Unsupervised Learning using Nonequilibrium Thermodynamics", described the diffusion approach — turning data into noise so as to later restore data from noise. The boom began in 2022: on 22 August Stability AI together with the CompVis group released Stable Diffusion for images, and in November 2022 Google showed Imagen Video, already for video. The pace then accelerated: in June 2023 Runway opened Gen-2 with text-to-video generation, and on 15 February 2024 OpenAI unveiled Sora, whose public release (Sora Turbo) took place on 9 December 2024. In ten years the field went from research GANs to commercial models available to business.

Why precision and setup matter

The quality of a clip is determined not by the fact of generation but by how precisely the prompt, the model parameters and the post-processing logic are set. An imprecise wording produces a drifting object, flicker and frames that can't be stitched into a coherent video, and then the generation has to be redone from scratch. Tuning the control parameters — the degree of adherence to the text, the length and frame rate, the reference images — directly affects the stability and predictability of the result. Post-processing covers what the model doesn't deliver on its own: color, sharpness, artifacts, editing rhythm and synchronization with sound. Without this software and engineering part, you're left with a set of raw fragments rather than material fit for publication.

What tools we work with

The stack splits into three layers: video-generation neural networks, editing tools and post-processing tools. At the generation level we use diffusion video models of the Runway, Stable Video Diffusion and compatible families, choosing the model for the task — dynamics, style, scene length. We handle editing, cuts, timing and sound work in video editors, and build batch operations and processing automation on FFmpeg, which transcodes formats, cuts and assembles tracks. We do color, artifact cleanup, upscaling and stabilization as separate nodes on top of the model's output. We pick the specific set for the budget, format and platform rather than forcing a single universal tool.

When the key tools appeared

The base working tool for video processing — FFmpeg — was released on 20 December 2000 by the French programmer Fabrice Bellard, and it has since become the standard for transcoding and assembling video. The modern generation layer relies on diffusion, formalized in 2015 in Sohl-Dickstein's work, and on its applied implementation Stable Diffusion from 22 August 2022. Video models appeared at scale right after: Imagen Video in November 2022, Runway Gen-2 in June 2023, OpenAI Sora in February 2024. So the mature processing tool has existed for over twenty years, while the generative layer on top of it came together literally over the past few years. We combine both layers: time-tested processing and fresh generation models.

Why you can trust this to us

ASI Robotics is a development team with combined experience in IT of more than 45 years, and we approach video as an engineering task rather than a one-off creative. We write the prompts and tune the pipeline for a specific result, fix the parameters and check the outputs before the production launch, rather than handing over the first run that comes out. The same engineering principle underlies everything we do: for cutting, welding, 3D printing or warehouse logistics tasks we prepare the program and the setup, while the cutting, welding, printing and hauling is done by the client's equipment. This division is honest: the production part is performed by the client's hardware, and on us are the software, the logic and the finishing to stable quality. So the risk is limited to the development and verification stage, and an already debugged solution goes into the production loop.