The fastest-moving audience catches the smallest mistakes.
AI-infrastructure buyers live in the docs. A video that mislabels a parameter or hand-waves the architecture reads as marketing, and marketing is what this audience came to avoid.
Service · AI infrastructure
AI-infrastructure video that gets the details right.
The details your audience checks, gotten right the first time.
// what we do
Lumaris produces video for AI-infrastructure companies, model serving, vector databases, GPU orchestration, agent frameworks, with editors who write code.
The details a non-technical editor gets wrong are exactly the ones your audience checks, so every claim clears a line-by-line validation before delivery. From one video at €2,000 to the full function on a retainer.
// who this is for
AI infrastructure moves faster than any category we cut, and the audience is the most technical. The people evaluating an inference engine or a vector database can read a benchmark and a config file, and they discount marketing on contact.
We make video that respects that. The demo is real, the numbers are framed honestly, and the workflow on screen is one an ML or platform engineer recognizes.
// the case
The questions you're weighing.
What is AI-infrastructure video production?
It is video for the layer beneath the AI application: model serving and inference, vector and embedding databases, GPU scheduling and orchestration, retrieval pipelines, and agent frameworks. The audience is the engineer deciding what to build on, not the end user of an AI app.
The work ranges from a launch video for a new inference runtime to a tutorial on wiring up retrieval, to an explainer that makes a genuinely novel architecture legible without overselling it.
Why is honesty especially important in AI-infrastructure video?
Because the category is full of inflated claims, and this audience is primed to catch them. A latency number without its conditions, a demo that hides the failure case, a benchmark with no methodology: each one tells an engineer to stop trusting you.
We frame performance claims with their context, show the real workflow, and never imply a result the footage does not support. In a category where everyone is overselling, the company that does not is the one that gets shortlisted.
How does Lumaris handle a category that changes every month?
By validating against current reality at edit time. Editors who write code read the config, the API call, and the architecture on screen and check it, and the dev team confirms anything fast-moving rather than trusting a recording from six weeks ago. Each delivery carries a Dev Validation Receipt.
It also helps that this is a no-incumbent category. There is no established AI-infrastructure video specialist, which means the companies that invest in accurate video now own the category's visual language as it forms.
// dev-validation
Your engineers review nothing.
Editors who write production code cut the work, and every command on screen clears a line-by-line check before you ever see it. The accuracy is handled before the review loop starts, so the draft lands right the first time.
- Commands run against current docs
- A receipt names what was verified
- 1 to 2 revision rounds, not 4 to 6
# video: cli-quickstart-v3 · client: [redacted] # checked against docs @ 2026-06-09 ✓ cli flags // 14 commands run, all current ✓ api references // v3 endpoints, no deprecations ✓ architecture diagram // matches running product ! terminology // "cluster" → "node pool" (corrected) ✓ code blocks // compiled, 0 syntax errors
# one in-house technical-video hire build.hire = { salary_loaded: "EUR 60,000 - 120,000 / yr", output: "capped at one person", ramp_gaps: "hiring, onboarding, PTO, churn", }; # the Lumaris floor buy.lumaris = { retainer: "EUR 72,000 / yr", // EUR 6,000 / mo includes: "a team, dev validation, a PM, strategy", output: "a full video arm, ~5-day cadence", };
// build vs buy
What it costs.
The front door is one video from €2,000, a complete purchase you keep. The retainer, from €6,000 a month, is the upgrade once the work earns it, never the gate. We frame cost one way only: build versus buy, never a vendor price match.
€2,000 a video · yours to keep €6,000 a month · the upgrade
What every engagement includes
One scope, not a tier menu.
- LLM, inference, vector DB, and agent video
- Launch, demo, and explainer formats
- Honest framing of benchmarks and claims
- Validation against current docs and configs
- About a five-day first draft
- From €2,000 a video, retainer from €6,000/mo as the upgrade
Keep exploring
More from the studio.
Lumaris is a technical-video studio for developer tools, with the mechanism behind the work on the method page and the reasoning in what dev validation actually checks. The fastest way to judge it is a free re-cut on your own video.
FAQ
AI-infrastructure video, in short.
Why does AI infrastructure need a specialist video team?
Because the audience lives in the docs and checks the details a generalist editor gets wrong: a mislabeled parameter, a hand-waved architecture, a benchmark with no conditions. Editors who write code read the config and the API call on screen, so the video reads as engineering rather than marketing this audience came to avoid.
Can you explain technical architecture on screen?
Yes. You send the raw material and the goal; editors who write code read a novel architecture and make it legible without overselling it. The line-by-line validation keeps the explanation accurate as you refine it, which matters in a category that changes month to month.
How fast can you support a launch?
About five days to a first draft, because the speed is a documented system, not a scramble. We validate against current reality at edit time rather than trusting a recording from six weeks ago, so a fast-moving inference runtime or vector database ships accurate on the launch timeline.
See it on your own footage.
On your own footage, not a case study. Nothing filmed yet? Send a topic.