A model vendor, an integrator, a developer. Three companies and two handoffs, and the thing never reaches production. We build the models, the compute, the agents and the software around them. There is nobody to hand off to.
No deck · No pitch · Just the honest read
The Gap
Almost everything expensive in a business is expensive for one reason. A person does it once, then somebody asks for it again. It is not hard work. It is identical work, which is exactly what nobody should be doing.
FRAGMENTED WORKFLOW
The way it usually goes
AUTONOMOUS APPLIED AI
Where we start
Per unit, per listing, per revision. That is the cost this replaces.
Where It Starts
Every business is different. Almost every first call is one of these four.
Area 01
We look at what it takes to turn a thing you operate into a thing that operates on a schedule, deployed as an endpoint so something else can call it, and so it costs nothing while nobody does.
Stays humanWhen it should run, what it must never touch, and who answers for it when it does.
Area 02
Right now you are the integration layer. We find where the copying happens, then decide honestly whether it wants an integration, an agent, or nothing at all. Not every gap is worth a system.
Stays humanWhich system is the truth when two of them disagree.
Area 03
We work out whether this is genuinely a build, and what it honestly costs to own software rather than rent it, with dashboard, login and mobile view included, because a model is not a system until somebody can use it.
Stays humanWhat the thing is actually for, and what it should refuse to do.
Area 04
We map how the work actually runs before we automate any of it, because you cannot hand a machine a process that only lives in one person’s head. Sometimes the map is the whole first project.
Stays humanThe decisions that were never written down, and why they were made.
Before we build anything, we find out where it hurts.
We map the top use cases in your department and your industry where work is operationally heavy, where hours are bleeding into rework, and where money is quietly leaking out of the process.
TOP USE CASES · BY DEPT · BY INDUSTRY
Clear plan for what to tackle first
What We Build
Shipped across twenty-eight industries: property, hospitality, fintech, healthcare, manufacturing, media, retail. Every one is a separate engagement. There is no bundle you have to take to start.
The software that runs the business, and the system that answers its phone. Equipment, inventory, onboarding, procurement, knowledge. Plus agents with their own numbers that pick up, qualify the caller, and fill the CRM from the recording instead of from somebody’s memory at six o’clock.
Autonomous teams with scoped tools, schedules and persistent memory. They do not wait to be messaged. They start the work, hand off between each other, and report when it is done.
Research, produce, publish and report on a schedule, across platforms, with nobody at the keyboard. Including the lead engines that find who to talk to before anybody has to look.
Multi-step generation pipelines for image, video, video-to-video and segmentation, built so each stage uses whatever model is best for it instead of whatever one vendor happens to sell. Custom nodes where the thing you need does not exist yet, and a simple interface over the top when the graph is too much for whoever has to run it.
A visualization product, not a batch of files. Image, 3D, CAD or video, whatever the output needs, built into software your team or your customers open and run themselves. The same engine works for property, furniture, ecommerce, fashion and beauty.
For when the software does not exist. Equipment, inventory, hospitality, onboarding, procurement, knowledge, built around the way you already work rather than the way a product assumes you do.
Internal tools · Operations · Monitoring · Dashboards
The Stack
A model on its own does not do anything. It needs machines to run on, something to start it, and software somebody can actually open.
Trained here
Sixteen model families across generation, editing and realism, plus custom LoRAs and cloned voices for a specific brand, product or person. Trained here, not called from somebody else’s API.
Scales to zero
Eight GPU classes, from a 24 GB card up to a B200 at 192 GB, deployed serverless, so a pipeline costs nothing at all while nobody is using it.
Self-starting
Autonomous systems with their own phone numbers, WhatsApp and voice. They call, message, trigger workflows and begin on a schedule rather than waiting to be asked.
The software too
The dashboard, the database, the login, the mobile view. A model is not a system until somebody can use it, so the software around it gets built here too.
Most AI work is split across a model vendor, an integrator and a developer, and it breaks in the gaps between them. Here there are no gaps, because there is nobody to hand off to.
Why Custom
Why use generic AI tools when your company isn’t generic?
Buying everyone a subscription is not adoption. It is procurement. The question was never which tool your team opens. It is which work should stop being done by hand.
Build your custom AI to refine how you create.
Common misconceptions
“We will start once the tools are more reliable.”
The 6% of organisations getting real profit from AI are not the ones with better tools. They redesigned the work around AI instead of putting AI on top of what they already had.
“AI is something you ask for help.”
That was the last generation. These are systems you give responsibility to, which is why what they can see and change matters more than what they can do.
“The more we automate, the better.”
Not every gap is worth a system. Sometimes the honest answer is an integration, and sometimes it is nothing at all.
“We will hand it the process and let it work the rest out.”
Before you give a system responsibility, you have to understand what you are giving it responsibility for. That is what the first stage is for.
“AI will tell us what to do next.”
It can work out how to get somewhere. It cannot decide where you are going. It does not know whether you are a luxury brand or a mass market one, whether growth is worth the margin, or which kind of attention is worth having.
“We do not know enough about AI to start.”
You do not need to. The work is taking something vague in your head and turning it into a problem a machine can actually work on.
“We just need to pick the right tools.”
The question is not which AI tools your team should use. It is how the work inside the company changes now that another kind of intelligence is available. Which work stays human? Which work becomes AI? What context does it need? Where does a person enter? Where do approvals happen? Who owns the final outcome? That is a far bigger change than adopting another piece of software.
Delusional Labs is the engine behind every system, agent and pipeline above. The other brands take the same machine into markets of their own: property, content and film. Every one of them runs on it.
Delusional Machines
One team behind every name.
Experiments across models, agents, interfaces and workflows, figuring out how enterprises and SMBs can adapt AI to the way their work already runs.
Coming soon
Taking AI beyond software with embedded systems, custom electronics and local or offline model infrastructure.
Commissioned R&D Prototypes only, no manufacturing
We help you go viral with organic content.
Generative film and visual effects, on pipelines built to survive a timeline and a client.
Applied AI for real estate operations: visualization, listing production, and automated lead qualification.
The Evidence
Surveyed across 1,719 executives in 97 countries. What separates them is not better models. It is that they changed how the work runs before putting AI into it.
Read the survey ↗ Anthropic · 2026 75%