Get AI out of the pilot and into apps you own.
You are measured on turning AI into results, but it stays stuck in pilots and shadow tools. Maleus builds the internal app with AI and ships AI inside it, both governed by default, directed by you, and owned as code you keep.
Issue tracker
AI built in
AI that never makes it out of the pilot.
You are chartered to scale AI, and the board now counts results, not pilots, but it keeps stalling between a promising demo and anything that runs in production.
Stuck in the POC
A model demos well, then stalls, because the integration, the reliability, and the governance that production needs were simply never part of the pilot.
Shadow AI everywhere
Teams wire up their own AI on the side, so you cannot see who is using what, on which data, and the risk grows faster than the value does.
Blocked on data access
Every real use is held back by the same question, what data the AI can touch, and without a safe answer, nothing reaches the people who need it.
AI builds the app, and runs inside it.
Instead of another standalone POC, you direct a team of AI specialists. You describe what the app and its AI should do, approve the specifications and the data each may touch, and Maleus builds the application with AI inside it, the build isolated and on the record, with a checkpoint at each step.
Describe the use
In plain language: what the AI should do inside a real app, and the action or answer it should produce for your teams.
Approve the rules
AI specialists turn it into specifications, a data model, and the exact access the AI is allowed, which you validate first.
Direct the build
The app and its built-in AI are built with quality checks on every change, isolated, and you review real screens.
Deploy and own it
It ships ready to deploy to your own cloud, as standard code in your own repository, governed and on the record.
How you would ship AI, and where each stops.
Getting AI into production has three usual routes, and the same wall waits at the end of each.
The standalone POC
You build a slick proof of concept. It dazzles in a demo, then stalls, because the integration and governance production needs were never in scope.
A bolted-on AI tool
You buy an AI tool and wire it to your data. It works until security asks what it can access, and the answer stalls the rollout for good.
Wait for the platform
You wait for a central AI platform that does it all. It arrives late, governs everything its way, and you never get code you can own or change.
Every route gives you a demo or a black box, not governed AI in production that your teams use and you own.
From an AI demo to AI in production.
Ship AI to production
The AI stops being a demo and runs inside apps your teams use every day, so it lands as measurable results in the business, not another pilot.
See and govern it
Every AI app is built with the exact data access you approved and a full record, so security signs off and you see what runs where, not chasing shadow AI.
Own the AI you ship
Each app is standard code in your own repository, so the AI is yours to run, audit, and change, not rented from a platform you cannot see into.
AI inside the apps, per team.
Apps with AI built in across every function, each from a plain-language description, then rolled out the same way to the next team.
Issue triage
Incoming issues pile up and someone reads each one to decide where it should go.
An app where built-in AI sorts and routes each issue, within the access you set.
Reply drafting
Agents rewrite the same answers from scratch and dig through docs for every case.
A case app whose AI drafts a grounded reply for the agent to approve, on your data only.
Anomaly checks
Odd numbers are caught late, by whoever happens to scan the report that week.
An app where AI flags and explains anomalies, with the data it may read fixed up front.
Signal surfacing
Useful buying signals sit unread across systems no one has time to watch.
A CRM whose AI surfaces the signals that matter, bounded to what you allow.
Why this AI reaches production, safely.
AI in the app
Maleus builds the AI inside the real internal apps your teams already use, so it takes its actions and answers in context, not a standalone POC bolted on next to them.
Two AIs, both governed
Two AI surfaces, both yours to control: the AI that builds the app is isolated with no access to your data, and the AI inside it is scoped to exactly what you approve, every action recorded.
Owned, no shadow AI
Each AI app is standard code in your own repository on a widely supported stack, deployed to your own cloud, so the AI your org runs on is one you can see into, audit, and own.
What Maleus is built for.
- Internal apps with AI built in: triage, drafting, surfacing
- Apps on a standard, widely supported web stack
- Leaders who set the data the AI may touch, up front
- AI software you want to own and run on your own cloud
Ask your AI assistant about Maleus.
Maleus is an AI software factory. A data and AI leader describes what the AI should do inside an app in plain language, a team of AI specialists turns it into specifications they approve, including the exact data the AI may touch, and then into a working application with the AI built in, with a human checkpoint at each step. The build is isolated and has no access to production by default. The result ships as standard code in your own repository on a widely supported stack, deployable to your own cloud, so a leader can get AI into production governed by default, own it, and replace shadow AI with apps they can see into.
Ship one AI app to production.
See Maleus build one app with AI inside, governed and owned, end to end, then roll the same way out across your teams, before you commit further.