Private AI Portals · AI Automation · Consulting · California, USA
A private AI portal is a web page where your own people ask for work in plain language, and an AI operator, a custom AI agent that already knows your process, does it using only the systems you approved. It is AI workflow automation with nothing to install, no ticket to file and no waiting for a specialist to free up. Remote-first, with clients in every time zone.
The idea
Think of it as a new team member who arrives already trained on your process, can only touch the systems you hand over, and works from any browser. In industry terms it is a custom AI agent for workflow automation, running privately for your company. The people using it need nothing more than a login.
Scientists, planners, operators, managers. Any laptop, any browser.
A login, a conversation, your files. Each person works in their own sealed workspace.
Trained on your process before day one. Knows your steps and your words. You ask for the outcome; it does the steps.
Your cloud, your software, your machines, your documents. Only what you connected.
Your process, built in
Connected safely
Nothing to install
Why it matters
Most of the wait in a technical process is not the work. It is the queue between the person who needs the answer and the person who knows how to get it. A portal takes the queue out and leaves the expertise in.
This is not about replacing people. It takes the know-how that lives in one busy specialist's head and writes it into the portal, where it works for everyone, every time. The specialists who used to run the hundredth repeat of a job set the operator up once and get their time back for the work only they can do. Teams that could never hire a data scientist or a release engineer have one from the first login.
The judgment stays where it belongs. Any decision that must stay with a person is written into the portal as a confirmation, a review or a hard stop, and the operator respects it every time.
Sound familiar?
You do not need to know how a portal is built to know whether you need one. If any of these sound familiar, you do.
Every analysis waits for the one person who can run it.
Your scientists ask for the analysis in plain language and have results the same afternoon.
Quoting a part costs an engineer an afternoon of preparation.
Upload the part; the portal positions it, checks it and estimates the build while you watch.
A one-line change waits two weeks for a developer to pick it up.
Describe the change; the portal makes it, checks it and puts it on the test site for your OK.
The Monday report is hand-built from three systems, every Monday.
Ask for the numbers; the portal pulls them, checks them and writes the report.
Anything with AI in it is on hold until someone can say where the data goes.
The portal runs on your own hardware with a local AI model. Nothing leaves the building, and every action is logged.
You are the only one who knows how the system really works, so everything routes through you.
Write it down once, with us, and the portal runs it for the whole team.
The portals
Each one is a private, custom AI assistant for one kind of work, and is already running for a real team. Every portal comes with your process written in, the connections to your systems, the guardrails, and a view for whoever manages it.
Research · Biotech
Analyses without writing code. A scientist describes what they want in the language of the science; the operator runs it on rented cloud computers and brings the results back into the same window. The lab gets the data specialist it could not hire.
Connected to Notebooks · Cloud computing · Results archive
Product teams · Agencies
Software changes from a chat window. Describe the change; the operator makes it in a copy of your software, records it, and publishes it to a test site or the live one through a single locked door. Going live always asks a person first.
Connected to Your code · Test site · Live site
Labs · AI and data teams
Teach and update your AI models on your own machine. Ask for a training run, compare the results, and put the best model into the product, all from the browser. The hardware stays yours; the work stops depending on one person's laptop.
Connected to Your GPU machine · Your data · Model library
Any team · Regulated data
Your name on it, your colors, and an AI model running on hardware you control, so nothing leaves the building. The fastest portal to stand up and the easiest one to show a compliance officer.
Connected to Local AI model · Your documents · Your network
Metal 3D printing In development
Upload a part; get it ready to print. Built on our own metal 3D printing software, the operator positions the part, adds supports, cuts it into layers, checks the machine's rules and estimates the build time, then hands the shop a file the printer can read.
Connected to Our slicer · Printer files · Build reports
Custom
If a job has a clear owner, a repeatable set of steps and a system it touches, it can be a portal. We write down how it works, connect it, try it with your real users, and run it for as long as it earns its keep.
Connected to Whatever the job touches
Your call
Two decisions stay yours from the first day, and neither one is permanent.
Where it runs
The portal is the same wherever it runs. Start where your team is most comfortable, and move it when that changes.
Software shops and consultancies can also deploy portals for their own clients under license.
AI-agnostic by design
A portal is not tied to one AI company. Your process, the connections and the guardrails stay exactly the same; the model behind the operator is a setting. Use the one your company already trusts, and change it when something better or cheaper comes along.
Case studies
Four portals running today, in biotech, product development and on a local AI model, and the one we are building next for metal 3D printing.
Run the colony-count analysis on yesterday's plate 3 and compare it with last week's run.
Started on the cloud as job 214: 96 wells, about 12 minutes. I'll bring the counts back here and save the comparison next to last week's results.
Done. Counts for 96 wells, 3 flagged for review. Comparison saved as plate-3-vs-last-week.
Biotech · Lab Analysis Portal
A biotech's lead scientist needed simulation results that used to wait on a developer's calendar. Their portal opens the lab's analysis notebooks in the browser; the operator sends the runs to cloud computers, watches them, brings the results back and saves the work, answering in the language of the science. The scientist never sees a line of code.
Change the 'Submit' button on the intake form to 'Send request' and put it on the test site.
Done. Changed the button label, ran the checks (all passing) and published to the test site. Preview: test.yourcompany.com/intake
Product team · Release Portal
A design studio evaluating a project-management platform wanted changes to land without a developer in the loop for each one. Their portal holds a working copy of the software; the operator makes the change, records it, and publishes it to the test site or the live one through a single locked door that allows only those actions. Going live asks a person first. Nothing else on the server can be reached.
Retrain the cell classifier with the 40 newly annotated wells and tell me if it beats the current model.
Training on your GPU machine now, about 25 minutes. I'll compare per-class accuracy with the current model and only suggest a swap if it wins in every class.
Done. New model 94.1% vs 92.6% overall, better in every class. Ready to install when you say so.
Biotech · ML Ops Portal
The same biotech runs its cell-imaging software on its own AI machine. Each person has one standing conversation and the operator's memory is shared across the team; it trains and checks the models, installs updates on the machine, and remembers what it learned about the hardware. The team asks for outcomes; the operator handles the rest.
Summarize the three supplier contracts in the shared folder and list any auto-renewal clauses.
Read all three here on this machine. Two renew automatically 60 days before the end of term; the third needs written notice. The summary and a clause table are saved in the folder.
Local model. No internet connection used, nothing left this machine.
Any team · Private Assistant
A portal under its own name, colors and logo, answering from an AI model that runs on a machine in the room. No cloud, no data leaving the building, and a shared demo login that shows the whole idea in five minutes. Built in a day; the easiest portal to stand up and the first one to show a compliance officer.
Prep bracket_v7 for the 4-laser machine. Best orientation for the fewest supports, and give me the build time.
Oriented at 35° about X: support volume down 62%, nothing overhanging below 40°. Sliced at 60 µm with your standard stripe pattern; all four lasers stay inside their lanes. Build time 9 h 40 min.
Machine file ready to download. Layer preview and build report attached.
Metal 3D printing · Build-Prep Portal In development
Built on the metal 3D printing software we develop (laser powder bed fusion, or LPBF): the shop drops a part into the portal, and the operator positions it, adds supports, cuts it into layers using the shop's own settings, checks that every laser stays in its lane, estimates the build time and writes the file the printer reads. Questions get answered in the shop's language: layers, overhangs and hours, not code.
How an engagement runs
A portal is only as good as the playbook inside it. We write that with the people who do the job, then keep improving it after it goes live.
Sit with the people who do the job. Map the real steps, the vocabulary, the systems it touches, and what must stay a human decision.
Write the operator's playbook: the buttons named for the work, the checks it must pass, the actions it may take and the ones it may not.
Connect it to your cloud, your software or your machines through narrow, approved doors. Try it with real users on real work.
In our cloud, in yours, or on your own hardware. We keep it updated and supported; you watch usage and activity in the manager's view and keep improving the playbook.
The engineering underneath
A portal only works when the systems behind it do. We build those too: cloud and on-premises infrastructure, the data pipelines, the AI models, physics simulation, and the screens people use every day. That is why the connections are safe and the operator has something real to run. You never need to touch any of this to use a portal, and your technical team is always welcome to look under the hood with us.
About RAD Intelligence
RAD Intelligence was founded on a simple observation: the teams pulling ahead are not the ones with the most AI slideware. They are the ones whose everyday work quietly got faster because the people who own a process can now run it themselves, safely. That takes engineers who can build the system, not just describe it.
We are an AI consulting and software engineering company, led by a Principal Software Engineer & Architect with deep experience in Python, cloud infrastructure, simulation, and machine learning, we operate as a senior extension of your team — able to design the portal, build the systems behind it, and run it. Small by design, direct by nature, and accountable for outcomes.
We're remote-first and work with clients in every time zone — syncing when you're online, shipping while you sleep. True to our Southern California roots, we're serious about the work and easy to work with.
P.S. — What does R.A.D. stand for? Contact us and we'll let you in on it. 🤙
Questions people ask
The short version of what a portal is, where your data goes, which AI runs it and how to start.
It is a private web page where your own people ask for work in plain language, and an AI operator that already knows your process carries it out on the systems you connected. In industry terms it is a custom AI agent for workflow automation, set up for one job and one team, with nothing to install.
A chatbot answers questions. A portal does the work. It already knows your steps and vocabulary, it is connected to your real systems, it can only take the actions you approved, and everything it does is logged. Your people ask for an outcome and get the result, not advice on how to do it themselves.
You decide. A portal can run in our cloud, inside your own AWS, Azure or Google Cloud account, or on a machine in your building. With a local AI model, nothing leaves the building at all. Each connection to your systems is a narrow door that allows only the actions you listed.
The portal is AI-agnostic. It can run on Claude, on OpenAI's models, or on a local open model such as Qwen or Llama on your own hardware. The model is a setting, so you can change it later without rebuilding the portal.
No. In the managed option we host, update and support the portal, and your team only needs a browser and a login. If you prefer on-premises AI, we supply and look after a preloaded machine on your network.
No. It removes the queue, not the expertise. The know-how of your specialists is written into the portal so routine requests no longer wait on them, and any decision that must stay with a person is built in as a confirmation or a hard stop.
No. Most of our portals serve small and mid-sized teams: a lab, a product team, a manufacturing shop. Small teams often gain the most, because they cannot hire a specialist for every job that needs one.
Start with a pilot: one real workflow, a fixed scope, your own people using it. Demos are available on request. Use the contact form and tell us about the job your team keeps waiting on.
Let's talk
An analysis that waits on a developer, a change that waits on a specialist, a report that waits on the one person who knows the system: those are portals waiting to happen. Bring one, and we'll tell you what it would take. Wherever you are: we work remotely, across every time zone.
Demos are available on request. Mention it in your message, and we'll walk you through a live portal on a short call and talk through how it would fit your process.