A private AI system on your own infrastructure, connected to your documents, jobs, and history. Built by Moonello, an AI systems firm that runs its own AI in production every day.
In most companies, nobody knows: employees paste customer lists, contracts, and pricing into public AI tools every day, and that information leaves your control the moment they hit enter.
Your people are not waiting for permission. When we run discovery at Moonello, unsanctioned AI use is something we find in nearly every company we walk into. Employees pasting customer lists, quotes, and contracts into public tools because it helps them move faster. The productivity is real. So is the exposure.
Even used safely, public tools answer from the internet, not from your quotes, your job history, or your procedures. Our clients come to us when fluent guesses stop being good enough, when the answer has to be grounded in what the business actually knows. That grounding is an architecture problem, and it's the one we solve.
We've watched both plays. Companies that block AI push it onto personal phones; companies that ignore it accept invisible risk. The durable answer we've landed on with our clients is simpler: give your team something better than the thing you took away.
What many teams call a private GPT or a company GPT is what we at Moonello build as an internal AI system: a private assistant that runs on your own infrastructure and knows your documents, your jobs, and your history. Your team gets the ChatGPT experience they already want, grounded in the business and governed by you.
Ask it what your warranty policy says, what you quoted a customer last spring, or how the last three jobs like this one went.
The difference between this and a public tool is the difference between asking a stranger and asking your best employee.
We connect the system to your sources, file shares, ERP, project history, so answers come from what the business actually knows
A clean chat window, on their desktop and phone, that speaks their work: our clients' teams use it to look up procedures mid-job, draft customer responses from real account history, and get new hires answering questions in weeks instead of quarters.
Adoption is rarely the problem.
Answers are the floor. The systems we build at Moonello also act: watching a queue and routing what arrives, assembling the weekly report from three systems, flagging the order that doesn't match the quote.
Every agent runs with defined boundaries and human checkpoints. This is production automation, not a demo.
Nothing gets replaced. The system plugs into what exists.
Your ERP, your drives, your email, the databases your business runs on through governed integrations.
That's usually the moment our clients realize this isn't another tool to manage; it's a layer that makes the tools they have answerable.
Yes. When the system runs on your infrastructure, under your access rules, with nothing leaving to train anyone else's model, and that is exactly how we build them.
The system deploys inside your environment (ie: your AWS/Azure/Google Cloud account), your tenancy, your region. We build it; you own it. When the engagement ends, nothing about your AI depends on Moonello holding your data, because we never do.
Documents are retrieved at the moment of a question and discarded after the answer. Nothing persists into model weights, nothing is reused outside your control. It is a guarantee we architect in, not a checkbox we hope a vendor honors.
The assistant respects the same permissions your business already enforces. Payroll questions answer for the people cleared to ask them. In our builds, access rules live at the retrieval layer, so there is no prompt clever enough to see around them.
Every question, every source consulted, every action an agent takes — logged and reviewable. When we hand the system over, your leadership can answer the question boards are starting to ask: who is using AI here, for what, and on what data.
Our builds typically deploy on AWS Bedrock, including GovCloud when compliance demands it, which keeps model traffic inside infrastructure that never trains on your inputs.
At Moonello, we run every internal AI build through the same four phases, on a schedule we publish up front. The phases exist so that at no point are you funding faith. Each one ends with something real you can evaluate.
We map your sources, your permissions, and the questions your team actually needs answered. Not an abstract requirements doc: we leave discovery with your data inventoried, your access rules understood, and the pilot scoped to the use case with the most obvious payoff.
Your documents, in a working assistant, answering real questions from a small group of your people. This is the phase where our clients stop evaluating the idea and start evaluating the results, which is exactly why we put it before any large commitment.
The pilot hardens into a system: full integrations, role based access enforced, monitoring in place, rolled out to the teams who need it. We train your people in the open, not in a handoff PDF.
Launch is a transition, not an ending. We tune retrieval as your data changes, review the audit logs with you, and expand into the next use case when the first one has proven itself. Many of our clients carry this forward as an ongoing arrangement.
Phases 1 and 2 are the Pilot engagement. All four, end-to-end, are the Foundation.
Our internal AI engagements start at $15K for a working pilot on your own data, and $75K for the full 90-day path to production. Both are priced flat; both are published here because you shouldn't need a sales call to learn a number.
Discovery and a working assistant grounded in your documents, used by a small group of your team, deployed in your environment. You end the pilot with real answers to real questions, a security architecture reviewed by your people, and a clear read on what production would look like. If it ends there, you keep everything we built.
Everything in the pilot, then full integrations with your systems, role-based access, monitoring, governance, and rollout across the teams that need it. This is the engagement where, at Moonello, we do our best work: the one that ends with your company running its own AI, owned outright.
Your pilot investment applies in full toward the Foundation. Most clients start with the pilot. It's the version of this decision that doesn't require a committee.
A Michigan industrial services company with two decades of quotes, job records, procedures, and no way for anyone to ask them a question. Institutional knowledge lived in a handful of veterans' heads.
Employees had started feeding client documents into public AI tools to move faster. Leadership was weeks away from banning AI outright.
We built a private assistant in their own AWS environment, grounded in their project history and SOPs, with access rules matching their existing permissions. Nothing leaves their infrastructure. Nothing training anyone's model.
Their team asks questions in plain language and gets answers grounded in twenty years of their own work. New hires ramp up against the company's real history instead of tribal knowledge. And leadership went from planning a ban to planning the second use case.