Give the model an identity
The AI authenticates, holds a permission set, and is subject to the same access rules as a person. It cannot read what it is not entitled to read.
AI that does real work, and can be audited afterwards.
Most AI in business software is a chatbot bolted to the side: impressive in a demo, untrusted in production, because nobody can say what it can see or prove what it did. We build it the other way round — the model gets an identity and a permission set like any staff member, works on the same records, and leaves the same audit trail.
Is this you?
You probably need this if…
- Staff spend hours reading documents to pull out a few fields
- The answer to a question exists in your data, but nobody can find it
- You've trialled an AI tool and couldn't satisfy yourself it was safe
- Drafting the same kind of document is a daily task
- Compliance asked what the AI could access and nobody knew
What we do
How we approach it.
Put it where the work is
Inside the screens your team already uses, acting on live records — not a separate chat window they have to remember to open.
Log everything it does
Every action is recorded with the record touched and the authority used, so an auditor gets a real answer rather than a shrug.
Keep the provider swappable
We build against an abstraction, so you can move between hosted models or run an open model on your own infrastructure without a rewrite.
Scope
What this covers.
- Assistants inside your own systems
- Document understanding & extraction
- Search across company data
- Drafting & summarisation
- Permissioned agent actions
- Full audit trails
Why take our word for it
Mardio ships an AI assistant that works within each user's permissions and logs every action it takes against the live database.
What you get
Concretely, what lands in your hands.
- The AI capability, live inside your system
- A written statement of what it can and cannot access
- Full audit logging of model actions
- Evaluation results against your own examples
- Cost-per-use projections
- A documented path to swap providers
How to buy
How this is usually engaged.
1–2 weeks · fixed fee
Discovery sprint
We sit with the people who do the work and write down how your business actually runs — the entities, the approvals, the exceptions — then scope and estimate the build against it.
Defined outcome · staged payments
Fixed-scope project
A written scope, milestones you pay against, and working software in your hands early. Ninety days of defect warranty from go-live, measured against the scope we agreed.
Monthly · senior engineers
Dedicated team
Senior engineers working your backlog to your priorities, in your ceremonies. Useful when the destination is still moving, or when you need to add capacity to a team you already have.
FAQ
Questions we hear about this.
Will our data be used to train someone's model?
Not with the providers we use under their business terms, and not at all if you choose a self-hosted open model. Which of those applies is a decision we make with you at the start, not an assumption.
What if the model gets something wrong?
Anything consequential is proposed rather than executed — a draft to approve, not an action already taken. Where the model does act directly, it acts inside permissions that bound the blast radius, and every action is reversible and logged.
Can this run entirely on our own infrastructure?
Yes. Open models have become good enough for most document and drafting work, and we are deliberately provider-agnostic so that running on your own hardware is a configuration choice rather than a rebuild.
Talk to us about ai & intelligent systems.
Describe what you run today and what isn't working. We'll come back with an honest view of what we would build, and what we wouldn't.