Reasoner
Ask, and Oria reasons over governed data and institutional memory. Dashboards, maps, simulators, relationship graphs, timelines and work boards come out of the conversation, not out of a ticket.
← ARPIA
Meet Oria.
The super agent that knows how your organization works.
Oria runs on ARPIA, the platform underneath, so it reads the ERP, CRM and data platforms you already run through a governed reflection of them. And it knows your organization because every account arrives with Cerebro, the institutional memory that holds the decisions, rules and procedures your people already wrote down. Your team asks, analyses, builds and acts from the first session, with budgets and an audit trail. You can start this week: no platform project, no business case, no nine months of procurement.
Start today
30 minutes to size it. Your team works the same week.
Days to 13 minutesCollections, trigger to ERP, in production
ISO 42001Certified AI management system
SOC 2 Type IIFull security audit
Since 2020Governing AI in production, on 22 years of enterprise software
WHY THIS EXISTS
Adopting AI should not need a nine month business case.
The usual path to governed enterprise AI runs through vendor onboarding, security review, legal, a justified use case and a pilot. Eight to twelve weeks before anyone touches anything, and often the better part of a year before the first result.
Meanwhile your people are already using AI. Just not yours, and not governed.
Oria inverts the order. Seats first, governed from day one, and the use case emerges from what your people actually do with it.
Seats are billed per day. Add someone on a Tuesday and you pay from Tuesday. Adoption spreads because it is allowed to, not because a committee approved a rollout.
WHAT YOUR PEOPLE GET
Four surfaces, one governed session.
Oria is not a chat window bolted onto your data. It reasons, builds, authors and executes, and every one of those runs inside the same governed session, on the ontology and permissions the business already defined. Each one has a demo, no slideware.
Coder
Generation constrained by your ontology and your security policies, with a review gate before anything ships. The standards are the guardrails, not a code review afterwards.
Workbench
Author the ontology itself: nodes, relations and the tables behind them, with permission gates on propose, apply and sample. This is where the DNA of the business gets written down.
Oria Machine
A governed execution environment. Code runs in its own pod with your ontology scope and permissions, human approval where it matters, and its own persistent storage.
WHAT MAKES IT USEFUL
Most assistants hand you a document. This one hands you something that runs.
The spreadsheet somebody maintains by hand is where most of the real operating knowledge of a company lives. Oria takes it, understands it next to your governed data, and gives back a working tool rather than a summary of it.
01
Drop the file in
Excel or CSV into the session. It lands in the object store, shows up as a file in the exploration, and the agent reads it alongside the nodes you already gave it.
02
Ask for the thing you actually need
Not a summary of the sheet. A dashboard, a map, a simulator, a work board, or a small application over that data, built in the session and pinned there.
03
Wire in the tools you use
Connect your own accounts through MCP with your own sign in. The agent uses them as tools in the same session, under the permissions your organization already set.
04
Leave it running
The work executes in its own governed environment with approval where it matters, keeps its own persistent storage, and can be promoted from your workspace to the organization's.
That is the difference between an assistant that drafts and one that builds. The spreadsheet stops being a file somebody emails around, and becomes a tool with governed data behind it, an audit trail, and a memory of why it works the way it does.
THE DIFFERENCE
Every other assistant reads. This one writes.
Most companies already have an enterprise chat assistant. It answers well, and then the work starts: you take the answer somewhere else to analyse it, and somewhere else again to build anything with it. It can only reach what somebody already wrote down, and the decision taken in a meeting or the rule that surfaced while debugging is not in there, because nobody ever typed it. That is also the knowledge that walks out when a senior person resigns.
The enterprise AI chat you already pay for
- Answers and drafts, then you copy the result into another tool to do anything with it
- Reads what somebody already wrote down
- Memory is per user, opaque, with no attribution
- No governed view of the operational data, so no analysis you can act on
- Nothing it produces is attached to the entities the business runs on
Oria, on ARPIA
- Records what the organization decided, as a byproduct of the work
- Analyses governed data and returns dashboards, maps, simulators and boards
- Builds applications against your ontology, reviewed before they ship
- Every institutional claim carries the id of the memory behind it, and the corpus stays readable from outside through MCP
- Full lineage: which model, on what context, under which rules, approved by whom
The point is the whole cycle in one governed place: ask, analyse, build, decide, act, and the record accumulates while you do it. The part that is hard to copy is not the memory, it is that the memory lives beside the ontology. When a memory says how churn is calculated, the node that calculates it is one step away, with its lineage and its governed actions. A memory layer on its own is a two quarter build for anyone. That is why this sits on a data platform and not on a search index.
It scales down as well as up. A team of twenty gets the same governed cycle as a company of three thousand, at the price of the seats they actually use.
ARPIA is certified under ISO 42001 and SOC 2 Type II, and Oria inherits that infrastructure. Your own compliance stays yours to run, but the governance layer is not something you have to build first.
WHAT COMPOUNDS
Cerebro comes with the account, from Basic.
Not an add-on to configure later. Every account is provisioned with its institutional memory ready and embedded in Oria, switched on in every session, for the Reasoner and for the Workbench.
- Answers lean on the decisions, rules and procedures the organization already recorded
- Immutable rules are injected whole into every turn and govern the answer, which is refused if it would break them
- The user decides which domains the AI may read in that session, and the limit is enforced on the server
- Three ways in: the agent when asked, one click, or automatic capture to a personal domain
- Writing to a shared domain always needs a human to confirm it
Six months in, you have not just been using AI. You have a written record of how your organization reasons.
And it is not held hostage. The same corpus is readable from the AI tools your people already use, through MCP. The pitch is not stay because your memory is here. It is work wherever you want, this is where it accumulates.
It starts empty and gets better with use, so we seed it during onboarding with the documents, tickets and wikis you already have. Oria begins knowing something, and from there the work keeps it alive.
WHERE IT LANDS FIRST
The teams that feel it in the first week.
Every one of these ends in something governed: a pipeline that ran, a board that is live, a decision with its lineage attached. Not a document somebody has to carry to the next tool.
Finance
Which accounts to chase, and the collection already moving.
Oria maps the exposure, diagnoses what changed, prescribes the action and activates it into the ERP, with human approval where the money moves.
Out: a governed pipeline that ran. Collections went from days to 13 minutes, trigger to ERP, in production at a financial services group.Operations
What the agents are doing right now, on one board.
Running objects, errors, what is waiting on a person. Operations stops asking whether a flow ran and starts seeing it.
Out: a live operations board. Metrics and object states update as the work happens.Commercial
A price or a forecast that arrives with its reasoning.
Competition, inventory and sales history combined into a recommendation you can interrogate, because the reasoning that produced it is attached to it.
Out: a recommendation with its lineage. Analysis that took weeks resolves in minutes.Risk and compliance
Every AI decision, reconstructable.
Which model ran, on what context, under which policy, approved by whom. Budgets are enforced before the spend, not discovered on the invoice.
Out: an audit trail you can hand over. Built for scoring and decisioning that has to answer to a regulator.Data
What breaks if this column changes.
Lineage that covers the tables and the reasoning objects on top of them: pipelines, nodes and the apps that read them, in one graph.
Out: lineage across data and reasoning. Most tools trace tables and stop there.Technology
What the business asked for, built inside your standards.
Generation constrained by the ontology and the security policies, with a review gate before anything ships, and code that runs in its own governed pod.
Out: reviewed work, not a prototype. The guardrails are the standards, not a code review afterwards.PLANS
Start with seats. Grow when the work asks for it.
Three levels, billed per seat per day, so a seat added mid month costs what it used. The AI itself is paid separately and you set the ceiling.
Oria Basic
$5/user/month
For putting governed AI in everyone's hands.
- Multi-model AI with governance
- Model assigned by role and profile
- Budget controls and shared AI Wallet
- Cerebro included and preconfigured, on in every session
- Internet search and artifact generation
- Security and audit trail
- Runs any app already built on ARPIA
Consumption tier. Does not build.
Oria Pro
$50/user/month
For the people who want to build their own tools.
- Everything in Basic
- A more capable Reasoner, running on Oria Machine
- Build a personal application on your own ontology access
- Persistent repository storage for that work
Building is scoped to the personal workspace.
ARPIA Builder
$100/user/month
For whoever owns the ontology and the governance.
- Everything in Pro
- Data management
- Ontology authoring and governance
- AI governance configuration
- Workshop project creation
Institutional building, not just personal.
Why the seat is cheap
Because you are not buying intelligence by the seat. AI consumption runs on a prepaid AI Wallet, a shared pool you size and control, billed on what was actually used. That is what keeps a governed seat at five dollars instead of sixty.
Or bring your own models
Connect your own provider accounts or keys and the tokens stay on your bill. ARPIA charges a flat governance fee of $1 per million tokens, whatever the provider or model tier, for the budgeting, assignment, audit and policy that traffic passes through.
WHERE IT GOES
The platform conversation, when you have earned it.
01
Seats
Your people work with governed AI. Spend is capped, every session is auditable.
02
Memory
Findings accumulate with attribution. The organization starts recognising its own knowledge.
03
Tools
Power users build personal applications. The useful ones get noticed by the people beside them.
04
Ontology
What the business calls its entities gets written down and governed, by the people who know.
→
Platform
Now a use case is obvious, scoped and defensible, because it came out of the work instead of a slide. That is when our teams build with you.
Start today.
Tell us how many people and what they work on. Thirty minutes is enough to size the seats and the wallet, and your team can be working the same week.
Size your seats.
Tell us who starts and what they work on. Thirty minutes on a call is enough to confirm it, and your team can be working the same week.