What teams ask
before we begin.

Recurring work with a clear trigger, a defined outcome and enough volume to matter is usually the best place to start. Typical examples include checks, case handling, record updates, request fulfilment and coordination across systems.

We also look closely at exceptions, risk and the quality of the available information. If the workflow cannot be bounded or the business case does not hold, we will say so.

No. Aitonomy is built for recurring business work across functions. The relevant unit is a workflow and its outcome, not a department or a particular kind of software team.

An agent might support operations, finance, customer service, compliance, people teams or any other area where work moves through repeatable steps and systems.

Possibly. Individual AI tools help people complete individual tasks. A dependable agent role needs more: a defined job, approved access, clear limits, human review, production supervision and evidence of quality and value.

Aitonomy provides that operating layer and the team to put it into production. We work with the tools and systems you already use rather than asking you to start again.

There is no fixed headcount threshold. What matters is that there is a real workflow with enough recurring work to justify a role, a named owner for the outcome and the ability to give the agent controlled access to the systems it needs.

We start with a scan of one workflow. Together with the people who run it today, we map the work from trigger to completion, count volume, handling time, rework and exceptions, and list the systems involved.

The output is a baseline, a proposed agent role and a go, adjust or stop decision grounded in the business case.

We prioritise work where the outcome is clear, the volume is meaningful and the risk can be contained. The first role should be valuable enough to matter and bounded enough to earn trust quickly.

We agree the success measures, human review points and stop conditions before the agent touches live work.

It means an Aitonomy engineer works directly with your process owner and the people doing the work. They turn exceptions and unwritten rules into tested agent behaviour, build the integrations and remain accountable through the move into production.

Your IT team approves security, credentials and architecture. Aitonomy carries out the implementation work.

Our target is supervised production in four weeks for a well-scoped first role. The scan normally covers weeks zero to two, onboarding weeks two to four, and trust-building continues as the role handles a limited live queue.

The exact timing depends on system access, workflow complexity and the number of exceptions. We make dependencies and decision points visible up front.

Enough to explain the workflow, approve access and controls, review exceptions and judge whether the output is right. Aitonomy carries the mapping, build, integration, testing and production transition.

Involvement is highest at the start and becomes lighter as the role stabilises and the review burden falls.

Aitonomy Control is the operating platform for your agent workforce. It shows every role, owner, permission, level of autonomy and production status, then connects that activity to quality, capacity and value.

It also gives people one place to review exceptions, approvals and the evidence behind each action and decision.

Agents connect to the tools, APIs and records your work already depends on. Each role receives only the approved connections and actions needed for its job, so you do not need to replace your existing technology.

Every role has a named human owner and an explicit level of autonomy for each capability. An agent can suggest, draft or complete work, depending on what has been approved.

Exceptions and decisions that need judgement are routed to people with the supporting evidence attached. A capability can be reduced or paused without stopping the whole role.

No. Aitonomy Control is designed to use the approved model that best fits each role and capability. The platform is not tied to a single model provider, and the role remains defined independently of the model underneath it.

Accepted human corrections become specific proposed improvements. Safe changes can follow an approved route; material changes can require Aitonomy or human review.

Every applied change is versioned, visible and reversible. Agents do not silently rewrite their own operating rules.

We agree a baseline before production and measure each role against it. Aitonomy Control tracks quality, work completed, human review, time freed, operating cost and return on cost.

Measured results are kept separate from derived estimates, so you can see what has actually happened and what is being projected.

We scope the first workflow and delivery work before we begin, then agree the ongoing platform and agent fees for the roles that move into production. The shape depends on workflow complexity, integrations, controls and expected volume.

We would rather price the defined work and business case than publish a number that hides those differences.

We compare the cost of the agent role with the work it completes and the human capacity it frees. A role only earns room to grow when its quality and economics hold against the agreed baseline.

Aitonomy Control keeps model, platform and agent costs visible alongside the resulting capacity and value.

You can, and some organisations will. The question is whether you want to assemble the agent engineering, integrations, production controls, supervision and measurement capability before the first role can prove value.

Aitonomy brings that operating capability as one team and platform, while your people retain ownership of the workflow, approvals and accountable decisions.

Agents receive a defined role, bounded access and explicit approval points. They pause when a case falls outside the agreed rules, when required information is missing or when a decision needs human judgement.

People can intervene, lower autonomy or stop a capability, and every action and approval remains visible.

Deployment, access, retention and data boundaries are agreed for each role. Aitonomy is EU-sovereign by design, with operation and data residency aligned to European requirements.

Each agent is limited to the data and actions its role has been approved to use. We address the exact security and architecture requirements during the scan and onboarding.

Governance is part of the operating model: named ownership, explicit access, defined autonomy, human review and a traceable history of actions and decisions.

We track the EU AI Act as it phases in through 2026 and beyond. Your own obligations still depend on the use case, so risk and accountability are assessed role by role.

Yes, provided the workflow, controls and evidence meet the organisation’s requirements. Regulated work makes explainability, named accountability, bounded access and human review especially important.

We design those requirements into the role and its production process rather than adding them after the agent has been built.

Aitonomy supervises the role in production, reviews failed and delayed cases and improves the rules, examples, system actions and approval thresholds behind them.

Volume, case types and autonomy expand only when the agreed quality, capacity and value measures support it.

We keep the role under measurement. Quality, exceptions, reversals, review rates and cost are visible in Aitonomy Control, and changes are versioned.

If performance drops, the relevant capability can be pulled back, corrected and retested before it earns more autonomy again.

Yes. We start with one role so the controls and business case are proven on real work. From there, proven roles can take on more volume and new case types, and new roles can be added across other workflows and teams.

The goal is a dependable agent workforce that grows by evidence, not a collection of disconnected pilots.