What teams ask
before we begin.

Look for work with a clear starting point, a defined outcome and enough volume to create value. Examples include checking documents, handling cases, updating records, fulfilling requests and coordinating work across systems.

We also assess exceptions, risk and the quality of the available information. If we cannot set clear limits for the work or establish a sound business case, we will say so.

No. We build agents for work across your organisation, including operations, finance, customer service, compliance and HR. We start with the work and the outcome you need, then build around the teams and systems involved.

The right starting point depends on where an agent can create value for your business.

It depends on what you want AI to do. Agents that take on work across your systems need clear responsibilities, approved access, defined limits, human review and ongoing supervision.

We provide the platform and team to build and run those agents, track their quality and value, and connect them to the tools and systems you already use.

There is no minimum team size. You need enough work to justify the investment, someone responsible for the outcome and approved access to the systems the agent will use. We assess those conditions with you before we build.

That depends on how your organisation uses the capacity agents free up. Our aim is to help your team take on more work and give people more room to use their expertise. Before we build, we agree with your leadership how the freed time will be used.

Yes. They know how the work runs and where exceptions arise. They help define the agent’s work and review it from the first live case.

Wherever you are with AI today. If you are not sure where to start, a Scan works out per process where AI pays off. If the process is already chosen, we map it with the people doing the work: volume, handling time, rework, exceptions and the systems involved.

You get a baseline, a proposed agent and a business case to decide whether to proceed, adjust the scope or stop.

We weigh expected value, risk and time to go live. The first agent needs a clear outcome, enough work to justify the investment and a scope we can test and supervise.

Before go-live, we agree how success will be measured, where people review the work and when the agent must stop.

An Aitonomy engineer works directly with your process owner and team. They build the agent around your working practices, including exceptions and unwritten rules, connect it to your systems and remain responsible through go-live.

Your IT team approves security, credentials and architecture. We carry out the implementation.

Our target is four weeks for a first agent with a clearly agreed scope. Weeks 0 to 2 cover the scan and business case. Weeks 2 to 4 cover building and testing. The agent then starts with a limited number of real cases under human review.

Timing depends on system access, process complexity and exceptions. We identify dependencies and decision points with you up front.

Your team explains the work, approves access and controls, reviews exceptions and checks results. We handle process mapping, building, integration, testing and go-live.

Involvement is highest at the start and reduces as the agent performs reliably and needs less review.

Weeks 0 to 2: we measure the work with your team and agree the business case. Weeks 2 to 4: we build the agent, connect it to your systems and test it on past cases. From week 4, it handles real cases under your team’s review. You track results against the baseline in Intelligence.

After the first measurement, before we build; before go-live, after testing on past cases; and at each monthly review. We continue when the agent meets the agreed targets, adjust when the work or requirements change, and stop when the business case no longer holds. Performance is visible in Intelligence throughout.

It connects your systems and company knowledge (Context), runs processes with agents (Agents), shows what to improve (Intelligence) and keeps every step within your rules (Security and governance). You see what each agent does, who is responsible and what it can access.

It gives your team one place to review exceptions, approve decisions and see the evidence behind agent actions.

Our engineers connect agents to your existing systems and information, using approved interfaces such as APIs. Each agent receives only the access and permissions it needs for its work. You keep the technology your teams already use.

Each agent has a named owner in your organisation. You approve how independently it can perform each task: suggest, draft or complete work.

Exceptions and decisions that need judgement go to the right person, with supporting evidence. You can reduce autonomy or pause a specific capability without stopping the agent’s other work.

No. The platform supports multiple model providers. We select an approved model suited to the work each agent performs. The agent’s work is defined independently of the model it uses.

Accepted corrections from people become proposed improvements. Safe changes can follow an approved process. More significant changes may need review by Aitonomy or a designated person.

Every applied change has a recorded version, is visible and can be reversed. Agents do not silently change their own operating rules.

We agree a baseline before production and track each agent against it. Intelligence shows quality, work completed, human review, hours freed, operating costs and return on cost.

Measured results stay separate from estimates, so you can distinguish what has happened from what is projected.

We agree the scope of the first process and delivery work before we begin, then agree ongoing platform and agent fees for the agents that go live. Pricing depends on process complexity, integrations, controls and expected volume.

Your price reflects the agreed scope and business case, including the integrations and controls required.

We compare each agent’s cost with the work it completes and the capacity it frees. Savings depend on how you use that capacity. We expand when quality and value meet the agreed targets.

Intelligence shows model, platform and agent costs alongside the time saved and the value delivered.

You can. Building in-house means bringing together agent engineering, system integrations, controls, supervision and performance measurement. The choice depends on the capabilities you have and want to build within your organisation.

We provide the platform and the team to build your agents and support them. Your people stay responsible for the process, approvals and decisions that need their judgement, and over time they take the lead in running it.

Each agent has a defined scope, approved access and clear approval points. It pauses when a case falls outside the agreed rules, required information is missing or a decision needs human judgement.

People can intervene, reduce autonomy or stop a capability. Actions and approvals remain visible.

You decide where agents run, which data they can access and how long it is kept. We use European infrastructure and EU-based providers, with data boundaries agreed for each agent.

Each agent is limited to approved data and actions. During the scan and onboarding, we agree the security and architecture requirements for your organisation.

Each agent has a named owner, approved access, defined autonomy and human review. Its actions and decisions are recorded so they can be traced and reviewed.

We track EU AI Act requirements as they take effect. Your obligations depend on the use case, so risks and responsibilities are assessed for each agent.

Yes, provided the process, controls and evidence meet your organisation’s requirements. Regulated work makes explainable actions, clear responsibility, limited access and human review especially important.

We design these requirements into the agent and how it runs from the start.

You do. The agents we build for you, the data they work with and their configuration in the platform belong to your organisation.

Your team gradually takes the lead and makes changes through the platform. We keep monitoring, investigate failed or delayed cases and support your team, including on working instructions, examples, system actions and approval thresholds.

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

We monitor performance continuously. Intelligence shows quality, exceptions, reversed actions, review rates and costs. Changes are recorded by version.

If performance drops, we can reduce autonomy for the affected capability, fix the issue and retest before expanding it again.

Yes. We start with one agent to test the controls and business case on real work. As results prove reliable, agents can handle more volume and new case types. We can also build new agents for other processes and teams.

You add agents and processes as results justify it, with the same discipline around quality, oversight and value.