Questions companies ask before working with us.

Aurelith is a Greek applied-AI company that helps operating companies turn AI into working systems. It works across three connected levels: AI consulting and team training, custom AI agents and workflow automations connected to the tools you already use, and Company Brain. Each level builds on the one before, so you can start where your business is today.

Aurelith (AURELITH Ι.Κ.Ε.) is a Greek company. It works with operating companies in Greece first and then across Europe, in Greek and English, and every engagement starts with a free first conversation.

With a free first conversation about your goals and challenges. Aurelith then analyses your business, including your processes, your tools and how your people work, and sets out a written proposal with the service level, scope, deliverables, timeline and investment. You agree the proposal before anything is built. Book a free first conversation.

Both. At Level 01 Aurelith sets your AI direction and trains your teams. At Level 02 it designs and builds custom AI agents, workflow automations and software connected to your email, documents, CRM and ERP, with people approving the decisions that matter. After launch, support has no end date.

Company Brain is Aurelith’s governed organisational operating system. It turns your company’s work, decisions and outcomes into one evidence-backed memory, with AI agents that earn autonomy under human authority. It is developed and available now through Founding Design Partnerships, which start with one valuable workflow, a controlled set of authorised sources and three to five agreed success measures.

There is no public price list. The first conversation is free, and the investment is set out in a written proposal once Aurelith understands your business. See how pricing works.

You can start at any level. Level 01 suits companies that first want a clear AI direction and confident teams. Level 02 suits companies with a workflow that is ready to automate, and Level 03, Company Brain, suits companies ready to connect knowledge, systems and decisions across the business. Aurelith recommends the right starting level after the free first conversation and an analysis of your business.

Level 01, AI Consulting & Enablement, covers leadership alignment, role-based team training and a prioritised AI roadmap. Level 02, Custom AI Solutions, covers custom AI agents, workflow automations and software connected to the tools you already use, with people approving the decisions that matter. Level 03, Company Brain, connects your company’s knowledge, systems and AI agents into one governed intelligence. The exact scope and deliverables are set out in your proposal.

Yes. Aurelith designs and builds AI automations that read, compare and prepare work across your email, documents, CRM and ERP, then stop for a person to approve every decision that matters before anything irreversible happens. This is approval-first automation, part of Aurelith’s AI automation service at Level 02.

Yes. Aurelith builds custom AI agents that take on defined tasks inside your tools, such as reading documents, comparing options and drafting summaries, while your people keep authority over the decisions that matter. Each agent is shaped around your business and connected to the systems you already use, as part of Aurelith’s AI agents service at Level 02.

Aurelith has no public price list. The first conversation is free; scope, deliverables, timeline and investment are set out in a written proposal after Aurelith understands your business. The investment depends on what the work involves, such as the number of systems to connect, the volume of exceptions to handle, how approvals are designed, the interfaces your people need and the quality of your data.

The timeline depends on the level and the scope, and it is set out in your proposal before any work begins. Aurelith recommends starting with one valuable workflow, putting it to work in real conditions and expanding from there, so your team sees it working before the scope grows.

Aurelith stays by your side. After testing in real conditions, refining and guiding your team through everyday use, Aurelith keeps supporting your people with no end date, so they keep getting value from what was built together.

No. Aurelith connects AI to the systems you already rely on, such as email, documents, CRM and ERP, so work flows between them instead of starting over. Where a process needs its own interface, Aurelith can build a custom application or internal platform around it.

An AI consultant helps a company decide where AI should and should not be used, and in what order. At Aurelith that means aligning the leadership team, analysing your processes, tools and the way your people work, showing where AI pays off and where it does not, and turning the result into a prioritised AI roadmap with a training plan by role.

An AI roadmap is a prioritised plan that sets out which AI use cases a business will pursue, in what order, and what each one needs in data, tools, people and approvals before it is built. A good roadmap gives every use case a business case and ends with a first step that can start now.

No. You can start at any of Aurelith’s three levels, and the first conversation decides where. If you already know which workflow should improve, you can go straight to a Level 02 build. AI consulting is the right start when your leadership wants a shared view of where AI fits before it invests.

Aurelith starts from the business, not the technology. It looks at the work your people do every week, how much time and attention it takes, how much it varies, how ready the data and tools are, and who would own the result. Work that recurs, follows a recognisable pattern and has a clear owner usually comes first; rare work, or work whose data is not ready, usually waits.

Yes. Implementation is Level 02, Custom AI Solutions: Aurelith builds AI automations, custom AI agents and software connected to the tools you already use, with people approving the decisions that matter. As with every Aurelith service, the scope, timeline and investment are agreed in a proposal before any build begins.

Aurelith has no public price list. The first conversation is free, and the investment is set out in a written proposal after an analysis of your business. For AI consulting it depends mainly on how many teams, processes and tools are in scope and whether a training plan by role is included. More on scope and pricing.

No. Aurelith works with the owners and leadership teams of operating companies, and the scope is set to fit the business. A smaller company may focus on one area of the business; a larger one may bring several departments into scope. The proposal sets this out before any work begins.

AI literacy is the skills, knowledge and understanding that let people use AI systems in an informed way and be aware of their opportunities, risks and possible harm, which is close to how Article 3(56) of the EU AI Act defines it. At work it means knowing what the AI tools you use can and cannot do, handling data carefully, checking what comes back and knowing when to hand a case to a person.

Yes. Aurelith designs each programme around the AI tools your people use or plan to use, the rules you have or still need, and examples from their own work, so what they learn applies directly to their daily tasks.

Everyone who uses AI at work or decides how it is used, each at the depth their role needs. Leaders need to judge where AI creates value and how it is governed, managers need to set rules and review output, specialists and everyday users need safe daily habits, and technical teams need to connect AI to systems responsibly. Aurelith maps the roles in your company before it designs the programme.

Both belong to Level 01, AI Consulting & Enablement. AI consulting decides where AI pays off and in what order; AI training gives people the skills to work with what the roadmap brings. Aurelith can deliver them together or on their own, and when both are in scope, the roadmap helps decide which roles to train first.

Yes. Aurelith works in Greek and in English, and the language of each programme is agreed in your proposal.

No. After delivery, Aurelith helps your people apply what they learned in their own work and stays by their side with support that has no end date, so their skills keep pace as your AI tools and rules change.

Aurelith has no public price list. The first conversation is free, and the investment is set out in a written proposal after an analysis of your business. For training it depends mainly on how many roles and teams the programme covers and how closely it is built around your own tools and rules. More on scope and pricing.

The duty in Article 4 is mandatory; a specific training is not. The text names no course, format or level, and the Commission’s Q&A says no strict requirements or mandatory trainings are imposed. It also warns that relying only on instructions for use may be ineffective, so role-based training and guidance remain the most direct way to take the measures Article 4 asks for.

Yes. Article 4 applies to providers and deployers of AI systems with no size threshold. The 2026 amendment dropped the “sufficient level” wording partly because of the burden on smaller enterprises (recital 8 of Regulation (EU) 2026/1744), and Article 4(2) now asks the Commission and the Member States to support providers and deployers, in particular SMEs.

Generally yes, for that use: the AI Act defines a deployer as anyone using an AI system under its authority, outside personal, non-professional activity. The Commission’s Q&A says a company whose employees use ChatGPT to write advertisement text or translate needs to comply with Article 4, and that staff should be informed about risks such as hallucination. Whether a specific use is in scope depends on the facts.

No. Article 4 does not mention a certificate, and the Commission’s Q&A states there is no need for one: organisations can keep an internal record of trainings and other guiding initiatives. Treat any offer presented as an official AI Act literacy certificate with caution.

Start from the AI your people actually use: what those tools can and cannot do, which information must stay out of them, how to check their output, the company’s rules, and when a person must decide. Then add the risks of your specific systems and the legal and ethical aspects, at a depth that fits each role.

Article 4 sets no interval. A sensible practice is to refresh when your AI systems, their use or people’s roles change, to brief new staff and contractors when they start, and to set a fixed review date, for example once a year, because the tools change quickly.

Yes. Aurelith provides role-based AI training for leaders and teams in operating companies in Greece and Europe, built on the company’s own tools and rules, in Greek or English. It is a practical way to take the measures Article 4 asks for, not a certification. Every programme starts with a free first conversation.

AI automation uses AI models for the steps of a business process that fixed rules cannot handle, such as reading documents and emails, comparing options and drafting replies, while conventional automation runs the predictable steps. Aurelith builds AI automations approval-first: the AI prepares and moves the work, and a person approves every decision that matters before anything irreversible happens.

Yes, where rules are enough. Aurelith uses rules for the predictable steps and AI where the work needs reading or judgement, and keeps a person on every decision that matters. The full comparison of RPA, workflow automation, AI automation and AI agents is in the guide AI automation vs RPA.

Good candidates recur often, follow a recognisable pattern, involve reading or preparing documents, emails or records, and have a clear owner who can approve the outcome. Typical starting points are incoming documents and forms, supplier and quote comparison, email triage with drafted replies, CRM updates and follow-ups, reports and summaries, and internal requests and approvals. The guide What to automate first gives a seven-question checklist.

No. An AI automation works alongside your CRM, ERP, email and document storage: it reads from them, prepares the work and writes back only what a person has approved. Your systems remain the record, and the automation moves the work between them.

Through approval-first design. Each automation has only the permissions its job needs, a person approves every decision that matters before anything irreversible happens, your team can see what was prepared and who approved it, and unclear cases go to the right person. The scope grows only after the automation has proved reliable in real conditions.

Aurelith has no public price list. The first conversation is free, and the investment is set out in a written proposal after an analysis of your business. For an automation it depends mainly on the number of systems to connect, the volume of exceptions, how the approvals are designed and whether your people need their own interface. More on scope and pricing.

An automation’s timeline is part of your proposal and is agreed before any work begins. Aurelith recommends starting with one valuable workflow, testing it in real conditions with your team and widening the scope once it has proved itself, so the first automation is working before the next one is planned. More on scope and timing.

Rarely in one step. Most processes mix predictable steps for conventional automation, reading and drafting for AI, and decisions that stay with people. Start with one workflow end to end, with approval steps, and expand as it proves reliable.

AI automation follows a designed sequence in which AI handles specific steps, such as reading, comparing and drafting. An AI agent chooses its own next step and tool within set limits. Automation suits recurring workflows whose path is known; an agent suits work whose path differs from case to case.

Not company-wide. The first workflow needs only the documents and fields it depends on to be accessible and reasonably consistent. Testing on real cases shows where data quality causes errors, so you fix what matters first.

It can be, when it is designed for it: access limited to what the job needs, personal data handled on a lawful basis, a log of what was read and done, and approval before anything irreversible. In an Aurelith project, data handling is part of the scope agreed in the written proposal, before implementation.

Against the baseline taken before launch: handling time per case, the share of outputs approved without edits, rework, escalations and turnaround. Review the numbers with the workflow owner on a fixed rhythm, and widen the scope only when they hold.

Aurelith starts with a free first conversation, then analyses the business: its processes, its tools and how people work. The first workflow, its approval steps, deliverables, timeline and investment are set out in a written proposal agreed before implementation. See AI automation by Aurelith.

A custom AI agent is software built around one responsibility in your business. It uses an AI model to choose the next step, works only with the context and tools you approve, and hands the decision to a person when judgement is needed. Custom means it is designed around your workflow, your rules and your systems, not configured once for everyone.

Aurelith recommends a chatbot when people mainly need answers, guidance or drafts inside a conversation, and an agent when the work has to move forward across steps and systems: reading a request, gathering the facts, preparing or taking a permitted action and escalating whatever it cannot settle. It makes that call once it understands the workflow, and the two often work together. The guide AI agents for business compares chatbots, traditional automation and AI agents.

Yes, where your systems allow secure access. Aurelith connects each agent to the tools your people already use, such as email, documents, CRM and ERP, with only the read and write permissions its responsibility needs. Anything the agent writes back follows the approval points you agreed, and your systems remain the record.

You do. Before the agent handles real work, Aurelith agrees its responsibility, permissions and approval points with you, and your team can see what it read, prepared and did. A new agent typically starts by observing, recommending or drafting for approval, and it receives more authority one action at a time, only when evidence shows it is reliable and you decide to grant it.

With a free first conversation about one valuable workflow. Aurelith then analyses how the work runs today and sets out the scope, deliverables, timeline and investment in a written proposal. Once you agree it, the agent is built with your team and tested on representative real cases, including the ones that must escalate, before its role grows.

Aurelith tests the agent in real conditions with your team, refines it and guides the people who work alongside it, then stays by your side with support that has no end date. Its authority grows only one action at a time, when evidence shows it is reliable and you decide to grant it.

Aurelith has no public price list. The first conversation is free, and the investment is set out in your written proposal once the work is understood. For an agent it depends mainly on the number of systems it connects to, how varied and exception-heavy the work is, how the approvals are designed, whether your people need their own interface and the quality of the data it relies on. More on scope and pricing.

A chatbot mainly generates responses inside a conversation. An AI agent can also decide which step to take next, retrieve approved context, use connected tools, perform permitted actions and escalate to a person when human judgement is required.

Traditional automation follows predefined rules and paths. An AI agent is useful when a workflow includes variable language, unstructured information, exceptions or context-sensitive decisions that cannot be represented efficiently as fixed rules.

No. A focused single agent with clear tools and boundaries is often easier to evaluate, secure and maintain. Multiple agents are worth introducing only when responsibilities or toolsets genuinely need to be separated.

Start with one valuable and bounded workflow: define the desired outcome and today’s baseline, map the context and actions it needs, and test a focused pilot against representative real cases before wider integration.

No. A knowledge base stores documents that people write and maintain. A company brain links documents to the activity, decisions and outcomes around them, keeps each item tied to its source and access rules, and lets AI agents use that context only within approved authority.

In Aurelith’s model it holds three: the Reality Twin shows how work actually runs, the Design Twin tests a better way of working, and the Agentic Twin holds what is allowed to execute. All three are versioned views over one Company Memory.

There is no single standard. Aurelith uses five rungs, from R0 Observe (read-only) through R1 Recommend, R2 Draft and R3 Approval-bound execution to R4 Bounded autonomy (verified continuously). Published research uses other scales, such as five levels defined by the user’s role.

Yes. In a governed company brain, authority is held per workflow, action, tool, context and model, and it is withdrawn when the evidence weakens. The agent then works at a lower rung until it proves itself again.

No. It starts with a controlled set of authorised sources for one workflow, connected read-only. More sources are added only for an agreed purpose, and each keeps its access rules.

The Aurelith team writes them. Each guide names its author, links to the author’s profile on the team page, and shows when it was first published and when it was last updated.

Every statistic or legal statement links to the primary source it comes from, such as EUR-Lex, the European Commission or NIST, and each guide lists its sources with the date they were last reviewed. Guides that touch on law carry the note “General information, not legal advice.”

Yes. Each guide ends with the Aurelith services that put it to work. Every engagement starts with a free first conversation, followed by an analysis of your business and a written proposal that is agreed before any implementation.

Yes. Every guide is published in English and in Greek, with the same structure, the same sources and the same update dates.