Service · AI solutions

AI solutions for companies: assistants, AI agents and process automation

Ignition Labs designs and deploys AI solutions for companies – assistants over company data, AI agents that work with tools and systems, and automations with large language models (LLMs) in everyday processes.

We work with small and medium-sized companies in Slovakia that want to use AI practically – in a specific process, not as yet another chatbot on a website.

We take on repetitive tasks such as sorting and processing enquiries, preparing documents, answering from internal documentation, checking data and connecting AI to existing systems.

We use our own AI agents daily in our work, for every deployment we define what the AI may do on its own and what goes to a human for approval, and we deliver the first working version in 21 days for the agreed scope.

Discuss my project Frequently asked questions

What we prepare for companies

An AI solution from Ignition Labs starts with a specific process that costs your people time today, and ends with a tool that is connected to your systems and has clearly defined boundaries.

An AI assistant over company documents and data that answers with a reference to the source
An AI agent connected to tools – email, calendar, CRM, internal APIs, Telegram
Automated processing of enquiries, orders or documents using LLMs
Voice and text interfaces to internal systems
AI features in an existing app (suggestions, summarisation, classification)
AI-assisted development – faster delivery of ordinary apps with the help of AI

An AI agent is not a chatbot

A chatbot answers questions. An AI agent takes a task, picks tools, performs steps in systems and hands over a result – for example it processes an enquiry, creates a CRM record and prepares a reply for approval. That is exactly why it needs limits, control and traceability, not just a good prompt.

At Ignition Labs we work with agentic systems that receive tasks, select tools, route work between agents and execute defined workflows through voice and text interfaces. We design the same approach for clients.

An AI agent is not a chatbot: how it works and where it can fail

How we deploy AI safely

For every deployment we define what the agent may do on its own and what goes to a human for approval. The steps the AI performs are logged and can be reviewed afterwards.

Before we start, we name which data the model needs, with which provider it is processed and what stays exclusively in your systems. Running costs of AI services are billed according to the providers' prices and depend on usage – you get an estimate when we agree on the scope.

AI-assisted development: faster delivery of ordinary apps

We also use AI when building conventional applications – as a tool that speeds up writing code, tests and documentation. Solution design, technology decisions and accountability for the result remain with people.

Faster development does not mean less control: every part the AI prepares is reviewed the same way as hand-written code, and the same rules apply before deployment as on any other project.

Building apps with AI: when it makes sense and what to watch out for

What we actually use

The internal technology stack of Ignition Labs consists of our own AI agents, orchestration and task routing, a voice interface, a Telegram integration and connections to tools and APIs. We use it for our own work and automation – and it is where we verify what we recommend to clients.

AI Agent Infrastructure – how we have it built

How the collaboration works

Four steps from the first conversation to delivery of the first working version – the same for every service.

STEP 1
We clarify the goal
We go through the problem, the users and the outcome the first version should deliver.
STEP 2
We agree on scope and price
We define what will be part of the first version, what stays for later, and how delivery will work.
STEP 3
We build and show progress along the way
We go through the development together so that unclear points and feedback can be caught early.
STEP 4
We test and hand it over
We verify the agreed user scenarios and prepare the handover according to the agreed scope.

We deliver the first working version in 21 days. The timeline applies to the scope of the first version agreed in writing beforehand; larger projects are split into stages.

Price and timeline

We have no flat price list. The price follows the scope we define in step two – the number of user scenarios, roles, integrations and operational requirements. You receive the exact price, timeline and handover terms in a proposal before work begins.

What makes up the price of a first version

Frequently asked questions

What is the difference between a chatbot and an AI agent?

A chatbot answers questions in a conversation. An AI agent performs tasks: it uses tools, reads from and writes to systems and chains steps in a workflow. An agent therefore needs clearly defined permissions, limits and a record of what it did.

How much does an AI solution for a company cost?

The price follows the agreed scope – the process we automate, the systems that need to be connected and the control requirements. On top of that there are running costs of AI services according to the providers' prices and usage. Both are estimated in the proposal you receive before work begins.

What data do I have to share and where is it processed?

During design we define which data the model really needs, with which provider it is processed and what stays exclusively in your systems. Only what is necessary for the given process is shared.

Can the AI take steps on its own, without a human?

It can, but only within boundaries we define in advance. For every deployment we split the steps into those the agent may perform on its own and those that go to a human for approval.

Can you add AI to an app we already use?

Yes, if the app provides an interface or access to its data. Typically this means summarisation, classification, suggested replies or an assistant over documents. We verify the options when defining the scope.

Do we need lots of data or our own team for AI to make sense?

No. We start with one specific process and the data you already have – documents, emails, records in your systems. The first version should show value on one scenario; we expand only based on what usage shows.

Do you use AI when building ordinary apps too?

Yes. We use AI as a tool to speed up our work on every project. Solution design, technology decisions and accountability remain with people.