Company data that's prepared
for AI agents

Claude, ChatGPT, Microsoft Copilot Studio and other AI assistants connect to BizMachine data through the MCP connector. The data covers 4 million companies in Czechia and Slovakia and another 8.1 million in Poland, Hungary and Germany.

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4M+

Companies in Czechia and Slovakia

12M+

Companies in 5 markets in total

100+

Data sources

55+

Company signals

BizMachine prepares verified data in the exact format your agents need

AI built on verified data is more accurate, hallucinates less, and enables reliable automation, auditable decisions, and faster deployment. This is the decision-making infrastructure that enterprise AI relies on.

Transparent data from 100+ sources

Each value comes with the date it is valid for. Changes from the commercial register and the contract register also carry a link to the source document. The response also tells you why a value is missing: if it could not be calculated, or that indicator is not enabled for you.

Documentation that AI can read

Every indicator has a described meaning, range, and bands, so the agent doesn't have to guess where a high value begins. Along with the number, it requests the factors that form it. That’s the difference between "the company has 82" and "the company has 82 out of 100 because it is hiring for development and has won two contracts."

Data prepared by domain

The REST API has separate endpoints for company profiles, events, job ads, e-shops, vehicles, and other domains. The MCP connector builds on this with three levels of detail: a short overview, a full profile, and a single-domain detail. This way, the agent first checks how many companies match the criteria and only requests full data where it’s worth it.

Follow-up steps and write-back

Every record includes a company ID (IČO), and if applicable, a VAT ID and website. From a found company, you can move to its AI Prioritization score, then to the factors that make it up, and from there to specific business signals. The agent can write the result back into Prospector as a tag that you can share with your team.

Agent without BizMachine MCP

Agent searches for facts on the web. It is hard to know if a number is real or possibly made up. The agent focuses on information retrieval and doesn't have as much room for interpretation.

The agent has good information on large, well-known companies. Small and medium-sized ones are often missing from its training data, and more importantly, you don't know which companies the model skipped.

The agent only knows the current state available on the web. It can't piece together how a company performed three years ago or what has changed since then.

The agent won't catch a change you'd want to react to on its own. You have to ask specifically and hope that it was written about somewhere.

You have to go through hundreds of companies one by one, in depth. It’s slow, token-intensive, and the results are different every time.

Team output gets lost. The text stays trapped in the conversation. Whatever you discover usually ends up in some export, and next time, you start all over again.

The agent cannot tell which companies visited your website. That data sits in a separate tool, outside the conversation.

You hope the data is fact-based

Agent with BizMachine MCP

Agent takes facts from the BizMachine database where you can see e.g. the update date for each record. This leaves the AI agent free to focus on interpreting and synthesizing the data.

12+ million companies in five countries, including 2.9 million in the Czech Republic. Before the agent downloads the first profile, it will tell you how many companies actually match your criteria.

For Czech and Slovak companies that file financial statements, the agent sees which years are on file and gets
the latest balance sheet and income statement line by line. It works with the detail, not only the final number.

More than 55 types of tracked changes: new executive, change of ownership, insolvency, won public contract, new branch and more.

Large-scale analysis is no problem. A short overview as a first filter, a full profile for a shortlist, and a deep dive into a single domain.

Preserved know-how. Save your findings directly as a label in Prospector and share them with your team. You will have them available for your next query as well.

WebTracks identifies companies among your website visitors and saves them to labels in Prospector. The agent sees these labels and filters the companies by industry, size, signals or AI Prioritization value.

Your agent works with verified data

Can ChatGPT or Claude replace a company database?

ChatGPT and Claude are good at general questions about companies. For systematic market research they have three gaps.

Thin coverage of small companies

A model knows large, well-known companies. Its knowledge of small Czech and Slovak companies is uneven, and you cannot see which ones it skipped.

No structured history

A model cannot reliably list the financial statements a company filed over the years, or give a dated list of what changed at the company.

Figures without a date or source

A figure from a model usually has no date. If the model names a source, it is often a web page, not an official register.

The MCP connector closes these gaps without replacing your assistant.

ChatGPT or Claude still does the analysis and the writing. The facts come from the BizMachine database, which covers 4 million companies in Czechia and Slovakia and 8.1 million in Poland, Hungary and Germany. Each value carries the date it is valid for. More than 55 types of company changes are tracked with dates.

Join hundreds of successful companies using BizMachine.

“Thanks to the fact that we have Prospector integrated via iFrame directly into our internal application, we have in one place detailed data about each company within the Czech and Slovak markets.”

Jakub Kuráň

Head of Sales Operations

Global Payments

Prospector has helped us significantly refine the acquisition of new partners and the work with existing clients. Today, traders are more proactive and reaction times towards clients have noticeably shortened.

Robert Pradl

Sales Lead

Liftago

“Prospector quickly settled in with us. Today, colleagues from our sales team can no longer imagine a day without it, and you can know it from the results of the company. We enjoy the fact that it is modern, intuitive to use and understands perfectly with the other tools we use. “

Marek Oppel

Sales Performance Manager

PPL

BizMachine customer testimonials

Frequently asked questions

Questions we receive about AI integration

Company data that's prepared for AI agents

Claude, ChatGPT, Microsoft Copilot Studio and other AI assistants connect to BizMachine data through the MCP connector. The data covers 4 million companies in Czechia and Slovakia and another 8.1 million in Poland, Hungary and Germany.

4M+

12M+

100+

55+

BizMachine prepares verified data in the exact format your agents need

AI built on verified data is more accurate, hallucinates less, and enables reliable automation, auditable decisions, and faster deployment. This is the decision-making infrastructure that enterprise AI relies on.

Transparent data from 100+ sources

Documentation that AI can read

Data prepared by domain

Follow-up steps and write-back

Agent without BizMachine MCP

Agent searches for facts on the web. It is hard to know if a number is real or possibly made up. The agent focuses on information retrieval and doesn't have as much room for interpretation.

The agent has good information on large, well-known companies. Small and medium-sized ones are often missing from its training data, and more importantly, you don't know which companies the model skipped.

The agent only knows the current state available on the web. It can't piece together how a company performed three years ago or what has changed since then.

The agent won't catch a change you'd want to react to on its own. You have to ask specifically and hope that it was written about somewhere.

You have to go through hundreds of companies one by one, in depth. It’s slow, token-intensive, and the results are different every time.

Team output gets lost. The text stays trapped in the conversation. Whatever you discover usually ends up in some export, and next time, you start all over again.

The agent cannot tell which companies visited your website. That data sits in a separate tool, outside the conversation.

You hope the data is fact-based

Agent with BizMachine MCP

Agent takes facts from the BizMachine database where you can see e.g. the update date for each record. This leaves the AI agent free to focus on interpreting and synthesizing the data.

12+ million companies in five countries, including 2.9 million in the Czech Republic. Before the agent downloads the first profile, it will tell you how many companies actually match your criteria.

For Czech and Slovak companies that file financial statements, the agent sees which years are on file and gets the latest balance sheet and income statement line by line. It works with the detail, not only the final number.

More than 55 types of tracked changes: new executive, change of ownership, insolvency, won public contract, new branch and more.

Large-scale analysis is no problem. A short overview as a first filter, a full profile for a shortlist, and a deep dive into a single domain.

Preserved know-how. Save your findings directly as a label in Prospector and share them with your team. You will have them available for your next query as well.

WebTracks identifies companies among your website visitors and saves them to labels in Prospector. The agent sees these labels and filters the companies by industry, size, signals or AI Prioritization value.

Your agent works with verified data

Can ChatGPT or Claude replace a company database?

ChatGPT and Claude are good at general questions about companies. For systematic market research they have three gaps.

Thin coverage of small companies

No structured history

Figures without a date or source

BizMachine MCP + your AI tools + your know-how = the sales team of the future

Find a new segment of prospects without clicking through filters

Describe your segment in a sentence, not by clicking through filters. The assistant will tell you how many companies match and save them to a label in Prospector.

Meeting preparation

Instead of manually searching through registries or gathering information from the web, you get a briefing based on verified data. What the company does, how big it is, its financial health, and what has been happening there recently.

Timing for your outreach

Ask about your customer list and get quick information on what has changed. New management, hiring, a won tender, or a drop in revenue are reasons to call right now.

Market size

How many companies actually fit your ideal customer profile? You will get the answer in a few seconds, along with a sample of companies so you can see exactly who fits.

Which visiting companies fit your ICP

Build your business decisions on BizMachine data—whether they are made by a human or AI.

Join hundreds of successful companies using BizMachine.

Questions we receive about AI integration

Updated in August 2026

Which AI assistants can I connect to BizMachine?

Is ChatGPT or Claude enough instead of a company database?

Do I need a developer to connect the MCP connector?

When should I use the REST API instead of the MCP connector?

Can my AI assistant see which companies visited our website?

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Company

Products

Industries

Data

Company data that's prepared
for AI agents

For Czech and Slovak companies that file financial statements, the agent sees which years are on file and gets
the latest balance sheet and income statement line by line. It works with the detail, not only the final number.