AI, data & analytics

This demo automates incoming email orders directly into an ERP system such as Odoo. It recognizes orders in various formats – whether it’s a PDF, Excel file, image, or plain email text – and converts the data into a consistent format before entering it into the system. This eliminates manual entry and the errors that come with it.

The benefits are immediate: order processing time drops from minutes to seconds, errors decrease, and employees gain more time for other tasks. The solution works equally well with small and large order volumes, scaling as your business grows and improving customer experience through faster and more accurate order handling.

This demo uses AI to process training and educational materials. Documents such as guidelines, onboarding materials, or extensive technical manuals can be uploaded into the system, after which users can interact with the content through AI. This makes heavy materials easy to approach and brings interactivity to training, accelerating learning.

In industrial companies, the AI Trainer can be used for onboarding new employees, internalizing safety instructions, or learning machine operation manuals. In service industries, it supports continuous learning and helps keep skills up to date.

The result is faster onboarding, fewer misunderstandings, and more consistent competence across the organization.

This solution enables users to search and ask questions directly from large documents such as maintenance manuals, technical guides, or contracts. A user can ask in natural language: “What does this maintenance manual say about the safety inspection of part X?” The AI then retrieves the correct section and provides an answer.

It speeds up information retrieval, reduces errors, and supports decision-making in environments where data is scattered and critical.

This demo converts meeting recordings or field reports into text using the OpenAI Whisper model. In industrial contexts, it accelerates maintenance documentation by eliminating the need for technicians to write reports manually. In service operations, it saves time by transcribing and analyzing customer service calls.

The benefits include faster workflows, less manual documentation, and more accurate data that can be used later in analytics.

This demo reads text from images and scanned documents. In industrial settings, it can be used to digitize handwritten maintenance reports, old drawings, or paper forms. Elsewhere, OCR speeds up the processing of documents such as contracts and invoices.

The result is less manual data entry, fewer errors, and easily searchable and usable digital data from archived materials.

This demo summarizes customer feedback or lengthy reports into clear overviews. In industrial use, it can quickly highlight key insights from supply chain reports or production quality data. In service businesses, it can process customer surveys efficiently.

The main benefit is faster decision-making since there’s no need to read entire documents the most important insights stand out immediately.

The trend analysis demo predicts the development of sales data and visually presents upcoming trends. In industrial contexts, it helps with demand forecasting, resource planning, and reducing downtime. In service businesses, it can be used to evaluate the results of marketing campaigns.

The benefits come from better forecasting and more efficient resource use, allowing the right decisions to be made in time rather than afterwards.

The customer analysis demo reviews a potential customer’s website and generates personalized outreach materials based on its content. This is especially useful for B2B sales organizations that want to prepare more effectively for bids or sales meetings. For industrial companies, it means the sales team gets pre-analyzed background data on the customer’s situation.

The benefits include faster preparation, more personalized sales communication, and higher success rates in the sales process.

This demo analyzes financial statements and long-term maintenance plans (KPTS) using AI. While originally built for real estate use, the same model applies to industrial financial reports or other large document analyses. The bot highlights key figures and risk areas, speeding up report handling and supporting decisions with clear, comparable data.

The benefits include more transparent data management and faster financial decision-making.

This demo shows how AI can control systems through natural language. The user gives a command, for example, “Scan Kuopio market square with drones 1–5,” and the AI interprets the instruction, assigns it to the drones, and simulates their operation on a map.

The solution illustrates how a language model can act as an interface for complex processes. The same principle can be applied to warehouse robots, production lines, or logistics chains. The benefits are ease of use, faster decision-making, and reduced human error.

Artificial intelligence can be harnessed to handle documents, analyze data, and even make decisions for you. For example, invoice processing becomes fully automated when GPT pulls key details (like due date and amount) from each invoice, feeds them into your accounting system, and flags any anomalies. The result: fewer errors and more time and resources for higher-value work.

A language model also makes accessing a database easy. The user doesn’t need to know SQL—just ask, “Show customers whose orders are more than a week overdue.” The AI interprets the request, converts it into the correct query, and returns the results.

OpenAI’s Function Calling capability further extends what AI can do: it can fetch data from multiple systems, process inputs, and carry out defined actions as part of an automation workflow.

Retrieval-Augmented Generation combines information pulled from your company’s databases or documents with the answer generated by the AI, ensuring the content is both accurate and perfectly suited to your business context.

AI doesn’t just analyze data – it can also act as an active part of your processes. OpenAI’s Function Calling capability extends these possibilities by connecting AI directly to systems and automated tasks.

In practice, this means AI can fetch data from multiple systems, process inputs, and execute defined actions as part of automation. This speeds up and simplifies workflows, for example, checking inventory levels, logging maintenance needs, or generating reports can all be done with a single AI command.