Help your people find answers faster, process information and prepare materials for decision-making. We will design practical uses of AI for everyday work, from searching company knowledge to summarising documents and preparing content. We will select suitable tools and set up their use with your data and security in mind.
Selecting the right technology, from conventional automation to advanced AI, based precisely on the needs of a specific business process.
Helping employees make decisions and providing fast access to internal information.
Using AI meaningfully to simplify routine work instead of merely creating text or generating chats.
Search, summarization and explanation in natural language.
Repetitive activities and process steps can be delegated to AI and automation.
AI can connect to internal documents and knowledge sources.
Assistants help with specific tasks in the right work context.
Support for text, video, learning materials and internal communication.
AI, automation and existing systems can work together in one workflow.
An AI assistant is a digital helper for a specific employee, team or process. It can answer questions, prepare materials, summarize documents, support communication or find information in company sources.
RAG (Retrieval-Augmented Generation) connects generative AI with a company's own knowledge. The system first finds relevant information in selected sources and then uses it to prepare an answer.
Alongside generating an answer, an AI agent can perform individual task steps, gather and evaluate information, prepare a proposal and use a connected tool or system according to defined rules and permissions.
AI avatars and AI video make it possible to create digital presenters and audiovisual content for learning, information and internal communication.
AI governance sets rules, roles and processes for safe and controlled AI use, including responsibility for data and outputs, access rights and risk management.
Select a process where AI can deliver a concrete benefit.
Assess the workflow, systems, data and risks.
Choose automation, an assistant, RAG, an agent or a combination.
Validate the benefit on a specific use case with a limited scope.
Connect AI to relevant systems and processes after the pilot is validated.
Set responsibilities, access, rules and monitoring.
Extend a successful use case to other teams or processes.
Together, we will find a suitable use case and define the next steps.
Search across internal policies, technical documentation, knowledge bases and document archives.
AI avatars and AI video make it possible to create digital presenters and audiovisual content for learning, information and internal communication.
Helpiner turns natural-language requests into governed workflows across connected business systems.
A natural-language interface to enterprise information and selected workflows across SAP, ERP and CRM systems.
Safe and responsible AI adoption starts with a clear governance framework. It connects business goals, data security, transparency, accountability and risk control. It helps define where AI creates value, which rules apply and how output quality is reviewed over time.
Regulatory and internal policy compliance, protection of personal and sensitive data, access management, decision transparency, human oversight, monitoring, data-source management and output reliability testing.
The governance framework is reviewed as the solution, data, risks and business requirements evolve, keeping AI use reliable and accountable over time.
Conventional automation suits stable processes with precisely defined inputs, outputs and rules, where deterministic repeatability matters.
Generative AI is suited to natural language, unstructured documents, text requests, content creation and summarization, or semantic search through extensive knowledge sources. The two approaches can be combined.
In a combined workflow, AI interprets the request and identifies its type, automation runs specific steps, the system records the result, and a person approves a decision that requires accountability.
It is the use of artificial intelligence to support or automate specific business activities, for example through AI assistants, RAG systems, AI agents or AI videos.
It is for companies that want to make employees' work more efficient, use internal knowledge better, automate repetitive processes or provide digital support to employees and customers.
Yes. A solution can work with existing data, documents and business systems based on available interfaces and process requirements.
Yes. A solution can be designed around a specific process, available data, internal rules and user needs.
The solution receives an input such as text, a document or a request, analyzes it, finds relevant data, creates an output or performs defined steps. It can work independently, with human review or as part of a workflow.
Implementation time depends on the use-case complexity, data availability, required integrations and governance requirements. A specific timeframe should be set only after these areas are analyzed.
RAG connects generative AI with company documents and other knowledge sources so it can use information from specific company sources when answering.
An AI assistant helps with specific tasks, while an AI agent can perform subsequent process steps according to defined rules and work with connected tools or systems.
Conventional automation suits stable, precisely defined processes. AI is more useful for natural language, unstructured data, documents and information interpretation.
Responsibility is divided by system and process between the business owner, technology owner, data owner, security or compliance roles and users according to their responsibilities.
Depending on the project scope, cooperation can continue after implementation. AI solutions need monitoring of output quality, knowledge freshness and integrations, as well as further development when the company's needs change.