AI engineering for enterprises
Our AI engineering team builds RAG systems, AI agents and custom AI software. Integrated into your existing IT landscape, including rollout and support.
Companies working with rwQUANTICAL
What is AI engineering, and does my company need it?
AI engineering means building AI into a business application so that people actually use it day to day. That takes AI engineers who understand a company's business processes and have the technical skills to automate them with AI applications.
With LLMs, it is possible for the first time to automate processes that require understanding text or interacting with people. AI agents can now take on complex tasks that until recently only people could handle.
Whether a company needs AI engineering therefore depends mainly on whether such processes are to be automated or supported with AI. AI engineering becomes relevant as soon as AI is to be integrated into existing processes and systems.
AI engineering with rwQUANTICAL
rwQUANTICAL is a consultancy specialised in AI engineering. Our AI engineers bring a deep technical understanding of current AI technologies and several years of experience in automating business processes.
We apply high standards when selecting our AI engineers. Everyone in this area brings three core competencies, which we map in our competence triangle:
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Technical expertise
Our AI engineers bring technological expertise and experience in software development. It shows in how they handle data models and their integration into process landscapes.
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Communication skills
Our AI engineers communicate clearly and in a way the business side understands. Integrity guides everything we do.
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Business expertise
Our AI engineers understand our clients' processes and translate requirements into structured solution designs. That understanding of processes is what makes solutions work in the business.
This combination lets us support companies in AI engineering end to end. Our AI engineers support clients from strategy through to technical implementation and work independently at the interface between business and technology.
Our AI engineering services for enterprises
Which initiatives are worth it is settled beforehand by our AI consulting. This is where implementation starts.
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AI prototyping
Building AI prototypes and proofs of concept to validate new use cases quickly, then moving successful approaches into MVPs and production-ready solutions.
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AI software development
Developing custom AI applications for specific business processes and requirements. This includes generative AI applications, document AI, classification and information extraction.
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AI agent development
Developing AI agents that plan and carry out tasks on their own. This includes AI agents, agentic AI systems, copilots and multi-agent systems.
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RAG development
Building RAG systems that connect language models securely to a company's own knowledge. This includes enterprise search, AI knowledge management and chatbots based on your own data.
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AI process automation
Automating business processes by combining AI with classic workflows, for example with LLMs, n8n and existing automation tools.
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AI system integration
Integrating AI applications and language models into existing IT landscapes. This includes LLM APIs and integrations into SAP, ERP, CRM and DMS systems.
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Local AI solutions
Building local AI solutions in which sensitive data never leaves the company. This includes local LLMs, on-premise AI and privacy-focused AI infrastructure.
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LLMOps & AI operations
Taking over deployment and stable operation of AI applications in production. This includes LLM monitoring, evaluation, cost control and continuous improvement.
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AI governance
Implementing the technical and organisational framework for responsible use of AI. This includes AI governance, the EU AI Act, AI compliance and responsible AI.
How we work
From the first prototype into production.
We build in four steps. After the prototype you decide whether to continue, and every further step delivers a result you can check.
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Prototype and proof of concept
We test the use case on your real data before budget goes into full development. That shows early whether the data and the model quality hold up.
Outcome: A working prototype and a sound decision on whether to continue
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MVP with the business team
Successful approaches move into a first usable version for the people who will work with it. Their day-to-day feedback flows straight into the next iteration.
Outcome: An MVP in real use, with a prioritised list of next steps
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Integration and rollout
We connect the solution to your systems, from LLM APIs to SAP, ERP, CRM and DMS. Then comes the rollout to all users, including support.
Outcome: A production application inside your IT landscape
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Operation and further development
We take over deployment and stable operation. Monitoring, evaluation and cost control run alongside, so the application gets better after launch rather than worse.
Outcome: Ongoing operation with monitoring, evaluation and cost control
Looking for interim AI engineers?
Our AI engineers join your team directly on the project. They are permanently employed with us in Germany and work across the DACH region, on site or remotely.
Questions about AI engineering
How does AI engineering differ from AI consulting at rwQUANTICAL?
AI consulting settles which initiatives are worth it and ends with a prioritised roadmap. AI engineering starts after that: we build the solution, integrate it into your systems and run it. You can also come to us directly with a finished roadmap.
Does our data have to leave the company?
Not necessarily. On request we build local AI solutions with local LLMs and on-premise infrastructure, where sensitive data stays in house. Which option fits depends on your data protection requirements and existing infrastructure. We settle that before the prototype.
Which systems can you connect?
LLM APIs as well as SAP, ERP, CRM and DMS systems. For process automation we also work with n8n and existing automation tools. Other systems we check in the prototype.
What happens if the prototype does not convince?
Then we stop, and you have a sound answer before budget has gone into full development. That is exactly what the prototype is for. Usually it also shows why: the data situation or the scope of the use case.
Who runs the solution after launch?
We do, if you want. LLMOps and AI operations are part of our services, with deployment, monitoring, evaluation and cost control. If your own team is to take over, we prepare the handover with documentation.
Can your engineers also work directly in our team?
Yes. As interim AI engineers they join your project team on site or remotely, permanently employed with us in Germany. That is the right route when you steer the development yourself and need capacity or specialist knowledge.
Which AI initiative should go into production at your company?
Describe the use case to us. We will tell you whether a prototype is the right next step and what it needs.