Retrieval-Augmented Generation Experts in Stuttgart
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Meet FRATCH Experts in Stuttgart, who have recently used Retrieval-Augmented Generation
Artyom Narimanyan
Last position:
AI Automation Engineer & Solution Architect at Technology Research Project
Designed and developed an AI-powered automation platform using n8n to analyze social media niches, identify target audiences, and automate marketing strategy generation. The solution combined AI agents, workflow orchestration, and data analysis to automate research processes and generate data-driven insights.
- Designed and implemented complex automation workflows using n8n
- Developed AI-powered analysis agents for market and audience research
- Integrated multiple APIs and AI services into automated workflows
- Built automated market, competitor, and target audience analysis pipelines
- Leveraged Large Language Models (LLMs) for information summarization, classification, and prioritization
- Containerized and deployed the platform using Docker
Technologies: n8n, AI Agents, OpenAI APIs, Prompt Engineering, LLMs, Docker, Linux, REST APIs, Webhooks
Dennis Dickmann
Last position:
Founder at Latence
- Founded Latence to commercialise runtime safety patterns from HALO as a deployable product.
- Built end-to-end as single technical founder with open-source stack on NVIDIA ecosystem.
- Developed TRACE: real-time safety layer for knowledge agents and RAG pipelines with groundedness scoring, prompt-attack detection, GDPR redaction, context compression, audit-ready traces.
- Developed vLLM Factory: production inference framework on vLLM with custom Triton kernels and 12 parity-validated plugin models, achieving up to 11.7× throughput vs vanilla PyTorch.
- Developed ColSearch: single-node multi-vector late-interaction retrieval engine with Rust SIMD and fused CUDA, achieving 3.12× FastPlaid geomean QPS on BEIR-8 and a 1.58-bit quantized lane 6.4× smaller than FP16.
- Developed llm-opt: LLM compression research framework with hierarchical importance, structured pruning, tabu search, knowledge distillation.
Albert Frischmann
Last position:
Lead Product Owner at CMBlu Energy AG
- Lead Product Owner for 4 development teams
- Leading and coordinating a greenfield project with parallel implementation of core components by independent teams; managing dependencies and resources
- Establishing a data lakehouse approach, including analysis of data volumes and future requirements as part of a cloud migration (best-of-breed approach)
- Responsible for requirements analysis, selection, and piloting of a LIMS/ELN system, supported by advising decision-makers and managing external vendors
- Introducing and managing an OpenWeb UI and Azure OpenAI-based RAG system to support knowledge extraction and data-driven analyses
- Setting up, configuring, and managing Jira projects, as well as developing project-specific workflows and automations
- Implementing classic Scrum processes with all ceremonies and taking on the Scrum Master role for all involved teams
- Assisting in hiring through interviews and assessments from a product owner's perspective
- Making key architectural decisions, including selecting the platform for the data lakehouse (Databricks) and the strategic integration of LIMS and analytics platforms
Boas Betzler
Last position:
Technical Leader and Executive Sponsor, AI Solution Assistant
- Product owner and Executive Sponsor for AI Assistant trained on a knowledge base of past solution designs, RFP documents, and current product documentation
- Used by close to 100 global architects and engineers in pre-sales and post-sales
- Generates full solution documents with requirement driven architecture decisions, solution overview, diagrams, bill of material, roles and responsibility matrix
- Technologies: AI, LLM, Chat Bot, RAG, Agentic AI, Vector Databases, Public Cloud
Steffen Dressler
Last position:
CEO & Founder at 11bytes GmbH
- Digital Transformation & Strategy: Advising clients on developing digital business models. Supporting from the first idea through MVP development and go-live to successful scaling.
- Software Development: Designing, implementing, and operating cloud platforms. Deep hands-on experience with agile methodology (SCRUM).
- AI: Intensive building of knowledge and experience in AI-driven coding and AI solutions (AI Engineering and MLOps), focusing on data-sovereign open-source solutions and Microsoft Azure. Leading and hands-on execution of AI projects.
- Leadership: Building, leading, and developing the agency team of eleven international experts.
- Focus on Regulated Markets: Experience identifying and addressing industry-specific compliance requirements. Implemented the internal change project “ISO27001 ready”.
- Overall Entrepreneurial Responsibility: Managing delivery, sales, HR, and controlling. Ensuring highest customer satisfaction (5.0-star rating) as well as quality and efficiency in software development.
- Stakeholder Management: Collaborating with managing directors, departments, service providers, and external IT teams.
Christian Saba
Last position:
Research Associate – AI Consultant at Fraunhofer IAO
- Developed NLP and LLM POCs for use in manufacturing companies
- Applied advanced machine learning algorithms to analyze production data and develop custom data pipelines for quality assurance
- Designed and led the IAO basic seminar on AI in industry, including hands-on training modules
Sakshi Chaudhari
Last position:
Full Stack LLM Developer at Accenture
- Analyzed business needs and collaborated with stakeholders to translate them into technical requirements and user stories, guiding AI solution development within Agile Scrum teams.
- Designed, built, and deployed scalable Large Language Model (LLM) solutions supporting digital transformation initiatives, focusing on client requirements and outcome-driven delivery.
- Implemented Retrieval-Augmented Generation (RAG) pipelines using vector databases to enhance knowledge services that support business decision-making.
- Collaborated closely with cross-functional teams, including data scientists, product managers, and business analysts, to ensure AI solutions aligned with business goals.
- Provided end-to-end client support, ensuring smooth adoption and resolving operational issues in production deployments.
- Engaged in continuous learning and training to enhance consulting skills and agile project management.
- Planned and created test cases, executing manual and automated testing using Selenium and Jira for enterprise applications.
- Documented test results and collaborated with development teams to ensure high-quality software delivery.
Andreas Neumann
Last position:
Project Manager at Rundfunk Berlin-Brandenburg rbb / IVZ
- Implementation of a GDPR-compliant knowledge management system "Ylvi" using Microsoft Azure Cloud Services (EU operation)
- Deployment of RAG technology (Retrieval Augmented Generation) and ChatGPT model for a digital coaching service
- Provision of company information: training documents, training videos, intranet content, technical concepts, best-practice processes, change management materials
- Used as an expert system in user support, with key users and end users
- Knowledge transfer on LLMs and RAG
- Research and development of RAG model structure and language model configuration
- Solution architecture, document analysis, test concept for language models, knowledge transfer workshops, content analysis, test concept, system prompt development
- Development control, stakeholder management, change management, implementation, deployment
Marcel Kleber
Last position:
Freelance at Kleber Digital Consulting
Christoph Diefenthal
Last position:
Agentic RAG AI System at Financial Services Provider
- Developed an agentic RAG system to support the development organization.
- Technologies: Python, LangGraph, Qdrant, Claude Code, GitHub.
Sandra Krone
Last position:
SEO Partner at OLDSCHOOLSEO
Main Responsibilities
Technical SEO: Performance engineering and optimization of static web architectures (Next.js, Tailwind, Netlify).
Semantic SEO & Content: Conceptualization of machine-readable SEO content templates and operational, hybrid copywriting to structure content for AI search systems (RAG-readiness).
Local SEO: Data-driven management of regional visibility and optimization of Google Business Profiles for local service providers.
White-Label SEO: Hidden operational execution of end-to-end SEO campaigns for lead agencies (DACH region).
Key Achievements (KPIs)
B2C SEO (hearing care professionals): +2,370% Performance Index, +523% Visibility Index, and +100% organic traffic (estimated visits) in 36 months.
B2B IT SEO (interface development): +21,867% Visibility Index and +98% search impressions in the 3-month comparison (visibility recovery).
Technical SEO Performance (insurance): Google PageSpeed scores of 98/100 (desktop) and 97/100 (mobile) with a Largest Contentful Paint (LCP) of 1 second.
Local SEO (service provider): Increase in click-through rate (CTR) on the homepage from 1.17% to 5.72% (mobile: 9.09%).
Impact
Generative Engine Optimization (GEO): Establishment of lasting domain authority as a valid, structured data source for search engines and RAG systems.
Lead generation: Transformation of invisible websites into stable organic acquisition channels to reduce paid ads budgets.
Agency scaling: Risk-free expansion of the service portfolio for external marketing agencies through reliable white-label support.
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 14 years)
Position duration
2.8 years
Positions per freelancer
7 (Germany: 9)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Automotive, Professional Services
Certification focus areas
Information Technology, Project Management, Operations
Bachelor's degree or higher
89% (Germany: 96%)
Master's degree or higher
67% (Germany: 76%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 96%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Stuttgart are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Stuttgart using Retrieval-Augmented Generation
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What RAG does
Retrieval-Augmented Generation, often called RAG, combines search with text generation. It pulls relevant documents first, then lets a language model answer with that context. Teams use it to build assistants, internal knowledge tools, support agents, and grounded search experiences.
Core stack
RAG work usually spans several parts of the stack.
- Document ingestion and cleanup
- Chunking, embeddings, and vector search
- Prompt design and context assembly
- Model selection and fallback logic
- Evaluation for relevance and hallucination control
Where it fits
Strong RAG solutions appear in product support, sales enablement, legal search, HR knowledge bases, and engineering documentation. In Stuttgart, companies often use it where German and English content must stay aligned and searchable. It is a good fit when answers must reflect current company data, not only model memory.
Why companies bring in specialists
Freelance experts help when a prototype needs to become a dependable system. They tighten retrieval quality, reduce noisy context, and make the answer flow stable across different document types. They also help teams pick the right architecture for a pilot, an internal rollout, or a customer-facing assistant.
What strong professionals know
Good RAG specialists work across search, language models, and data pipelines. They know how to measure answer quality, handle source citations, and keep sensitive content under control. They also understand when a simpler semantic search setup is better than a full generation layer.
Delivery and collaboration
A solid engagement usually covers discovery, data review, and a working proof of concept. From there, specialists refine retrieval, test prompts, and prepare handover for the team that will run it. Remote work is common, but on-site sessions in Stuttgart can help when source systems, governance, or domain language need close review.
Frequently asked questions
Key details about Retrieval-Augmented Generation, drawn from the questions we get asked most.
Retrieval-Augmented Generation is used to answer questions from company documents, product knowledge, support articles, and other internal sources. It helps teams build assistants that can cite or reflect the content they retrieve instead of relying only on model memory. That makes it useful for search, support, and knowledge access.
RAG is more than a search box on top of a chatbot. A strong setup retrieves relevant content, assembles it into context, and then generates an answer that follows that source material. The quality depends on retrieval, chunking, prompting, and evaluation, not just the chat layer.
Retrieval-Augmented Generation is usually the better choice when the knowledge changes often or must come from trusted documents. Fine-tuning changes model behavior, while RAG keeps knowledge outside the model and easier to update. Many teams start with RAG and only fine-tune when they need style or task-specific behavior.
A strong RAG specialist often brings search, data engineering, and LLM prompt work together. Useful skills include vector databases, embeddings, document parsing, API integration, and evaluation design. For production work, security and access control matter too.
A small proof of concept may need only one focused Retrieval-Augmented Generation expert, but production work usually needs broader system thinking. The project gets harder when the source data is messy, multilingual, or permissioned. In those cases, you want someone who has shipped retrieval pipelines before.
RAG work is often done remotely because most tasks involve data, prompts, and APIs. On-site sessions in Stuttgart can still help when teams need access to sensitive content, domain experts, or internal process reviews. A hybrid setup is common when the knowledge base is complex.
Ask how the person tests retrieval quality, grounding, and failure cases in Retrieval-Augmented Generation systems. Good experts can explain their choices for chunking, reranking, citations, and fallbacks in plain language. They should also show how they prevent vague answers and source drift.
In Stuttgart, many RAG projects need both German and English content to work well. That can include policy documents, technical manuals, support tickets, and product knowledge. A good specialist knows how to handle mixed-language sources without breaking retrieval quality.
The average hourly rate of freelancers in Stuttgart, Germany who have used Retrieval-Augmented Generation in their recent projects is 96 €, which corresponds to a daily rate of about 766 € based on an 8-hour working day.
Of the freelancers in Stuttgart, Germany who have used Retrieval-Augmented Generation in their recent projects, 89% hold at least a Bachelor's degree and 67% hold at least a Master's degree.
On average, freelancers in Stuttgart, Germany who have used Retrieval-Augmented Generation in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Stuttgart, Germany who have used Retrieval-Augmented Generation in their recent projects are German (100%), English (100%), and French (36%).
The most common industries among freelancers in Stuttgart, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (91%), Automotive (55%), and Professional Services (55%).
The most common business areas among freelancers in Stuttgart, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Product Development (91%), and Project Management (55%).
Main locations of FRATCH Experts, who have recently used Retrieval-Augmented Generation
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Dusseldorf 
Dortmund 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
