Retrieval-Augmented Generation Experts
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Meet FRATCH Experts who have recently used Retrieval-Augmented Generation
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
- Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
- Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
- AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Creation of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization, and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Qamar H.
Last position:
Freelance Consultant Data Analytics & AI Portfolio at TIC Company
- Support for a data, analytics and AI initiative in a regulated enterprise environment by structuring, evaluating and prioritizing several data-driven use cases based on business impact, feasibility, scalability, data maturity and governance requirements.
- Translation of complex business and analytics requirements into clear product, data and implementation logic, as well as preparation of decision-ready documents, target visions and roadmap inputs for stakeholder and management discussions.
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Khalid E.
Last position:
Lead Architect & Developer at kem-consulting
Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.
Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.
Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.
Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.
Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.
Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.
Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Sahra H.
Last position:
Interim Manager at TelemaxX Telekommunikation GmbH
Following the turnaround completed in 2025, the company is pursuing a growth target of more than €50 million in revenue by 2030, driven by the Datacenter First strategy, efficiency gains through AI and a restructuring of sales. This required a market-oriented product portfolio and documented and automated processes from lead to invoice.
Restructuring of the product portfolio for data centre, colocation and cloud.
Analysis of market drivers and growth areas for portfolio and pricing decisions.
Documentation and optimisation of processes from lead to invoice and up-selling.
Introduction of AI-supported systems in sales and marketing to automate these process steps.
Regulatory assessment of AI use, particularly for customers from the banking sector.
Redesigned product portfolio for data centre, colocation and cloud as the basis for growth planning.
Fully documented process map from lead generation to invoicing.
Prioritised AI use cases for sales and marketing with a regulatory assessment for each use case.
Established governance for the use of AI in a critical infrastructure environment.
Alignment of product, sales and marketing management based on shared KPIs.
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Yashar S.
Last position:
Compliance Consultant at RAS Reinhardt Maschinenbau GmbH
- Designed and moderated a NIS2 preparation workshop for RAS Reinhardt Maschinenbau GmbH and its IT service provider Catuno GmbH. Together with executive management and IT leadership, the current status was assessed, an initial GAP analysis was conducted and areas for action were prioritized based on ISO 27001.
- Developed an ISO-27001-based regulatory framework (ISMS) for NIS2 compliance, including a structured current/target GAP analysis, targeted improvement of the security maturity level and preparation of the organization for NIS2 audit readiness.
Dmitry P.
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Fadi S.
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Ali A.
Last position:
Founder & Architect at Independent AI R&D
- Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
- GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
- AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner
Folke V.
Last position:
Nameling – AI-supported product development
- Relaunched a self-developed semantic name recommendation product by combining semantic search, graph-based similarity analysis, LLM-/RAG-supported content, and AI-assisted development processes.
- End-to-end responsibility across the product lifecycle—from use case definition and solution design through prototyping and evaluation to the iterative development of the roadmap.
- Evaluated AI use cases in terms of user value, technical feasibility, data quality, governance, and operating costs to guide MVP scope, roadmap decisions, and continuous product improvement.
Matthias S.
Last position:
Technology Lead & Co-Founder at LegalMind GmbH
- Redesign of legal operations: standardised workflows reducing routine effort by up to 80%, with source citation, hallucination check as quality gate, role model, logging and audit trail.
- Compliance-by-design operating model (EU AI Act readiness, GDPR, eIDAS) with documented, releasable process steps.
- Roadmap, sprint planning and release management for an agentic RAG platform with counsel-in-the-loop approval, audit trail and German hosting.
- EU AI Act readiness, GDPR and eIDAS requirements managed as first-class project deliverables; go-to-market for two customer verticals.
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.8 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Education
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
97%
Master's degree or higher
75%
Doctorate
13%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
97%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts 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 26 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Retrieval-Augmented Generation experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (95%)
- Professional Services (43%)
- Education (39%)
- Automotive (39%)
- Banking and Finance (38%)
- Manufacturing (32%)
- Healthcare (31%)
- Retail (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RAG does
Retrieval-Augmented Generation, commonly called RAG, combines information retrieval with text generation. Before a language model answers, the system searches approved sources and adds relevant passages to the prompt. This helps produce answers grounded in company knowledge rather than relying only on training data.
What it builds
RAG is used for assistants, internal search, support tools and research workflows that must use current or private information. Typical projects include:
- Enterprise knowledge assistants for policies, manuals and product information
- Document question-answering for contracts, reports and technical records
- Customer support systems connected to trusted service content
- Search and summarisation across structured and unstructured data
Core ecosystem
A RAG solution often combines an embedding model, a vector database and a large language model. Specialists work with tools such as OpenAI APIs, Hugging Face models, LangChain, LlamaIndex, Elasticsearch, Weaviate, Pinecone, pgvector and Azure AI Search. They also connect ingestion pipelines, document parsers, metadata stores and observability tools.
When to bring in expertise
Companies usually need freelance expertise when a prototype must become a dependable product, when existing search returns weak context, or when sensitive data requires careful access controls. A specialist can select chunking and embedding strategies, design retrieval flows, connect enterprise sources and establish evaluation methods without forcing a complete platform change.
Skills that matter
Strong professionals understand both information retrieval and language-model behaviour. They assess precision, recall, grounding, latency and answer usefulness rather than judging output by fluency alone. They can handle hybrid search, reranking, query rewriting, citation handling, prompt design, API integration and protection against prompt injection or data leakage.
Signs of quality
Look for evidence of production systems that retrieve from real business data, not only demonstrations using clean sample documents. A capable specialist explains why a retriever, embedding model or vector store fits the data and shows how results are tested. They define source permissions, fallback behaviour, monitoring and human review before launch, then improve the system using measured failure cases.
Frequently asked questions
Need clarity? These are the questions we hear most often about Retrieval-Augmented Generation.
Retrieval-Augmented Generation is used to create language-model applications that answer questions using selected external sources. Common examples include internal knowledge assistants, document search, customer support tools and research systems.
RAG supplies relevant information at query time, while fine-tuning changes a model's learned behaviour through additional training. RAG is often easier to update and audit for changing knowledge, whereas fine-tuning may suit stable style, format or task patterns.
A strong Retrieval-Augmented Generation specialist usually understands embeddings, vector and hybrid search, data ingestion, prompt design and API integration. Experience with access control, evaluation, observability and cloud infrastructure is also valuable for production work.
The right level depends on the scope and risk of the system. A small proof of concept may need focused retrieval and model-integration skills, while a production system benefits from experience with data quality, permissions, evaluation, monitoring and failure handling.
Yes, RAG work is often suitable for remote collaboration because data flows, retrieval tests and application changes can be reviewed online. On-site work may help when source systems are isolated, security reviews require physical access or teams need close workshops.
Ask how the specialist measures retrieval quality and detects unsupported answers. A credible Retrieval-Augmented Generation professional discusses chunking, metadata, reranking, citations, access controls and evaluation sets, rather than focusing only on fluent responses.
RAG is not automatically the best answer for every search problem. Conventional keyword, semantic or hybrid search may be preferable when users need exact filtering, transparent document navigation or deterministic results without generated text.
Common problems include poor document parsing, oversized or incomplete chunks, weak metadata, irrelevant retrieval and answers that exceed the supplied evidence. A capable RAG specialist also checks prompt injection, permission leaks, stale sources, latency and model hallucination.
The average hourly rate of freelancers 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 who have used Retrieval-Augmented Generation in their recent projects, 97% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers who have used Retrieval-Augmented Generation in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers who have used Retrieval-Augmented Generation in their recent projects are English (98%), German (96%), and French (17%).
The most common industries among freelancers who have used Retrieval-Augmented Generation in their recent projects are Information Technology (95%), Professional Services (43%), and Education (39%).
The most common business areas among freelancers who have used Retrieval-Augmented Generation in their recent projects are Information Technology (98%), Product Development (94%), and Business Intelligence (59%).
Main locations of FRATCH Experts, who have recently used Retrieval-Augmented Generation
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Nuremberg In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
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