
AI Agents Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used AI Agents
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
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.
Chris W.
Last position:
Senior Strategy Advisor, Transformation Lead – program realignment with target picture, governance, and priority steering at Sparkassen-Finanzgruppe | S-Communication Services
In-house consulting provider and driver of transformation within the group, multi-stakeholder environment and C-level.
Realignment and stabilization of a cross-functional transformation and scaling program within the group. Sharpening the target picture, priorities, and set of measures, as well as building reliable governance, planning, and steering structures. Structuring roles, responsibilities, and strategic initiatives while including AI and IT automation ideas.
Designed program realignment and project portfolio management
Developed strategy model and target picture for IT projects
Structured portfolio, roadmap, and priorities
Established governance and regular meetings
Worked out operating model for flagship projects
Assessed AI and automation ideas
Clarified roles and responsibilities
Implemented change measures
Developed, moderated, and evaluated workshops
Transformed 17 initiatives into a steering model
Increased transparency and decision-making ability
Strengthened commitment in steering
Sharpened the operating model structurally
Integrated three top-5 institutes
Involved over 80% of stakeholders
Governance
Portfolio steering (PPM)
Change management
Artificial intelligence
Workflow automation
AI use case assessment
Confluence
Jira
Stakeholder management
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Bidya B.
Last position:
Product Manager – Payments & Platform at Pipedrive
CRM and revenue platform managing billing and subscription workflows.
- Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
- Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
- Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
- Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.
Joost D.
Last position:
Independent Product & Venture Builder at Self-employed
- Ran structured venture discovery across five fields (real estate, trades & property management, psychotherapy & coaching, social impact). Conducted 100+ interviews plus surveys and ad tests, with kill criteria defined upfront for every hypothesis
- Designed and built AI agents for my own workflows: voice-message transcription and summarization, a journaling app with UI, a voice assistant for task planning, plus research and discovery-synthesis agents (Claude Code, Cursor)
- Built an AI-assisted tool that crawls and values property listings to pre-screen investment opportunities
- Executed several real estate projects in Germany and abroad, from sourcing and valuation to acquisition, renovation and operations
Abdulla A.
Last position:
Principal AI Product Consultant at Recare
- Shipped Recare Voice Desktop from 0 to 1 in two months, including multi-language clinical documentation that auto-transcribes into structured German medical notes.
- Reduced LLM inference costs by 60–70% across Docs and Extract through prompt caching architecture.
- Built the AI workbench used by PMs/engineers for prompt experimentation and the Langfuse eval stack (10k+ traces evaluated).
Hubertus S.
Last position:
Senior Product Manager AI
Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.
- Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
- Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
- Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
- Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
- Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.
Myrto P.
Last position:
UX Lead, Strategist for Property Management Systems at Destination Solutions
- Leading UX for a Property Management System, an all-in-one solution for vacation rental agencies and tourism regions, covering marketing and rental of holiday apartments and houses
- UX audits, conception, and implementation of UX strategy with a focus on regulatory, security, and user-centered requirements
- Advising C-level stakeholders on UX strategy and design best practices
- Planning and conducting research with agencies and property owners
- Design system strategy and definition of UX architecture
Pradeep S.
Last position:
Tech Product Lead – AI, Data & Platform Products at Elli GmbH- A brand of Volkswagen
- Own the 12–18 month roadmap and key outcomes for Elli's enterprise customer platform, covering onboarding, pricing, billing, analytics and broader platform modernization; redesigned the Fleet onboarding funnel to double conversion, supporting a projected €20.7M revenue uplift by 2028.
- Lead the broader Energy Intelligence product and directly own its AI/ML, asset and portfolio-optimization capabilities, including MLOps and safe strategy deployment, strategy lifecycle management and backtesting; delegated data and V2G integration roadmap ownership to a new PO as the platform scope expanded.
- Built a Human-in-the-loop GenAI/RAG support workflow, increasing L1 resolution by 24%, routing accuracy to 91%, and reducing L2 workload by 30%.
- Introduced standardized data contracts and a self-service Python toolkit for traders and Data Scientists, increasing platform adoption by 15% and reducing support effort by 50%.
- Built and scaled a real-time orchestration product from 32 to 3,000+ endpoints across four markets, growing recurring revenue from €1.4k to €56.3k MRR.
- Developed product and AI capability across the organization, training 20 PMs on RAG, agents and prototyping; mentoring a junior PM and coaching an Enterprise Platform Tech Lead toward Product Management ownership.
Tommy S.
Last position:
Process Manager · Order-to-Cash & Automation at EWE Tel GmbH
- Root cause analysis of complex business, technical, and data-related errors in PowerCloud across process, booking, and system boundaries.
- Data-driven management of payments and receivables; contributed to reducing historical receivables from over 100 Mio. EUR to under 25 Mio. EUR.
- Identification of automation and straight-through processing potential at the interface between business departments, IT, and external service providers.
Saman S.
Last position:
AI Product Builder at Instalemon.com
- Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
- Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
- Designed and implemented evals and observability through Mastra studio.
- Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
- Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.
Mukund B.
Last position:
Voice AI Chatbot - Real-Time Audio Assistant
- ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.
Marvin M.
Last position:
Co-founder & CTO · Freelance Software Engineer (AI & SaaS) at Self-employed
- Self-employed · Berlin, Germany
- Co-founded the company and own the entire technical side: product architecture, backend, frontend, infrastructure and operations.
- Designed and built the Automated Booking System (ABS) as well as the core platform and payment logic.
- Development of AI-powered SaaS products, from architecture through backend and frontend to production operation.
- Technical consulting on product architecture, data protection and permissible automation.
- Building, running and monetising my own API products for AI agents, each available as a REST interface and as an MCP server.
- Full ownership of architecture, infrastructure, billing, legal texts and go-to-market.
Aruldass A.
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Discover over 15,000 top freelancers
Statistics of experts using AI Agents
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 15 years)

Position duration
2 years (Germany: 2.8 years)

Positions per freelancer
9 (Germany: 10)

Top business areas
Information Technology, Product Development, Project Management

Top industries
Information Technology, Banking and Finance, Education

Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
98% (Germany: 96%)
Master's degree or higher
66% (Germany: 70%)
Doctorate
5% (Germany: 12%)

Certifications per freelancer
2 (Germany: 3)

Most common languages
English, German, Spanish

Speak two or more languages
93% (Germany: 97%)
Based on our profile pool as of 9 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin 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 in Berlin using AI Agents
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 9 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
AI Agents 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 (98%)
- Banking and Finance (51%)
- Education (44%)
- Professional Services (43%)
- Healthcare (41%)
- Retail (39%)
- Automotive (31%)
- Manufacturing (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What AI Agents Are
AI Agents are software systems that interpret goals, plan actions, use tools, and adapt their next step based on results. They often combine large language models with business rules, APIs, memory, retrieval, and approval controls. The terms agentic AI, autonomous agents, and LLM agents are commonly used for related approaches.
What They Build
Companies use agents to turn natural-language requests into controlled business actions. A well-designed system can research information, update records, prepare documents, route requests, or coordinate several specialist services while keeping people in the loop.
- Customer support and service-desk workflows
- Research, summarisation, and document review
- Sales operations and CRM assistance
- Internal knowledge and retrieval assistants
- Multi-step automation across business systems
Ecosystem And Tools
AI agent work spans model providers, orchestration libraries, data stores, and observability tools. Specialists may work with OpenAI APIs, Anthropic models, LangChain, LlamaIndex, vector databases, function calling, structured outputs, and evaluation frameworks. Strong delivery also requires API integration, secure identity handling, and production deployment.
When To Hire Specialists
Freelance expertise helps when a prototype must become a dependable product, when an existing assistant gives inconsistent results, or when sensitive company data needs stronger controls. Berlin companies often use a mix of remote and on-site collaboration, with English commonly used across international teams and German useful for local workflows.
- Define agent goals, tools, permissions, and escalation paths
- Connect models to CRM, ERP, support, or knowledge systems
- Test accuracy, safety, latency, and failure handling
- Monitor conversations and improve prompts and workflows
What Strong Professionals Bring
The best professionals treat AI Agents as software systems, not just prompt experiments. They separate planning from execution, constrain tool access, design useful fallbacks, and make decisions traceable. They can explain when a conventional workflow, search system, or deterministic integration is safer than an agent.
They also understand model selection, prompt design, retrieval quality, data protection, evaluation, and cloud operations. Look for clear architecture, realistic testing, readable integrations, and evidence that the system behaves well outside ideal examples.
Project Collaboration And Delivery
A successful engagement starts with a defined business outcome and a review of the data, systems, and permissions involved. The specialist should map the agent’s actions, human approval points, and measurable acceptance criteria before building. Iterative delivery works well: validate a narrow workflow, observe real usage, then expand carefully.
Remote collaboration is practical when documentation, access management, and feedback routines are clear. On-site work in Berlin can help with workshops involving operations, compliance, or domain teams, especially when the agent will handle sensitive internal processes.
Frequently asked questions
Quick answers to the questions that come up most around AI Agents.
AI Agents are used for tasks that require interpretation, planning, and action across connected systems. Common examples include support triage, knowledge research, document processing, sales assistance, and internal workflow automation. They work best when their tools, permissions, and human review points are clearly defined.
AI Agents can choose among approved tools and adjust their plan as they receive results, while a conventional chatbot mainly generates replies. Rule-based automation follows predefined paths and is often more predictable. Agentic AI is useful when a workflow contains varied inputs, but deterministic automation may be safer for fixed, high-risk operations.
A strong AI Agents specialist usually understands API integration, Python or TypeScript, cloud deployment, databases, retrieval systems, and identity management. They should also know prompt design, structured outputs, evaluation, observability, and data protection. Experience with business process analysis helps turn a promising demo into a useful workflow.
The right level depends on the scope and risk of the work. A small internal prototype may need a specialist who can validate models, prompts, and integrations, while a customer-facing system needs proven skills in testing, security, monitoring, and failure recovery. For regulated or sensitive use cases, ask for relevant production delivery rather than judging experience by project duration alone.
AI Agents projects can usually be delivered remotely when access, documentation, and stakeholder feedback are well organised. On-site workshops in Berlin may be valuable for mapping processes, reviewing sensitive data, or aligning several business teams. English is common in international technology teams, while German can matter for local users and operational content.
Ask an AI Agents professional to explain the system’s goals, tool permissions, fallback paths, and evaluation method in plain language. Good specialists test difficult and unexpected inputs, track failures, protect sensitive data, and make important actions reviewable. A clear architecture and working evidence are stronger signals than an impressive demo alone.
AI Agents make sense when users express varied requests and the system must select from several approved actions. A conventional workflow is often better when inputs and outcomes are stable, rules are strict, or every result must be identical. A careful specialist compares both options instead of forcing an agent into a problem that does not need one.
Before engaging an AI Agents freelancer, define the business outcome, source data, connected systems, permitted actions, escalation rules, and ownership of the final decisions. Agree how quality, security, cost control, and ongoing monitoring will be reviewed. A narrow first release with explicit acceptance criteria makes later expansion safer.
The average hourly rate of freelancers in Berlin, Germany who have used AI Agents in their recent projects is 93 €, which corresponds to a daily rate of about 743 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used AI Agents in their recent projects, 98% hold at least a Bachelor's degree, 66% hold at least a Master's degree, and 5% hold a doctorate.
On average, freelancers in Berlin, Germany who have used AI Agents in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Berlin, Germany who have used AI Agents in their recent projects are English (100%), German (93%), and Spanish (16%).
The most common industries among freelancers in Berlin, Germany who have used AI Agents in their recent projects are Information Technology (98%), Banking and Finance (51%), and Education (44%).
The most common business areas among freelancers in Berlin, Germany who have used AI Agents in their recent projects are Information Technology (95%), Product Development (95%), and Project Management (59%).
Main locations of FRATCH Experts, who have recently used AI Agents
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.
Countries:
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