Trainings, Workshops & Consultancy
From Zero to Production AI in Three Phases
Each phase is an independent 2–3 day workshop with a detailed handbook. Pick one, combine two, or take all three. We also offer AI/ML consultancy across all topics.
3
Independent Phases
2–3
Days Per Phase
20+
Production Tools
100%
On-Premises / Local
With Cloud Training
What We Do
Applied AI Consulting & Training
18+ years of combined production experience — from strategy to shipping.
AI Advisory
Strategic guidance to help you navigate AI adoption with clarity — from opportunity assessment to production roadmap.
- AI strategy & roadmapping
- Technology assessment & architecture review
- ROI analysis & build-vs-buy decisions
- Data sovereignty & risk mitigation
Hands-on Implementation
We build alongside your team — production-grade AI systems, not throwaway prototypes.
- End-to-end RAG, Agents & ML system development
- Team augmentation & pair engineering
- Production deployment & optimization
- Cloud migration (AWS, GCP, Azure)
Corporate Training & Workshops
Structured programs that take your team from zero to production-ready AI — or fully custom workshops designed around your stack.
- 2–3 day hands-on workshops with detailed handbooks
- Technical training for engineering teams
- Custom curriculum designed for your use case
- University programs & conference tutorials
Explore our structured training phases below, or and we'll design something entirely custom for your team.
Why Us
With 18+ years of combined experience, we've built and shipped ML systems serving millions of users — recommendation engines, search & information retrieval, NLP pipelines, RAG systems, AI agents, MLOps infrastructure, customer analytics, and marketing optimization (Target ROAS) across industries at scale.
Every tool we recommend, every pattern we teach, and every warning we give comes from systems we've personally built, debugged, and operated — not from textbooks or tutorials.
The Training Program
Three Phases to AI Independence
Every phase is independent — take Phase 3 on its own, combine Phase 1 & 2 into a single intensive week, or go through all three. Each comes with a comprehensive setup handbook.
Build Your RAG Foundation
The Intelligent Search & Answer Engine
Build a complete document-intelligence system from scratch. Upload documents, search them semantically, and generate answers with a locally-running LLM — no cloud dependency, full data control. You'll set up every layer yourself: parsing, chunking, embedding, retrieval, and generation.
What You'll Build & Learn
- Build a fully functional RAG pipeline end-to-end
- Run LLMs locally with Ollama — no API keys, no cloud
- Semantic vector search with OpenSearch
- Streaming responses for real-time UX
- Containerized with Docker — reproducible everywhere
- Detailed setup handbook included
Tools & Technologies
Docling + OCR
Extract text and structure from PDFs and scanned documents
By the end of Phase 1
A working document Q&A system running entirely on your own hardware
Understanding of the full RAG pipeline: parse → chunk → embed → retrieve → generate
Hands-on experience with vector databases and semantic search
Ability to run and manage local LLMs independently
Production Quality & Scale
From Prototype to Production-Ready System
Transform your Phase 1 prototype into a production-grade system. Add automated data pipelines, hybrid search that combines meaning and keywords, response caching, structured logging, end-to-end observability, and evaluation metrics. This is where you learn the engineering that separates a demo from a system you can trust.
What You'll Build & Learn
- Automated ingestion pipelines with Apache Airflow
- Hybrid search: BM25 keywords + vector semantics combined
- Re-ranking for dramatically better retrieval quality
- Redis caching to cut latency and LLM costs
- Full observability with Langfuse / Opik — traces, metrics, evaluation
- Load testing to find and fix bottlenecks
- Detailed production handbook included
Tools & Technologies
Pydantic / SQLAlchemy
Data validation, structured logging, and database abstraction
By the end of Phase 2
A production-ready RAG system with automated pipelines and monitoring
Measurable retrieval quality through evaluation metrics and dashboards
Cost optimization through caching and performance profiling
Confidence in system reliability backed by observability data
Cloud migration readiness — understand how to map local infra to AWS/GCP
AI Agents & Advanced Systems
Autonomous, Tool-Using, Enterprise-Ready Agents
Go beyond search and generation. Build AI agents that can reason, plan multi-step workflows, call external tools, and integrate with enterprise systems — all with human-in-the-loop approvals, guardrails, and security controls. This is where your AI system becomes an autonomous assistant that can actually get things done.
What You'll Build & Learn
- LangGraph for complex, stateful agent workflows
- Agent patterns: ReAct, Plan-and-Execute, multi-step reasoning
- Tool calling: APIs, databases, search, messaging integrations
- Human-in-the-loop approvals for critical actions
- Guardrails: rate limits, budgets, least-privilege security
- Memory layer: session and long-term context for multi-turn agents
- Knowledge Graph RAG with Neo4j for relationship queries
- MCP Server for standardized tool interfaces
- Detailed agent architecture handbook included
Tools & Technologies
MCP Server
Standardized protocol for secure LLM-to-tool communication
Session / Long-Term Memory
Persistent context across conversations for coherent multi-turn agent interactions
JWT / KeyCloak
Authentication and access control for multi-user agent systems
Tool Integrations
Connect to MS Teams, SharePoint, Google Drive, SAP, web search and more
By the end of Phase 3
Production-grade AI agents that plan, reason, and execute multi-step workflows
Secure tool integration with enterprise systems (ERP, CRM, DMS)
Human-in-the-loop control flows for critical operations
Agent observability — full traceability for debugging and compliance
Understanding of LLM strategy: when to use small vs. large models
Knowledge Graph RAG for complex, relationship-aware queries
One System, Built Layer by Layer
Each phase extends the last. By the end, you have a complete AI platform — or pick the layers you need.
Foundation
Parsing, vector search, local LLM, streaming
+ Production
Pipelines, hybrid search, caching, observability
+ Agents
Multi-step workflows, tool calling, memory, knowledge graphs
Fully On-Premises, Fully Yours
No vendor lock-in, no cloud dependency. Operate and extend independently.
Cloud-Ready When You Are
We guide you through migrating to AWS, GCP, or Azure — same patterns, production-grade.
Who This Training Is For
Engineering Teams
Build AI systems properly — production patterns, real infrastructure, the tools that matter.
Companies & Enterprises
Local AI infrastructure with full data control. No cloud dependency, no vendor lock-in.
Regulated Industries
Banking, government, healthcare — where data cannot leave your infrastructure.
Universities
Teach students how production AI actually works. Bridge theory and industry practice.
AI Practitioners
Move beyond demos to production architecture, monitoring, evaluation, and agents.
Conference Organizers
Half-day or full-day hands-on tutorials at conferences, meetups, and community events.
Proven in Real Classrooms
RAG Systems Workshop — HTW Berlin
Full-day hands-on workshop at HTW Berlin (University of Applied Sciences) — building production-ready RAG systems from scratch, covering vector databases, embeddings, retrieval strategies, and evaluation.
Ready to Build Real AI Systems?
Single-phase workshop, full program, or consultancy — let's talk about what fits your team.
contact.jamwithai@gmail.com