An interactive map of the technologies, frameworks, and trust signals that power production GenAI work — anchored by hands-on certification training from Euron (euron.one).
A focused, hands-on program from Euron — covering production ML systems, transformer architectures, retrieval-augmented generation, agentic workflows, LLMOps, and applied research in modern LLMs.
Production-grade retrieval with sub-second p95 and >0.9 recall@10.
Tuned for indexing speed, chunking strategy, and query latency at scale.
Durable, stateful orchestration with human-in-the-loop guardrails.
Self-hosted, fine-tuned, optimized for cost and data-residency.
Bridge agents to real SaaS — CRMs, Stripe, WooCommerce, internal APIs.
5+ years of shipping multi-tenant SaaS to enterprise customers.
A live graph of the toolbelt — Python & FastAPI under the hood, LangGraph agents & AutoGen / CrewAI orchestrations on top, AWS shipping it all. Hover any node to trace its connections.
Scroll through the architecture canvas to follow the data path that powers Forgestackk — from React UI through hybrid retrieval to a self-hosted LLM. Then read the two engineering doctrines that drive every system I ship.
Streaming UI with optimistic state, edge-rendered shells, and server actions piping tokens directly into the user's view.
→ Agentic + RAG platform — production GenAI MVP.
An end-to-end Generative AI platform combining advanced RAG (hybrid retrieval, RRF, cross-encoder rerank) with LangGraph-driven agentic workflows. Self-hosted LLaMA 3 via vLLM, multi-tenant Java API, streaming Next.js UI.
→ Shein · Google Shopping · Mirakl — at scale.
Production integrations with flaky third-party marketplaces. Idempotent webhook ingestion, schema-versioned mapping layer, retry windows with DLQ, rate-limit aware schedulers. The substrate every agentic AI tool eventually needs.
→ Multi-tenant plugins, checkout flows, billing hooks.
Multi-tenant WooCommerce SaaS — custom plugins, checkout extensions, billing pipelines, per-tenant configuration, audit-logged background workers. Translation: I already know how to scope GenAI per-customer, securely.
Most GenAI applications fail at scale because they rely on naive 'retrieve-and-generate' defaults. I engineer Advanced RAG pipelines designed for enterprise precision and low-latency data access.
Moving beyond simple cosine similarity. I implement parallel hybrid search (Dense embeddings + BM25) fused with Reciprocal Rank Fusion (RRF), followed by cross-encoder reranking to ensure high-fidelity context windows.
Implementing advanced routing, HyDE (Hypothetical Document Embeddings), and step-back prompting to close the vocabulary gap between user intent and unstructured document text.
Proficient in deploying and tuning vector databases like Weaviate, Qdrant, and Pinecone, balancing indexing speed, chunking strategies (semantic vs. token-based), and query latency for production loads.
True AI value isn't in stateless chatbots; it's in autonomous systems that can plan, execute, and course-correct. I leverage my deep background in SaaS architecture to build reliable Agentic AI frameworks that bridge the gap between LLM reasoning and real-world execution.
Grounding intelligence in reality. I build AI agents equipped with Model Context Protocol (MCP) integrations, enabling them to securely execute API calls, query SQL/NoSQL databases, and interact with complex SaaS ecosystems (WooCommerce, CRMs, Stripe).
Utilizing frameworks like LangGraph to map agent behavior as a state machine. I manage durable execution, short/long-term memory persistence, and human-in-the-loop guardrails for mission-critical workflows.
Implementing ReAct (Reasoning + Acting), self-reflection loops, and Evaluator-Optimizer topologies to drastically reduce hallucination rates and increase autonomous task success.
Most AI engineers can write a prompt. Far fewer can wire it into a multi-tenant, audit-logged, webhook-driven, billable enterprise system. This timeline reframes my SaaS years as the engineering backbone for production-grade AI.
Most AI engineers ship demos.
I ship systems.
Five years of webhooks, multi-tenant SaaS, marketplace integrations, and enterprise deployments — converted into the engineering backbone of every AI system I now build.