Research-trained, production-ready
Programs built for
real systems.
Enterprise AI enablement for business teams, technical workshops from LLMs through graph ML, backend engineering with Java, and curriculum strategy, grounded in fintech deployment and university teaching.
What we offer
Enterprise AI enablement
ChatGPT, Claude, and Gemini taught in your business context, not tool tours. Hands-on cohort workshops built around participants’ own recurring tasks, gathered through a pre-workshop intake, with clear guidance on which tool to route each task to and how to use AI safely with company data. An AI Champions track prepares internal advocates to sustain adoption after we leave.
Who it’s for
Business teams across functions: sales, finance, HR, product, operations.
You leave with
A working custom assistant built on your own material, a tool-routing cheat sheet, a failure-diagnosis card, and a prompt and template library.
Technical AI & ML workshops
LLM internals, RAG, evaluation, and safe use in regulated contexts; Python-first ML, NLP, and graph neural network labs; SQL and data pipelines where teams need them. Practical guardrails for models in sensitive domains, grounded in what we have shipped in AML and fintech.
Who it’s for
Engineers, data scientists, and analysts who build with these systems.
You leave with
Working lab code, evaluation checklists, and patterns you can apply to production systems.
Backend engineering with Java
Production Java and Spring Boot taught by an engineer who ships it daily: microservices from domain modeling to deployment, reactive (WebFlux) services, REST API design and third-party integration, and CI/CD with Docker. Grounded in fintech and enterprise systems.
Who it’s for
Backend developers and teams building or modernizing Java services.
You leave with
A working service built during the labs, plus patterns for testing, integration, and deployment.
Strategy & curriculum design
Roadmaps for leadership and L&D: what to teach first, how to sequence cohorts, how to measure skill gain, and how to align training with your compliance and data-governance posture.
Who it’s for
Leadership, talent, and learning teams planning an AI upskilling program.
You leave with
A sequenced training plan matched to your tools, teams, and constraints.
Delivery formats: on site across Saudi Arabia and the Gulf, or remote; single sessions or multi-week cohorts. Agendas reference your stack and governance constraints, not a generic deck.
Inside a workshop
The Enterprise AI enablement track in detail, delivered for ChatGPT, Claude or Gemini. Nothing here is a template session: the exercises do not exist until your participants tell us what they are struggling with.
Your tasks, not our examples
Every participant completes a questionnaire: their role, how they use AI today, the tasks they most want to accelerate, and—the question that does the most work—one task they already tried with AI that did not go well. Each brings one sanitized document from their own work, and a dataset where it is relevant. Every exercise in the room is then built from those answers.
Judgment, not a feature tour
How the model actually works and why fluent output tells you nothing about accuracy. A routing framework for choosing between tools on task, data sensitivity, currency, accuracy stakes and approved environment. Data security worked as a live redaction exercise against your own AI policy, agreed before delivery. Then each participant builds a reusable assistant on their own reference material.
Assets, not a slide deck
A working custom assistant, already loaded with their material. A tool-routing cheat sheet. A diagnostic card for working out why an AI attempt failed, which they use again in the session. A prompt library and template pack. Every module carries a priority rating, so a shortened agenda loses discussion time rather than anything people take away.
Scoped with you: cohort size, session length, the balance of teaching to hands-on time, and which modules make the cut are all agreed during design — against your tools, your AI policy, and the tasks your teams actually submit. Nothing is fixed before we have seen them.
How we work together
Scoped to engineers, analysts, or executives.
Goals & audience
Who attends, current tooling, and what “good” looks like after training.
Agenda & depth
Mix of concepts, live demos, and exercises, adjusted for time zone and skill level.
Workshops
Interactive sessions with space for Q&A tied to your domain (e.g. risk, product, data).
Optional support
Office hours or short async reviews to reinforce adoption.
Book a conversation
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contact@active-neuron.com
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