# Anass Essadikine > Anass Essadikine is a Data & AI Engineer and Software Engineer based in Casablanca, Morocco, specialised in Power BI dashboards, data pipelines, RAG assistants and AI automation (n8n), and Next.js / Spring Boot applications. Master's degree in Data Science & Big Data (Université Hassan II, Casablanca, 2026). Languages: Arabic (native), French (fluent), English (professional). Open to full-time roles, freelance missions and remote work. ## Key pages - [About](https://essadikine.com/about): background, skills and stack - [Resume](https://essadikine.com/resume): experience, education, skills - [Portfolio](https://essadikine.com/portfolio): projects as case studies - [Services](https://essadikine.com/services): what I offer and how I work - [Certifications](https://essadikine.com/certifications): verified credentials - [Contact](https://essadikine.com/contact): email, phone, booking - French version: https://essadikine.com/fr ## Services - [Power BI & Looker Studio Dashboards](https://essadikine.com/services/power-bi-dashboards): Power BI and Looker Studio dashboards built on a clean data model: KPIs, filters and automatic refresh. Casablanca, Morocco and remote. - [Data Engineering & ETL Pipelines](https://essadikine.com/services/data-engineering): ETL/ELT pipelines, data warehouses and data-quality checks in Python and SQL, so your reports always run on clean, up-to-date data. - [AI Automation & RAG Assistants](https://essadikine.com/services/ai-automation-rag): n8n automations with LLMs and RAG assistants on your own documents: emails, lead follow-up, document processing and internal Q&A. - [Web Development with Next.js, React & Spring Boot](https://essadikine.com/services/web-development-nextjs): Fast, SEO-ready websites and web applications with Next.js, React, Node.js or Spring Boot — from landing pages to dashboards and APIs. - [Technical SEO, GEO & AEO](https://essadikine.com/services/technical-seo): Technical SEO audit and fixes: indexing, metadata, structured data, Core Web Vitals, hreflang and AI-crawler visibility — implemented, not just reported. ## Featured projects - [D&A Studio — Data & AI Platform (PaaS)](https://essadikine.com/portfolio/dna-studio-data-ai-platform): A platform that brings geospatial tracking data, an AI assistant on company documents, anomaly detection and BI dashboards into one place. - [Decision-Support System for Social Security Data (CNSS)](https://essadikine.com/portfolio/cnss-decision-support-system-bi): A star-schema data warehouse fed by ETL flows, with analytics dashboards for decision-makers. - [AI Prospecting Automation with n8n](https://essadikine.com/portfolio/ai-prospecting-automation-n8n): An end-to-end workflow that reads documents with OCR, writes personalised emails with an LLM, sends them through Gmail and tracks everything in Google Sheets. - [RecruSmart — AI Recruitment Platform](https://essadikine.com/portfolio/recrusmart-ai-recruitment-platform): A microservices platform that matches CVs to job offers with machine learning and processes applications asynchronously. - [University Housing Management (FSBM)](https://essadikine.com/portfolio/university-housing-management-fsbm): A secure platform for managing student housing requests, allocations and payments end-to-end. - [Cloud Deployment with Automated CI/CD](https://essadikine.com/portfolio/cloud-deployment-automated-cicd): An end-to-end pipeline that builds, containerises and deploys a React app to Kubernetes on AWS on every push. ## Articles - [How to Build a RAG Assistant on Your Company's Documents](https://essadikine.com/blog/build-rag-assistant-company-documents): A RAG assistant splits your documents into chunks, turns each chunk into an embedding vector, stores the vectors in a database such as PostgreSQL with pgvector, retrieves the most similar chunks for each question, and asks an LLM to answer only from those chunks — with sources. Quality depends mostly on chunking, retrieval and the prompt, not on the model. - [Automate Prospecting with n8n, an LLM and Gmail](https://essadikine.com/blog/automate-prospecting-n8n-llm-gmail): An n8n prospecting workflow reads contact information (from documents with OCR or from a sheet), asks an LLM to return structured data and a personalised email, sends it with the Gmail node, then writes the contact, message and status to Google Sheets. Add a daily limit, a review step and error handling before sending at scale. - [Power BI Star Schema: From Raw Data to Reliable KPIs](https://essadikine.com/blog/power-bi-star-schema-raw-data-to-kpis): A star schema puts the numbers you measure (sales, payments, events) in one fact table and the things you filter by (date, customer, product, region) in dimension tables linked by one-to-many relationships. In Power BI it makes reports faster, DAX measures simpler and KPIs consistent across every page. - [Data Quality and Anomaly Detection in Practice](https://essadikine.com/blog/data-quality-anomaly-detection): Good data quality comes from layers: rule-based checks (nulls, duplicates, ranges, referential integrity) run on every load, simple statistical detection (z-scores, rolling medians) flags unusual values, and machine-learning models such as Isolation Forest handle complex patterns. Each failed check should block or flag the data and alert an owner. - [CI/CD to Kubernetes (K3s) on AWS with Terraform, Ansible and Jenkins](https://essadikine.com/blog/cicd-kubernetes-k3s-aws-terraform-jenkins): Terraform creates the AWS servers and network, Ansible installs Docker and the lightweight Kubernetes distribution K3s, and a Jenkins pipeline builds a Docker image on every push, pushes it to a registry and updates the Kubernetes deployment. Everything lives in Git, so the whole platform can be recreated and every release is repeatable. - [SEO, GEO and AEO for a Next.js Website in 2026](https://essadikine.com/blog/seo-geo-aeo-nextjs-2026): SEO makes pages crawlable, fast and relevant for Google; GEO (generative engine optimization) makes your content easy for ChatGPT, Perplexity or Gemini to understand and cite; AEO (answer engine optimization) structures it as short, direct answers. In Next.js: render pages statically, use real language URLs with hreflang, add JSON-LD, publish llms.txt and let AI crawlers in. ## Contact - Email: contact@essadikine.com - Phone / WhatsApp: +212 648 53 73 20 - Location: Casablanca, Morocco - GitHub: https://github.com/ESSADIKINE - LinkedIn: https://www.linkedin.com/in/anass-essadikine/ ## Optional - [Full text for LLMs](https://essadikine.com/llms-full.txt): every project, service and article in one file