Founder Free Access by Arkion Labs
Master AI engineering by building a real AI SaaS product.
Ledeqor helps developers learn concepts, build Arkion DocIntel, and turn the project into credible proof of skill.
14
Modules
236+
Lectures
Projects
Each course
8 wk
Roadmap
Project-based courses
Learn by building complete AI applications
Private workspace model
Course-specific content, learner progress, practice history, and future tracks.
Real system design story
Every course ends with a serious project and a clear product narrative.
Why this exists
Most AI tutorials stop where real engineering begins.
You learn prompts, build a chatbot, and still cannot show architecture, retrieval, evaluation, security, cost, or deployment depth. This platform is built around product proof.
Solution
Learn AI engineering the way products are actually built.
Ledeqor combines structured learning with course-specific projects, practice, and product outcomes. Each course is designed for a different level of experience and a different product build.
LLM API engineering
Use model APIs with prompts, structured outputs, validation, retries, streaming, and cost tracking.
Document intelligence
Parse PDFs, understand scanned document limits, classify documents, and extract business fields.
Vector search and RAG
Build embeddings, semantic search, grounded Q&A, citations, and unknown-answer handling.
AI workflows
Design controlled agents for contract review, invoice summaries, comparisons, and checklists.
Evaluation and observability
Measure extraction accuracy, retrieval quality, citation correctness, latency, and token cost.
Security and deployment
Handle private documents with tenant isolation, RBAC, safe logging, Docker, queues, and production readiness.
Capstone
Every course comes with a real project, not just lessons.
The flagship AI Engineer Guide uses a document intelligence SaaS project. Future courses can use different builds and skill levels while sharing the same learning platform.
Beginner-to-advanced tracks
Course-specific capstones
Practice and lab loops
Progress saved per course
Guided project checklists
Launch-ready project assets
Roadmap
A complete path from full-stack developer to applied AI engineer.
Foundation
Product Direction
Python for AI Engineering
AI / ML / LLM Foundations
Document Intelligence
Document AI Fundamentals
LLM API Engineering
Structured Extraction
Retrieval and Architecture
Embeddings and Vector Search
RAG for Business Documents
Full Stack AI SaaS Architecture
Production Readiness
Agentic Workflows
Evaluation and Observability
Security and Privacy
Deployment and LLMOps
Project Launch
Capstone Build
Demo Readiness
Documentation and Polish
Available now
Start with the AI Engineer Guide. More tracks can plug into the same platform.
Build the AI portfolio project your profile is missing.
Learn the concepts, build the product, explain the architecture, and publish a project you can stand behind.