You will get AI Learning API: PDFs & Text to Gamified Courses and Quizzes
Project details
I built a production-ready AI microservice that transforms any PDF or text into structured micro-learning modules, quizzes, adaptive plans, and XP-based progress tracking — all via a clean REST API your LMS can call in minutes.
The engine runs on FastAPI + Claude AI (Anthropic), ships with Docker, full API docs, and a Postman collection. I dogfooded it into my own LMS (ExperioLearn) first, so every integration edge case is already solved.
Whether you're on Moodle, Canvas, a custom React app, or building from scratch — I'll have your learners generating courses from uploaded content the same week.
5 ready-to-call endpoints:
→ /ingest — upload a PDF or plain text (up to 20MB)
→ /generate — Claude AI produces micro-modules with summaries, concepts & tasks
→ /quiz — MCQ questions with explanations, per module
→ /plan — adaptive day-by-day schedule based on learner performance
→ /progress — XP tracking, level system, and milestone badges
This is your own deployed microservice — you own the code, the API key, and the data. Your users never leave your platform. No black-box SaaS, no vendor lock-in.
The engine runs on FastAPI + Claude AI (Anthropic), ships with Docker, full API docs, and a Postman collection. I dogfooded it into my own LMS (ExperioLearn) first, so every integration edge case is already solved.
Whether you're on Moodle, Canvas, a custom React app, or building from scratch — I'll have your learners generating courses from uploaded content the same week.
5 ready-to-call endpoints:
→ /ingest — upload a PDF or plain text (up to 20MB)
→ /generate — Claude AI produces micro-modules with summaries, concepts & tasks
→ /quiz — MCQ questions with explanations, per module
→ /plan — adaptive day-by-day schedule based on learner performance
→ /progress — XP tracking, level system, and milestone badges
This is your own deployed microservice — you own the code, the API key, and the data. Your users never leave your platform. No black-box SaaS, no vendor lock-in.
AI Development Type
Deep Learning, Model Tuning, Recommendation System, Software MaintenanceAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$950
|
Advanced
$2,200
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 1 | 1 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$75Frequently asked questions
About Fred
AI Systems Engineer | LLM Evaluation (RLHF) | Full-Stack Engineer
Nairobi, Kenya - 9:09 am local time
Most engineers stay on infrastructure.
Most annotators stay on task execution.
I’ve worked across both.
That means I don’t just design pipelines or deploy models in isolation—I understand how data is labeled, where quality breaks, and what actually improves model performance.
Result: systems built for real-world performance—not theory.
🛠 Software Engineering
I design and deploy full-stack systems built for real operations—not demos.
WeDemo Africa — AI + Human Talent Ecosystem
A production ecosystem I built to move users through:
Mindset → Skills → Monetization
This is a multi-layered operational system, not a content platform.
Core system design:
User progression: onboarding → learning → execution → earning
Hybrid AI + human workflows (mentorship, assessments, guidance)
Integrated platforms: learning, work, referrals, leadership
Gated progression: certification, assessments, controlled access
Operational layer:
Structured training pipelines
Dashboard-driven user tracking
Real-world outcome focus (not just completion)
✔ Live system handling real users and workflows
Mini Gigs Hub — Team Operations Dashboard
A production system for managing distributed teams:
Role-based access control
AM/PM shift reporting
Automated email alerts (cron jobs)
Admin analytics & performance tracking
Infrastructure & Systems
RustDesk relay & ID servers (VPS deployment)
Secure remote desktop environments
Browser isolation + proxy/session workflows
Core Stack
Next.js · React · TypeScript
PostgreSQL · Prisma · REST APIs
Vercel · Docker · VPS
Clerk · Role-based access
Cron jobs · Automation
JSON · CSV · JSONL pipelines
🤖 AI Systems Engineering
I design the operational layer between AI systems and the people training them.
That includes:
Annotation pipelines (RLHF, ranking, classification, etc.)
Task design + annotation guidelines
QA systems for consistent output at scale
I also build AI-powered APIs:
LLM integrations
Annotation workflow endpoints
Prediction-serving APIs
✔ Production-ready, documented, and integration-ready from day one
Training Systems
I’ve built a complete AI annotation training program:
5 structured modules
Covers text, image, audio, video + RLHF
Includes ethics, bias, and PII handling
Comes with:
Lesson scripts
Slides
Exercises
Assessments
✔ Ready for teams of 3–50
🧠 AI Evaluation
I’ve worked directly on:
Remotasks
Handshake
Mercor
So I understand:
How tasks are designed
Where they break in practice
What actually produces high-quality data
What I evaluate:
RLHF preference ranking
Prompt/response quality
Instruction-following accuracy
Hallucinations & factuality
Safety, bias, compliance
I also train evaluators in judgment—not just mechanics.
🎯 Who I Work With
AI companies building or refining LLM systems
Teams setting up annotation / RLHF pipelines
Organizations deploying training + workforce systems
If you need someone who understands:
systems + data + people + execution
—that’s where I come in.
🚀 Let’s Work
Send me a message with what you're building.
I’ll respond with a clear, technical breakdown of how I’d approach it—usually within a few hours.
Steps for completing your project
After purchasing the project, send requirements so Fred can start the project.
Delivery time starts when Fred receives requirements from you.
Fred works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & requirements review
Review your LMS stack, content types, and auth setup. Confirm scope and agree on integration approach before writing any code.
Deploy & configure the AI engine
Spin up the FastAPI microservice on your chosen host, configure environment variables, and verify all 5 endpoints via the health check.