Flagship Project~22 hrs

Build the Full Production AI System

From data pipeline to serving API — the project that connects everything.

6 parts. One production AI platform. Ingestion → retrieval → LLM orchestration with routing → serving → evaluation with release gates → reliability with cost controls. The system senior engineers are expected to explain.

~22 hours·6 Parts·14+ Staff Concepts

Part 1 is free — start building in 10 minutes

nexus / ai-platform

Control Plane: Routing / Eval Gate / Cost / Versioning / Feedback / Fallback

INGEST

Docs

Data

Clean

Chunk

→RETRIEVE

Embed

Hybrid

Rerank

Cite

→GENERATE

Route

Prompt

Version

Tools

→SERVE

Stream

Fallback

Auth

Multi-T

→EVAL

Offline

Online

Feedback

Gate

→OPERATE

Cost

Route

Incident

SLA

fig 1 — full production ai platform with control plane

PARTS

Full Stack

DURATION

~22

Hours Total

STAFF

14+

Concepts

CONTROL

Plane Features

What You'll Build

Not a chatbot demo — a production AI platform with routing, eval gates, feedback loops, and cost controls.

Data Ingestion Pipeline

Docs + structured data → parsed → cleaned → chunked → indexed with metadata audit trail

Hybrid Retrieval + Reranking

Semantic + BM25 with reranking, citation-ready context, and retrieval quality API

Intelligent Query Router

Factual → RAG, analytical → SQL tool, open → LLM-only. Prompt versioning with A/B experiments

Eval Gates + Cost Guardrails

Release gates that block bad deploys, feedback loops, model routing, and cost budget enforcement

Curriculum

6 parts. Build the full system, then own it in production.

Data Foundation & Ingestion

~2.5 hrs

Define the system contract, ingest documents and structured data, normalize content, build the processing pipeline, and produce indexing-ready outputs.

Ingestion pipeline with chunked corpus + metadata audit trail

Retrieval System & Knowledge Layer

Pro~3 hrs

Build hybrid retrieval with semantic + keyword search, implement reranking, assemble citation-ready context, and ship a retrieval API with quality metrics.

Hybrid retriever with reranking serving from API

LLM Orchestration & Intelligent Routing

Pro~3.5 hrs

Build prompt architecture, intelligent query routing (factual/analytical/open), prompt versioning with A/B experiments, and agentic tool extensions.

Orchestration with query router + prompt registry

Serving Layer & Production API

Pro~3 hrs

Ship a production API with streaming, failure mode routing (retrieval fails → LLM fallback → cached answer), multi-tenant design, and frontend integration.

Production API with streaming + failure fallbacks

You built a working AI system. Now make it production-grade.

Data pipeline

Retrieval API

LLM orchestration

Serving layer

Most engineers stop here. But production AI systems fail because:

No evaluation before deploy

No feedback loop to improve

Costs explode without guardrails

No incident response plan

→ The difference between "I built an AI system" and "I designed a production AI platform with routing, eval gates, and cost controls."

Expert tier unlocks

Eval pipeline + release gates

User feedback → improvement loop

Cost budget guardrails

Model routing (cheap/expensive)

Incident simulation (6 types)

Staff capstone + interview narrative

Evaluation, Feedback & Release Gates

Expert~4 hrs

Build offline + online evaluation, user feedback loops that improve the system, and eval-based release gates — CI/CD for AI that blocks bad deployments.

Eval pipeline + feedback loop + automated release gate

Unlock to continue

Reliability, Cost & Staff Capstone

Expert~4 hrs

Add cost budget guardrails, model routing (cheap/expensive/special), incident simulation, and a staff-level capstone: full architecture review + interview narrative.

Staff-level platform with cost guardrails + design doc

Unlock to continue

Control Plane (Staff Layer)

The layer that separates "I built features" from "I designed operating constraints and tradeoffs."

Query routing (factual/analytical/open)Part 3

Prompt versioning + A/B registryPart 3

Failure mode routing (cascade fallback)Part 4

User feedback → eval dataset loopPart 5

Eval → release gate (CI/CD for AI)Part 5

Cost budget guardrail (auto-downgrade)Part 6

Model routing (cheap/expensive/special)Part 6

Environment Setup

Spin up the full platform stack locally.

nexus-ai-platform

# Clone the project & launch the full AI stack
$ git clone https://github.com/aide-hub/nexus-ai-platform.git
$ cd nexus-ai-platform

# Start PostgreSQL + pgvector + Redis + Prometheus + Grafana
$ docker-compose -f docker-compose.platform.yml up -d

# Initialize the knowledge copilot system
$ python -m nexus init \
$   --sources docs,tickets,accounts \
$   --vector-store pgvector \
$   --eval-mode offline

Tech Stack

PythonFastAPIpgvectorClaude APIdbtAirflowRedisPrometheusGrafanaDockerPostgreSQL

Prerequisites

Python proficiency (async, classes, data structures)
SQL basics (queries, joins, aggregations)
REST API concepts (HTTP lifecycle, JSON)
Docker basics (containers, docker-compose)

This Project Connects

AI Data Engineer Path AI Platform Engineer Path RAG Skill Toolkit LLM Evaluation Toolkit

What is This Project?

A full-stack AI platform combines data engineering, retrieval systems, and LLM orchestration into a unified production system. This project builds the complete stack from data ingestion and transformation with dbt and Airflow, through vector storage with pgvector, to a Claude-powered AI layer with RAG retrieval, caching with Redis, and production observability with Prometheus and Grafana -- demonstrating how data engineering and AI engineering converge.

How This System Works

Build the data foundation with ingestion pipelines, dbt transformations, and Airflow orchestration

Set up pgvector for vector storage and implement semantic search with embedding pipelines

Orchestrate LLM interactions with Claude API, prompt management, and caching strategies

Add production infrastructure with Redis caching, rate limiting, and error handling

Deploy with Prometheus/Grafana observability, cost tracking, and performance monitoring

Why This Matters in Production

The boundary between data engineering and AI engineering is disappearing. Companies like Notion, Figma, and Linear are building AI features on top of their data platforms. The engineers who understand both the data infrastructure (ingestion, transformation, quality) and the AI layer (retrieval, LLM orchestration, safety) are the most valuable hires in the industry.

Real-World Use Cases

Teams building AI-powered features on top of existing data infrastructure
Full-stack engineers connecting data pipelines to LLM-powered applications
Startups building MVPs that combine data analytics with AI capabilities
Data engineers transitioning into AI engineering roles

What You Gain

A portfolio-ready platform demonstrating both data engineering and AI engineering skills

Hands-on experience with the full stack: ingestion, transformation, retrieval, and LLM orchestration

Production patterns for caching, rate limiting, cost tracking, and observability

Interview-ready knowledge of how modern companies combine data and AI infrastructure

Working Prometheus/Grafana dashboards with AI and data pipeline metrics

Tools & Skills Used

RAG dbt Airflow Vector Databases

Related Projects

Enterprise RAG →AI Retrieval Platform →Agentic Data Pipeline →

Frequently Asked Questions

How do I build a full-stack AI platform step by step?⌄

Start with data ingestion and dbt transformations, add Airflow orchestration, set up pgvector for retrieval, integrate Claude API for LLM capabilities, and deploy with caching, monitoring, and cost tracking.

What tools are used in a full-stack AI platform?⌄

This project uses Python, FastAPI, pgvector, Claude API, dbt, Airflow, Redis, Prometheus, and Grafana. It combines data engineering tools with AI infrastructure for a complete platform.

Is this full-stack AI project good for interviews?⌄

Yes. This project uniquely demonstrates both data and AI engineering skills. It shows you can build the entire pipeline from data ingestion to LLM-powered features, which is exactly what companies hiring AI engineers want to see.

What is a full-stack AI platform?⌄

A full-stack AI platform is a system that combines data engineering (ingestion, transformation, quality) with AI engineering (retrieval, LLM orchestration, safety) into a unified production system. It represents the convergence of these two disciplines.

How long does it take to build a full-stack AI platform?⌄

This project takes 18-22 hours across 5 parts covering data foundations, vector storage, LLM orchestration, production infrastructure, and observability deployment.

Ready to build the full system?

Part 1 is free — start with Data Foundation & Ingestion (~2.5 hrs)