You will get a Python AI Microservice With FastAPI and Structured Outputs
Project details
You need AI to process inputs and return reliable structured data — not free-form text your application has to parse and hope for the best. Classification results. Extracted entities. Scored outputs. Structured summaries. Data your code can actually use without writing regex to fix AI formatting.
I build Python AI microservices using FastAPI and Pydantic that call LLMs internally and return type-safe validated JSON. The AI cannot return malformed data — Pydantic enforces the output schema. Your application receives exactly the structure it expects, every time.
I have built this pattern across multiple production projects including my AI Career Mentor, which uses structured Pydantic outputs to generate typed roadmap data from Gemini API responses. This is not a new concept for me — it is how I build everything.
What you get:
FastAPI application with clean documented REST endpoints
Pydantic output schemas enforcing structured AI responses
Input validation rejecting bad requests before they reach the LLM
Error handling and retry logic for LLM failures
Docker container ready to deploy anywhere
Full GitHub repo with README
I build Python AI microservices using FastAPI and Pydantic that call LLMs internally and return type-safe validated JSON. The AI cannot return malformed data — Pydantic enforces the output schema. Your application receives exactly the structure it expects, every time.
I have built this pattern across multiple production projects including my AI Career Mentor, which uses structured Pydantic outputs to generate typed roadmap data from Gemini API responses. This is not a new concept for me — it is how I build everything.
What you get:
FastAPI application with clean documented REST endpoints
Pydantic output schemas enforcing structured AI responses
Input validation rejecting bad requests before they reach the LLM
Error handling and retry logic for LLM failures
Docker container ready to deploy anywhere
Full GitHub repo with README
AI Algorithms
Feedforward Neural Network, Large Language Model, Transformer ModelAI Applications
AI-Generated Code, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Gradio, Hugging Face, StreamlitAI Models
ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$149
|
Standard
$349
|
Advanced
$649
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 8 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Faizan
AI Automation Engineer | Claude, OpenClaw, LangGraph & n8n Developer
Saddiqabad, Pakistan - 1:40 pm local time
✅ 1 live deployed multi-agent system (with a real URL — check it)
✅ 40% reduction in manual outreach effort for a real client
✅ Co-authored published research on multi-agent reinforcement learning (MAPPO/CTDE)
Most freelancers automate individual tasks. I build end-to-end agent systems that run your operations — including OpenClaw deployments, the open-source self-hosted AI agent framework that became the most starred project on GitHub in 2026.
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WHAT I BUILD
▶ Multi-Agent Pipelines (LangGraph · CrewAI · OpenClaw)
Stateful, production-grade agent systems with persistent memory, tool-calling, MCP integration, and human-in-the-loop controls. Not demos — systems that run in production across Telegram, WhatsApp, and Slack. Use cases: autonomous lead qualification, AI operations routing, research and reporting agents.
▶ AI Workflow Automation (n8n)
End-to-end n8n pipelines connecting your CRM, email, forms, Notion, Airtable, and APIs. Lead capture → AI scoring → CRM update → follow-up, running 24/7. Reduced manual operational overhead by 40% across live client deployments.
▶ Agentic RAG Systems
Knowledge systems built on your actual data — documents, website, internal wikis — using Qdrant, Pinecone, and LangGraph retrieval agents. Accurate, sourced answers 24/7. Not hallucinations. Your data, retrieved correctly.
▶ AI Lead Generation Engines
3 years of lead generation experience combined with production AI engineering. Systems that discover, verify, score, personalise, and outreach — automatically. LeadForge (live: leadforge-bice.vercel.app) is the proof.
▶ Full-Stack AI SaaS MVPs
FastAPI backend · Next.js frontend · vector DB · JWT auth · CI/CD. Production-ready AI products built to ship to users or show investors in weeks, not quarters.
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SYSTEMS SHIPPED
→ LeadForge — Autonomous Multi-Agent B2B Lead Engine
Llama 3 · Playwright · Hunterio · SMTP drip · LangGraph
Live at leadforge-bice.vercel.app — from lead discovery to Day-7 follow-up, zero manual input.
→ Agentic RAG Knowledge System
LangChain · FAISS · FastAPI · React · GitHub Actions CI/CD
Full-stack document Q&A deployed in under a week.
→ AI Career Mentor — SaaS MVP
FastAPI · PostgreSQL · Gemini API · Pydantic structured outputs · JWT auth
Resume analysis → personalised 12–24 week career roadmap. Structured AI outputs, not free-form hallucinations.
→ Lead Management Automation
n8n · Gemini AI · Docker
AI-driven lead scoring and follow-up. 40% less manual outreach for a real client.
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TECH STACK
AI Agents: LangGraph · CrewAI · LangChain · OpenClaw · OpenAI Agents SDK
LLMs: GPT-4o · Claude Opus/Sonnet 4.5 · Gemini 1.5 Pro · Groq · Llama 3
RAG: Qdrant · Pinecone · FAISS · Sentence Transformers · Agentic Retrieval
Automation: n8n · Python · Playwright · REST APIs · Webhooks · asyncio
Backend: FastAPI · PostgreSQL · Supabase · JWT Auth · Pydantic
Frontend: Next.js · React · TypeScript · Tailwind CSS
DevOps: Docker · GitHub Actions · CI/CD
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HOW I WORK
✔ Scope agreed with clear milestones before any code is written
✔ Clean, documented code — you own it completely
✔ Daily async updates on active projects
✔ No surprise scope creep
✔ Systems built for production, not demos that break under real load
If you have a manual bottleneck you want eliminated, message me and describe it in one sentence. I'll tell you directly whether I can automate it, how long it takes, and what it costs.
Steps for completing your project
After purchasing the project, send requirements so Faizan can start the project.
Delivery time starts when Faizan receives requirements from you.
Faizan works on your project following the steps below.
Revisions may occur after the delivery date.
Schema Design and Approval
I design the Pydantic output schema based on your requirements and share it for your approval before writing a single line of endpoint code.
Build, Test and Deploy
Description: I build the FastAPI service, write prompt and retry logic, test every endpoint against real inputs, containerise with Docker, and deploy to your target environment.