RODIN SYSTEM Discuss a project

Python Development · AI Automation · LLM Integration

Practical AI automation for real business workflows.

I build focused Python and AI solutions to extract data from business documents, automate repetitive tasks, and make internal knowledge easier to access.

  • Clear scope
  • Tested workflows
  • Documented delivery
Discuss a project View my work
Incoming RFQExample request

“Please quote 12 units of VAN-BR-050.”

Structured resultValidated
Product reference
VAN-BR-050
Quantity
12 pieces
Catalog match
EXACT_MATCH
Decision
READY_FOR_QUOTE
Unresolved or ambiguous cases Human review
PythonFastAPIOpenAI APIRAGVector Searchpytest

Services

Focused solutions for documents and workflows.

01

AI Document Processing

Turn unstructured documents into usable business data.

Extract, organize, and validate information from PDFs, emails, and business documents using AI and explicit business rules.

Common use cases
Quotation requests, purchase orders, document classification, structured data extraction, and exception handling.
Typical deliverable
A Python processing pipeline with structured JSON/CSV outputs, validation checks, and human-review handling for uncertain cases.
02

Workflow Automation & AI Integration

Reduce repetitive tasks with focused automation.

Combine Python, language models, and existing APIs to streamline specific business processes without unnecessary complexity.

Common use cases
Request classification, task routing, batch processing, LLM integrations, and lightweight backend services.
Typical deliverable
A tested Python automation or FastAPI service with documented inputs, outputs, and error handling.
03

RAG & Knowledge Search

Find answers within your own documentation.

Build search and question-answering tools that help teams retrieve relevant information from internal documents, with references to the source material.

Common use cases
Technical documentation, internal knowledge bases, semantic document search, and document-grounded Q&A.
Typical deliverable
A scoped RAG application or backend API with semantic retrieval, source references, and retrieval evaluation.

Featured project

RFQ Intelligence

AI-assisted processing of industrial quotation requests.

An independent portfolio project demonstrating how incoming quotation requests can be converted into structured, validated information.

The system extracts requested items, matches product references against an industrial catalog, applies deterministic business rules, and routes unresolved cases for human review.

Valid requests can be marked as ready for quotation, while uncertain cases remain subject to manual verification.

Stack: Python · FastAPI · OpenAI API · pytest

Explore the project on GitHub →
  1. 01
    Extract
    Parse the request into structured fields.
  2. 02
    Match
    Check product references against the catalog.
  3. 03
    Validate
    Apply explicit business and pricing rules.
  4. 04
    Route
    Each request leaves with one explicit decision.
    READY_FOR_QUOTEREVIEW_REQUIRED

A practical approach

Start small. Validate. Improve.

Every engagement begins with a concrete problem, representative examples, and an agreed outcome.

  1. 01

    Define

    Understand the existing workflow and agree on a realistic scope.

  2. 02

    Build

    Develop a focused solution using the appropriate tools.

  3. 03

    Validate

    Test against agreed scenarios, review exceptions, and document limitations.

  4. 04

    Deliver

    Provide the source code, tests, documentation, and setup instructions.

Small, clearly scoped prototypes and proof-of-concept projects are welcome.

About

An independent practice for Python and AI.

Rodin System is my independent software development practice, focused on Python, AI integration, and business process automation.

My background in entrepreneurship and business operations helps me approach software development with a practical understanding of how companies work.

I focus on clearly defined problems, straightforward architecture, and solutions that can be tested, understood, and maintained.

Focus
Python · FastAPI · OpenAI API · RAG · Vector Search · Automated Testing
Engagements
Proofs of concept, focused automations, and tested backend components.
Handover
Source code, tests, documentation, and setup instructions.

Contact

Let’s solve a concrete problem.

Whether you want to simplify a manual process, explore an AI use case, or build a Python backend component, tell me what you’re trying to achieve.

We’ll start by defining a realistic scope and a clear deliverable.

Discuss your project → hello@rodinsystem.com