Intelligent Lift Configuration/Pricing/Quote Agent Development
Budget: ₹1,500 – ₹12,500 INR
We’re building an intelligent Lift Configuration & Quotation (CPQ) agent using LangGraph and OWL ontology. The goal is to improve the reasoning capabilities of LLMs by integrating structured knowledge from a Protégé-modeled ontology (components, features, tags, constraints, etc.).
Our current system supports YAML-based business rules. Now we want to measure and expand reasoning using ontology via:
owlready2 reasoning (e.g., sync_reasoner())
SPARQL queries
Demonstrate and implement how using an OWL ontology improves LLM reasoning (e.g., fewer steps, better answers, more structure) in a LangGraph agent setup.
Python
LangGraph / LangChain
OWL ontology (via owlready2)
OpenAI or DeepSeek LLM
SPARQL (optional)
Experience with OWL ontologies (Protégé / owlready2)
Understands how to connect symbolic logic (ontology) with LLM reasoning
Bonus: Has worked on CPQ, configuration, or constraint systems
To Apply:
Please share:
Similar agent or ontology-based automation work
Your approach to integrating ontology with LLM tools
Our current system supports YAML-based business rules. Now we want to measure and expand reasoning using ontology via:
owlready2 reasoning (e.g., sync_reasoner())
SPARQL queries
Demonstrate and implement how using an OWL ontology improves LLM reasoning (e.g., fewer steps, better answers, more structure) in a LangGraph agent setup.
Python
LangGraph / LangChain
OWL ontology (via owlready2)
OpenAI or DeepSeek LLM
SPARQL (optional)
Experience with OWL ontologies (Protégé / owlready2)
Understands how to connect symbolic logic (ontology) with LLM reasoning
Bonus: Has worked on CPQ, configuration, or constraint systems
To Apply:
Please share:
Similar agent or ontology-based automation work
Your approach to integrating ontology with LLM tools