Research
A reliable agent starts with a graph.
Our graphs come from research carried out with Lemon Learning and published at ICMLC 2026.
The paper
Building Specialized Software-Assistant ChatBot with Graph-Based Retrieval-Augmented Generation
Mohammed Hilel, Yannis Karmim, Jean de Bodinat, Reda Sarehane, Antoine Gillon
The context
Guiding a user through complex software.
Digital adoption platforms help employees use complex enterprise software: CRM, ERP, HRMS. Lemon Learning has shown that good guidance cuts training costs and speeds up onboarding. But writing and maintaining those guides is still manual work, screen by screen.
The problem
The model makes things up
Without a structured understanding of the target software, a large language model answers confidently, and sometimes wrongly.
It cannot be retrained
Production models are closed APIs: no access to the weights, so no fine-tuning.
The software changes
Every release moves screens and fields. A hand-written guide ages at the pace of updates.
The method
Turning the application into a state-action graph.
- 01
Extract
The web application is crawled and its interfaces extracted: screens, fields, buttons.
- 02
Structure
Each screen becomes a state, each gesture an action. The whole software becomes a knowledge graph.
- 03
Retrieve
For each question, the agent fetches the path in the graph that answers it, instead of guessing.
- 04
Answer, grounded
The model generates its answer from that path: contextual, checkable, nothing invented.
In production
It did not stay a paper.
The framework was integrated into Lemon Learning’s guidance workflows. The paper details the engineering pipeline, the design of graph retrieval and the lessons learned from deployment: scalability, robustness, industrial use cases.
And in our agents
The same idea structures every Rakam agent: a workflow graph, named steps, sensitive cases handed to a human. A predictable agent, so a testable one.
In preparation
Two more papers.
Workflow graphs
How a work graph makes an agent predictable and measurable.
Ethical AI
Human oversight, traceability and user permissions in an agent that acts.
See this graph on your software.
