Case Study – SCALAC.AI

Navigate complexities of Polish laws with an advanced AI legal assistant

What was the challenge?

Polish legal system is vast and complex, making it difficult for both legal professionals and the general public to find relevant information quickly and efficiently. Traditional legal research methods are often time-consuming and require a high level of expertise.

Solution we offered.

We developed Lex-GPT.pl, a sophisticated AI assistant specifically designed for Polish law. The system utilizes a hierarchical structure of agents, including a „plan and execute” agent and multiple ReAct agents. When a user asks a question, a planner agent first creates a research plan. Then, specialized ReAct agents operate in an iterative loop, using Retrieval-Augmented Generation (RAG) techniques to find relevant articles and paragraphs from indexed legal codexes and court judgments. If the retrieved information is not relevant, the ReAct agent can rephrase the query and search again, repeating this process until the correct information is found. For complex questions, the plan and execute agent can run multiple codex and judgment ReAct agents in parallel, significantly improving response time and comprehensiveness. Both the plan and execute agent and ReAct agents are implemented using Typed Actors with Finite State Machines (FSMs) in Scala, providing robust state management and concurrent execution. The legal database is indexed using ostgreSQL with the pgvector extension.

What’s the business value?

Lex-GPT.pl dramatically accelerates legal research, providing users with precise and relevant information in a fraction of the time. This empowers legal professionals to be more efficient and effective, while also making legal information more accessible to the public. The hierarchical agent system ensures a comprehensive and structured approach to answering legal queries, leading to more accurate and reliable results.

Tools and Technologies

Scala
Scala Typed Actors with FSMs
Python
PostgreSQL with pgvector
RAG (Retrieval-Augmented Generation)
ReAct Agent Framework
Hierarchical Agent System (Plan and Execute)

Improve learning experience and productivity with intelligent browser plugin

What was the challenge?

Users frequently interrupt their workflow to search for definitions, explanations, or summaries of content they encounter online. This constant context-switching between tabs and applications is inefficient and disrupts concentration, hindering both productivity and learning experience.

Solution we offered.

We developed a proof-of-concept browser plugin for the Intuition project that integrates seamlessly with the OpenAI API. This plugin provides a suite of AI-powered tools directly within the browser. By highlighting any word or sentence, users can instantly receive explanations, synonyms, or summaries. The plugin also features an assistant chat for more complex queries, configurable API endpoints for custom integrations, and the ability to find relevant online courses based on the selected topic. To further enhance learning, it can generate quiz questions from the text to test comprehension.

What’s the business value?

Browser plugin boosts user productivity and learning efficiency by providing immediate access to information and tools without leaving the current webpage. This minimizes context-switching, improves comprehension, and creates a more engaging and interactive learning experience. The configurable nature of the plugin allows for a high degree of personalization, making it adaptable to a wide range of user needs and workflows.

Tools and Technologies

JavaScript
OpenAI API
Browser Extension APIs