Lawyers and Legal practitioners spend resources and time reading lengthy court judgments and analyzing pre-trail case documents like FIRs to find relevant precedents and legal sections for their cases. With data scattered across different platforms and FIRs written in Urdu, this manual process becomes slow, less productive and prone to human error. The legal domain lacks an intelligent system that can automatically interpret case documents, summarize them and assist lawyers in order to save their time and increase productivity. Specter aims to solve this problem by building an AI powered RAG based legal assistant that can read legal documents, translate them into English, extract the key legal sections, and retrieve related past judgments from reliable sources .This project will automate legal research, provide concise summaries of judgments, and help lawyers better understand the strengths and weaknesses of a case. Specter will serve as a domain specific AI legal assistant for lawyers, law students, and legal firms by reducing research effort and time. 

The practice of Law relies majorly on the ability to retrieve and use previous court decisions and statutes (Written Law) on a case. In their daily practice, the legal practitioners peruse through the First Information Reports (FIRs) and legal documents, determine the laws and parts of the law imposed and then locate the relevant precedents (previous judgement) among the numerous judgments and legal materials. This issue is compounded by the legal environment which adds some additional layers. Courts in Pakistan follow a structured hierarchy which includes the Supreme Court, several provincial High Courts and courts which are performing at the district level. These legal books, old decisions and law reports are scattered in the form of printed books, legal journals and court libraries. It is that disintegration which makes the discovery of the strongest and the latest legal authority difficult. 

Legal Practitioners are under severe pressure to meet court deadlines, attend hearings, conduct client meetings, and all while managing resources. Although legal research is critical, it may be sidelined or not done properly, thus resulting in analysis being incomplete or overlooked precedents. [1]. In smaller law firms and for individual practitioners, time and resources are limited and therefore quick and accurate research becomes even more difficult to achieve. First Information Reports and pre trial legal documents are written in Urdu while Law is written in English language thus making it difficult to extract and map one onto the other. This makes the research activity time consuming so legal practitioners need to locate past judgments related to their case in research materials like Court Libraries, Legal Journals and Books. They need to read the past judgments and summarize them in order to locate statutes that apply to their case and execute their trial preparations based on that particular research. This reduces the speed at which a case is prepared and is likely to leave out an important precedent as a result of a potential human error. 

The proposed solution is an AI powered RAG based legal assistant built specifically to help lawyers to increase their productivity by getting rid of manual search of legal documents and precedents. The system will provide a LLM based chat interface to the user, allowing user to either ask a complex legal query or input a legal document of case like an FIR, perusing the text from document using OCR (even if it’s handwritten in Urdu) and converting it into English using translation techniques. Then it can automatically identifies the laws and sections mentioned in the case document through LLM reasoning and thinking and searches from a large database using semantic retrieval of past court judgments stored in the system in the form of Vector DB. The system then outputs the most relevant past case decisions (called precedents) that matches that particular case. It also summarizes those judgments in simple words so lawyers can quickly understand them. Moreover, it highlights the strong and weak points in each case to help lawyers build better arguments. The model allows lawyers to ask any legal question and get instant, source based answers with case references using modern Retrieval and Generative techniques (RAG). This saves a lot of time, reduces research mistakes, and makes legal work easier, especially for small law firms and individual practitioners. 

SPECTER AI's methodology is based on the structured, modular, and iterative approach that uses Natural Language Processing (NLP), Machine Learning, and Information Retrieval techniques (RAG) to create an intelligent legal information system. The system would gather, and store legal information including judgments, statutes and case citations, in a way that legal users can effectively search and comprehend precedents, and use them effectively in their case preparation. 

Specter AI is based on a multi-step pipeline, which is depicted in the architecture diagram. It gathers legal information using reliable sources and then data is preprocessed and embedded which makes it ready to be searched and retrieved using semantics. The processed data is then stored in a Vector Database and can be quickly accessed with the help of a RAG system which allows user to ask questions relevant to their case and the system will answer them based on the previous relevant judgements. 

The major goals and objectives of the project are:

• To gather and organize a huge amount of legal data including case judgements and laws.

•  To preprocess and structure the collected legal data for efficient search, retrieval and analysis. 

• To develop an architecture that integrates and indexes legal documents into a knowledge base by making use of the modern techniques of NLP and vectorization. 

• To create a retrieval system having the ability of locating the most relevant judgments based on user queries or legal sections. 

• To present a method of summarization that will result in brief and precise summaries of the retrieved judgments. 

• To assess the effectiveness of the retrieval and summarization modules by using the metrics available. 

• To present a functional prototype that assists lawyers in quick and reliable case research. 

• To provide a comprehensive blueprint and practical design for applying large language models to the Pakistani legal system. 

The aim of the project is to create a system capable of processing and analyzing Pakistani legal texts comprehending, extracting them, translating them, and inferring conclusions to support legal practitioners on their legal work. The system will be based on NLP, Machine Translation, Information Retrieval and Text Summarization to read and understand the law, translate legal documents in Urdu into English, find the relevant sections of the law and past cases and provide brief summaries to facilitate easier analysis. 

This project is being directed at legal practitioners and law firms who need quicker and more dependable access to the information about law. In addition, the project would help to support bilingual comprehension to enhance accessibility and provide a working prototype that streamlines the entire process through retrieval and summarization as a useful legal research and teaching aid. 

We are currently in the process of collecting our Pakistani legal dataset (Including FIRs, Court Data/Judgements, Legal Books and Journals). Our primary focus is on gathering and pre-processing legal data of Pakistan which is scattered across many sources. If you are interested in participating in our data collection process. Kindly contact us

Currently, we are facing the following challenges:

• Legal documents are often unstructured, inconsistent, or scanned with poor quality, which makes accurate processing difficult.

• Preserving the original legal context and meaning while analyzing and summarizing documents remains a key challenge.

• Limited availability of digitized legal data, especially older case records and FIRs, affects coverage and completeness.

• Handling multilingual legal material, particularly Urdu and English documents together, is still an ongoing challenge.

Tools and technologies that will be used for this project are:

For desktop application development

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