You will get a fantastic Smart-Aleck (AI-Powered Legal Research Assistant)
Project details
Smart-Aleck is an advanced AI-powered legal research platform designed to dramatically reduce research time for lawyers, paralegals, and legal researchers. By leveraging semantic search technology, Smart-Aleck goes beyond simple keyword matching, understanding the true context of legal queries to deliver precise and relevant case law, statutes, and legal precedents. It supports multilingual capabilities, enabling seamless research in both English and Georgian, making it highly effective for diverse legal teams.
Built with FAISS vector databases, sentence-transformers, and the OpenAI API, the platform intelligently indexes and retrieves information from large repositories of legal documents and court decisions. A user-friendly React.js chatbot interface allows natural language queries, while secure login ensures data confidentiality. With real-time search, summarization, and citation retrieval, Smart-Aleck empowers legal professionals to find answers up to 70% faster, improving accuracy, efficiency, and productivity in legal workflows.
Built with FAISS vector databases, sentence-transformers, and the OpenAI API, the platform intelligently indexes and retrieves information from large repositories of legal documents and court decisions. A user-friendly React.js chatbot interface allows natural language queries, while secure login ensures data confidentiality. With real-time search, summarization, and citation retrieval, Smart-Aleck empowers legal professionals to find answers up to 70% faster, improving accuracy, efficiency, and productivity in legal workflows.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,000
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 9 days |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
About Dhaval
AI/ML Engineer | Generative AI | LLMs | RAG | Data Engineering |Python
Elgin, United States - 7:40 pm local time
With over 8+ years of experience in AI/ML engineering, data engineering, and cloud-native development, I specialize in designing and deploying production-ready AI solutions using:
• Python & SQL
• GPT-4, Mistral & Large Language Models (LLMs)
• LangChain & Retrieval-Augmented Generation (RAG)
• Hugging Face Transformers
• AWS (SageMaker, Lambda, S3, Redshift)
• GCP (Vertex AI, BigQuery)
• Apache Spark, Kafka, Flink & Airflow
• Docker, Kubernetes & MLflow
What I Deliver
• Custom AI assistants, chatbots, and enterprise knowledge search solutions
• RAG-based applications with vector search and document intelligence
• LLM fine-tuning, prompt engineering, and AI workflow automation
• Fraud detection, anomaly detection, and risk modeling systems
• Machine learning pipelines from data ingestion to production deployment
• NLP solutions for document processing, summarization, sentiment analysis, and classification
• Real-time recommendation engines and personalization platforms
• Scalable ETL/ELT pipelines and cloud data engineering solutions
• MLOps implementation, model monitoring, and automated retraining workflows
• REST APIs and backend services for AI-powered applications
Recent Enterprise Projects
• Built AI-powered knowledge assistants using GPT-4, LangChain, and RAG architectures, enabling real-time retrieval of enterprise information and reducing manual search efforts.
• Developed fraud detection and anomaly detection platforms using machine learning, streaming analytics, and behavioral modeling to identify suspicious transactions in real time.
• Implemented recommendation and personalization engines that generated dynamic content, product recommendations, and pricing strategies based on customer behavior and inventory trends.
• Designed and optimized large-scale cloud data platforms using AWS and GCP, processing millions of records through automated ETL pipelines and analytics workflows.
• Delivered NLP solutions for document summarization, compliance reporting, sentiment analysis, and intelligent document classification using transformer-based models.
Why Clients Work With Me
• I combine deep AI expertise with strong software engineering and data engineering foundations.
• I build production-ready solutions—not just proofs of concept.
• I focus on scalability, security, maintainability, and measurable business outcomes.
• I provide end-to-end ownership—from discovery and architecture to development, deployment, and optimization.
• I stay aligned with business goals while leveraging the latest advancements in Generative AI and machine learning.
If you're looking to build an AI assistant, implement Generative AI, automate business processes, modernize your data platform, or deploy machine learning solutions at scale, I'd be happy to help.
Let's build intelligent solutions that drive real business value.
Steps for completing your project
After purchasing the project, send requirements so Dhaval can start the project.
Delivery time starts when Dhaval receives requirements from you.
Dhaval works on your project following the steps below.
Revisions may occur after the delivery date.
Step :- 1
Legal document dataset, preferred jurisdictions