AI Agents for Business: Practical Use Cases
AI agents and intelligent assistants are opening new opportunities for businesses to automate tasks, process information, and deliver more personalized services. By combining large language models with business data, application logic, and external tools, these systems can support complex workflows and assist teams in making more informed decisions.
LansurAI explores practical applications of AI agents across different business functions, including personalized shopping recommendations, customer support, feedback analysis, and recruitment. Each demonstration illustrates a specific business challenge, explains how the AI-powered system works, and highlights opportunities to improve existing processes. These use cases are designed to explore AI capabilities and integration approaches rather than present ready-made commercial products.
G-Chief
Brief description
An intelligent AI-powered nutrition and grocery recommendation system that helps generate recipe suggestions and personalized recommendations based on customer requests, as well as create product lists and find suitable alternatives.
How it works
G-Chief processes customer requests using an LLM, identifies their goals, tastes, and dietary restrictions, and generates suitable recipes and personalized recommendations. The assistant creates product lists, automatically selects alternatives, and matches ingredients with the store’s catalog. G-Chief integrates into existing workflows and enables businesses to scale personalized recommendations without increasing support workload.
Pain Points We Solve
- Low user conversion due to difficulties selecting suitable meals and products
- High support costs for assisting customers with nutrition and product choices
- Inability to scale personalized recommendations for a large number of users
- Lost sales caused by the lack of automatic alternative suggestions when a product is out of stock
- Insufficient personalization of the assortment leading to low customer engagement
Q-Assistant
Brief description
AI-powered customer support system that understands context, provides instant responses, and learns from every interaction to deliver exceptional customer experiences.
How it works
Q-Assistant analyzes customer queries using advanced NLP, searches through your knowledge base, and provides personalized responses. It integrates seamlessly with your existing support tools and escalates complex issues to human agents when needed.
Pain Points We Solve
- Long response times frustrating customers
- Repetitive questions overwhelming support teams
- Inconsistent answers across different agents
- High operational costs for 24/7 support
- Difficulty scaling support during peak times
R-Analyst
Brief description
Intelligent review analysis system that transforms customer feedback into actionable insights, helping businesses understand sentiment, identify trends, and improve their products and services.
How it works
R-Analyst processes reviews from multiple sources using sentiment analysis and topic modeling. It categorizes feedback, identifies key issues, tracks sentiment trends over time, and generates detailed reports with recommendations for improvement.
Pain Points We Solve
- Overwhelming volume of reviews to analyze manually
- Missing important customer insights buried in feedback
- Inability to track sentiment changes over time
- Difficulty identifying specific improvement areas
- Time-consuming process to extract actionable data
V-Augmentor
Brief description
AI-powered HR assistant that streamlines vacancy creation, CV processing, candidate-job matching, and interview preparation, helping hiring teams evaluate candidates faster and make more informed decisions.
How it works
V-Augmentor uses AI to turn hiring requirements into structured job profiles and uploaded CVs into candidate profiles. It compares candidates with specific vacancies, identifies relevant matches and potential gaps, calculates match scores, and generates targeted interview questions based on the analysis.
Pain Points We Solve
- Time-consuming creation and management of job vacancies
- Manual processing and review of candidate CVs
- Difficulty evaluating how well candidates match job requirements
- Missing important skill and experience gaps during candidate screening
- Time-consuming preparation of relevant interview questions