.webp)
LLM-Powered Recommendation System
An Israeli startup is transforming U.S. service providers' personalized offerings. Dataforest scaled the project from prototype to a full web app with advanced ML, LLMs, and RAG fine-tuning. Managing 100,000+ products for 50,000+ customers, it delivers precise recommendations and revenue forecasts, maximizing sales opportunities
< 1 min
tailored recommendations delivery
100,000+
products supported by the platform
.png)
About the client
The client, an innovative Israeli startup, aims to transform how U.S. service providers—especially in insurance and financial services—generate business recommendations in complex environments.
Tech stack
Qdrant
Django
AWS
ReactJS
Claude 3 Sonnet
The client's needs
The client envisioned a recommendation system powered by LLMs to deliver personalized product suggestions based on customer demographics, needs, and global trends. They saw an opportunity to automate business offers in complex environments. Dataforest took this vision from prototype to a fully functional web app with sophisticated recommendation and forecasting tools.
Challenges & solutions
Challenge
Develop a solution capable of handling an inventory scaled to over 100,000 products.
Solution
To address this complexity, we integrated the LLM with a Qdrant vector database and advanced ML models, including classification and clustering, further optimized results, providing precise recommendations at scale.
Challenge
Ensure the solution delivers results with high accuracy.
Solution
To deliver high accuracy for business offers generation, we integrated Qdrant vector DB with an LLM (Claude Sonnet 3) to perform retrieval-augmented generation (RAG), ensuring the system retrieves and contextualizes relevant data points before generating personalized product recommendations.
Engineered prompts tailored to yield highly accurate, contextually relevant recommendations and enhance the reasoning capabilities of the LLM, utilizing state of the art prompting techniques such as Chain-of-Thought (CoT) and Meta prompting.
Challenge
Achieve high efficiency and rapid results in generating new opportunities.
Solution
Our solution harnesses the power of Qdrant vector DB and advanced prompt engineering to deliver swift, precise filtering—driving exceptional speed and efficiency in opportunity generation.
Challenge
Delivering highly personalized, timely recommendations in an intuitive manner.
Solution
Developed a text2query system that transforms natural language product and customer descriptions into a custom ElasticSearch-like query format, enabling users to intuitively filter products and customer profiles for enhanced interaction flexibility.
Incorporated information on global events via Web Search services, allowing the recommendation engine to adjust suggestions based on relevant and impactful current events.
Leveraged the LLM to dynamically create and populate nodes within a knowledge graph, enabling a structured and interconnected representation of customer preferences, product information, and interactions, further enhancing recommendation quality.
Challenge
Solution
Challenge
Solution
Challenge
Solution
Challenge
Solution
Results
The client required a scalable recommendation system to suggest products based on customer characteristics and external trends, capable of handling from a few dozen to over 100,000 items.
Dataforest developed a robust solution integrating LLMs with a vector database, delivering personalized recommendations for up to 50,000 customers. Enhanced with advanced algorithms, this adaptable web app seamlessly fits various industries. Utilizing state-of-the-art prompting, it provides data-driven recommendations, accurate forecasts, and actionable insights—empowering businesses to offer precise and trend-focused solutions.
LLM-Powered Recommendation System
Steps of providing
data scraping services
How we provide data integration solutions
Steps of providing web applications services
The way we deal with your task and help achieve results
The way we deal with your issue and achieve result
Financial Intermediation Platform
The project aims to develop a deal origination platform for private equity investments in infrastructure-related sectors and involves building a secure, interactive B2B platform from scratch, with sign-up functionality to connect investment firms to proprietary investment opportunities. DATAFOREST built a highly-loaded platform and applied AI functionality to empower the application's development.
They understood our requirements, translated into actions rapidly, and adapted to requests easily.
All Success Stories
Latest publications
All publications
May 27, 2025
11 min
AI Agents in Loan Processes of Financial Institutions
We’d love to hear from you
Share project details, like scope or challenges. We'll review and follow up with next steps.
