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
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.
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.
Develop a solution capable of handling an inventory scaled to over 100,000 products.
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.
Ensure the solution delivers results with high accuracy.
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.
Achieve high efficiency and rapid results in generating new opportunities.
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.
Delivering highly personalized, timely recommendations in an intuitive manner.
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.
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.
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model accuracy
timely development
Enrico Cattabiani
They understood our requirements, translated into actions rapidly, and adapted to requests easily.
system integrations
CX boost
Stuart Theobald
They've had a quick grasp of what we are trying to do and delivered to our spec without a fuss.
performance boost
cost optimization
Daniel Garner
The team of DATAFOREST is very skilled and equipped with high knowledge.
satisfied clients in the first three months post-release
top providers integrated
API Integrations for Chargeback Management Tool
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