LeadStar Al Assistant

Industry
Affiliate Marketing / AdTech
Year
2024
Location
Europe
Services
Al Chatbot Development, LLM Integration, Backend Development
Project's length
~4-6 weeks (MVP phase)

LeadStar

LeadStar is an affiliate marketing platform that connects publishers with advertising campaigns and monetization opportunities. It provides access to hundreds of campaigns, along with analytics and promotional tools designed to help publishers grow their online revenue. With an intuitive dashboard and automated processes, LeadStar enables users to efficiently manage campaigns, monitor performance, and continuously optimize their monetization strategies.

Project overview

LeadStar identified an opportunity to leverage generative Al to help publishers navigate the platform more efficiently and find answers to their questions faster. The idea was to integrate an Al-powered chatbot directly into the platform interface. The assistant combines user queries with the platform’s knowledge base to deliver clear, context-aware answers and practical guidance on campaigns, tools, and platform features.

Our tasks

Al Chatbot Integration

The goal was to implement a conversational assistant inside the LeadStar platform interface. The chatbot needed to connect the platform’s internal knowledge sources with a large language model and provide contextual answers to user questions in real time.

LLM Optimization

To achieve reliable answers and a smooth user experience, it was necessary to experiment with prompts, retrieval mechanisms, and model parameters. The focus was on improving response accuracy and minimizing the risk of incorrect or hallucinated answers.

Backend Architecture

At the same time, it was important to design a backend architecture capable of handling communication between the user interface, the knowledge base, and the language model. The solution needed to remain scalable and efficient while supporting future AI modules.

01

Goals

Improving user onboarding

One of the main goals was to simplify the onboarding process for new publishers. Many users needed guidance when navigating the platform and selecting campaigns. The chatbot was designed to provide quick answers and guide users through the available features.

Making platform knowledge more accessible

LeadStar contains a large amount of documentation, tutorials, and blog content explaining how to use its tools. The assistant was created to transform this knowledge into a conversational format, allowing users to find information simply by asking questions.

Increasing platform engagement

By making it easier to discover campaigns, tools, and platform features, the AI assistant helps publishers interact with the platform more frequently and make better use of its monetization opportunities.

02

Challenges

Providing accurate answers from multiple knowledge sources

The chatbot needed to retrieve information from different types of content, including blog posts, documentation, and internal guides. These sources varied in structure and style, which required preprocessing and semantic indexing.

Reducing the risk of hallucinations

Since the assistant provides information about platform functionality and financial processes, ensuring answer reliability was essential. The architecture had to minimize the risk of the language model generating incorrect information.

Designing a scalable AI architecture

The solution needed to work as an MVP but also support future features such as campaign recommendations, automated insights, and personalized suggestions for publishers.

03

Our approach

Knowledge base processing and embeddings

All knowledge sources — including platform documentation and blog content — were processed and transformed into vector embeddings. These embeddings were stored in a vector database, allowing the system to perform semantic search and retrieve the most relevant information for each user query.

Retrieval-augmented generation

Instead of relying solely on the language model, the system retrieves relevant knowledge from the database before generating a response. This architecture improves answer accuracy and ensures the assistant remains grounded in the platform’s documentation.

Prompt engineering

Carefully designed prompts guide the language model in interpreting user questions and generating responses aligned with the platform’s knowledge base. This significantly reduces hallucinations and improves answer quality.

Scalable backend architecture

A dedicated backend service manages communication between the chatbot interface, the vector database, and the language model. This architecture allows the system to support additional AI modules in the future.

Technology we used

Large Language Models (LLMs)

Vector Database

Embeddings & Semantic Search

Retrieval-Augmented Generation (RAG)

Backend AI Service

Web Chatbot Interface

04

Results

Faster access to platform knowledge

Publishers can now find answers about campaigns, tools, and platform functionality instantly through the chatbot interface. This significantly reduces the need to search through documentation.

Improved user experience

The conversational interface makes the platform easier to navigate and helps users quickly understand how to use its features.

Foundation for future AI features

The architecture built for the chatbot creates a strong foundation for additional AI capabilities, including campaign recommendations, automated insights, and personalized publisher guidance.

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