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NIX helped transform the client’s complex automotive marketplace with an AI-driven conversational search engine that bridges the gap between natural user language and structured database parameters, driving higher output relevance and better user engagement.
Retail and E-commerce, Automotive
Generative AI, Data Science, Web Development, DevOps Services
AWS Cloud, AWS AI Agents, Azure AI, Python, React, FastAPI, LangSmith
The client, CarSoup, operates a leading online automotive marketplace for advertising, buying, and selling cars of all types from various dealers based on different requirements. The aggregation platform synchronizes daily with partner inventory to offer customers up-to-date listings, comparison tools, reviews, buying advice, and financial services.
Under a commission-based model, the CarSoup marketplace streamlines the search experience, and once a user finds a match, the platform redirects them to the dealer’s website. This empowers dealers to control the customer experience, finance requests, and lead conversion.
The client identified a significant point of friction: while the platform offered powerful search capabilities, the filter and configuration setup was quite complex and unintuitive, discouraging new users from using it. This ultimately led to users getting insufficient car search results and the client losing conversions and missing revenue opportunities. To address this issue, they decided to build an AI-powered site search capability that would:
NIX was engaged to develop a smart AI semantic search that precisely classifies the user’s intent and requests relevant clarifications through seamless, human-like interactions. The goal of the engine is to efficiently utilize the client’s complex search engine API by responding to user inputs and adjusting search parameters based on the conversation.
Many shoppers use slang terms when searching for certain cars, but the LLM often fails to interpret those informal inputs and map them to structured search parameters without specific context. The main challenge was the gap between how users naturally describe vehicles and how databases categorize them.
Our team engineered a sophisticated AI agent application hosted on AWS Cloud to significantly improve the vehicle discovery process, while also addressing client challenges in smart, innovative ways.
We developed an advanced decision‑making framework leveraging AWS AI Agents Service together with the Azure OpenAI Service. This multi‑agent system orchestrates the full capabilities of the client’s search API, dynamically configuring the best search parameters based on the user’s conversation.
To ensure a cohesive user experience, we integrated the engine via FastAPI and developed a custom, responsive React chatbot interface embedded directly within the platform’s UI. We further reinforced this complex ecosystem with a custom LangSmith logging and monitoring application. This provides total visibility into the AI’s decision-making paths, allowing us to proactively prevent pipeline bottlenecks.
To bridge the gap between structured API logic and shoppers’ natural language, we implemented a phased optimization strategy:
Improved the accuracy and relevance of search results, enabling users to find desired cars more efficiently.
Quicker, more precise results led to longer user sessions, increased engagement, and higher satisfaction rates, fostering customer loyalty in the competitive online market.
The AI engine effectively guided users who previously dropped off due to the tricky manual filter configurations.
The platform now offers a unique “digital concierge” experience that differentiates it from standard automotive aggregators, fostering long-term customer retention.
Collaborating with NIX, the client implemented the smart semantic search engine that transforms the user journey from a tricky filtering task into a streamlined, conversational experience. By successfully bridging the gap between search API parameters and user intent, the solution has already delivered significant gains in both user engagement and lead quality.
Up to 40%
increase in user engagement with advanced search features
35%
overall improvement in the relevance of search results
75%
reduction in support tickets related to search filter issues
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