Transforming Company Search with AI-Powered Semantic Search

From Traditional Keyword Search to a Smarter, AI-Driven Experience

Vector Search

Client Background

Our client, a B2B data provider, was struggling with an outdated keyword-based search system that allowed users to search for companies within a database of over 2.1 million companies.

The existing system relied on exact keyword matching, requiring users to enter specific company names, industries, or pre-defined categories. However, this approach was limiting because:

🔹 Users often didn't know the exact terms to use, leading to frustration and incomplete results

🔹 Keyword-based search required complex filtering, which made the user experience slow and unintuitive

🔹 Companies with different spellings, synonyms, or alternate descriptions were hard to find

The client wanted a more intuitive, AI-powered solution that could understand the intent behind user queries and return the most relevant results without relying on exact keyword matching.

This is where Velais stepped in to revolutionize the search experience.

The Challenge: A Rigid Search System That Failed to Deliver Relevant Results

Despite having a massive company database, the client’s existing system was inefficient and frustrating for users.

Key challenges included:

✅ Rigid Keyword Matching: Users had to guess the right words to get meaningful results

✅ Lack of Context Understanding: Searches couldn’t interpret semantic meaning or intent

✅ Poor Data Enrichment: The database lacked rich, AI-generated descriptions for companies

✅ Slow and Inefficient Filtering: Users had to manually narrow down results, slowing decision-making

Our mission was to replace this outdated keyword search with a powerful, AI-driven semantic search system, making it as simple as asking a question and receiving the best-matched companies instantly.

The Solution: A Next-Gen Search System Powered by GenAI & MongoDB Vector Search

To solve this challenge, we designed and implemented an end-to-end AI-powered search system. Our approach focused on:

1️⃣ Building a Comprehensive AI-Driven Data Pipeline

The foundation of semantic search is high-quality, enriched data. We built a GenAI-powered data pipeline that automatically:

• Search the internet for each company data (descriptions, industry trends, services, etc.)

• Uses a Generative AI model to summarize and enhance company descriptions

• Creates embeddings (AI-generated vector representations) for each company description

• Stores the vectorized data in MongoDB Vector Search for fast and efficient retrieval

This automated data pipeline ensured that the system always had up-to-date, AI-enriched information about companies.

2️⃣ Implementing a Semantic Search Engine

Instead of relying on keyword-based matching, we leveraged MongoDB Vector Search & Atlas Search to enable natural language search.

• Users could now describe the kind of company they were looking for instead of typing rigid keywords

• The system understood meaning and intent, delivering results even if exact words didn’t match

• Searches became more intuitive, faster, and far more accurate

3️⃣ Fine-Tuning an Embedding Model for Better Accuracy

To enhance the relevance of search results, we fine-tuned a custom AI embedding model tailored to the B2B industry. This ensured that companies were matched based on real-world similarities, not just text overlap.

Example:

🔍 Old System: Searching for “AI startups in finance” required filtering by "AI", "Startup" and "Finance" manually.

🚀 New System: Users could simply ask the system → "Show me startups using AI to improve financial decision-making" and get directly relevant results.

The Results: A 5X Improvement in Search Efficiency & Accuracy

💡 Before Velais: A rigid, keyword-based search that was frustrating for users

💡 After Velais: A fully AI-powered semantic search experience

🚀 Key Outcomes:

✅ Search accuracy improved by 5X, reducing irrelevant results

✅ Search speed increased by 60%, leading to a smoother user experience

✅ User satisfaction skyrocketed, with 90% of searches returning useful results on the first attempt

✅ Company discovery improved, with 20% more businesses being surfaced compared to keyword-based search

✅ Automated data enrichment, ensuring that company profiles were always up to date

Within six months, the client saw a significant increase in search engagement and user satisfaction, making the entire database more valuable and easier to navigate.

Why This Case Matters

This project highlights Velais' expertise in AI-powered search solutions. Instead of just improving an outdated search system, we redefined how users interact with data.

The transformation from keyword search to semantic search allowed users to search naturally, without needing to know exact terms. This reduced search friction, improved discovery, and made decision-making far easier.

For any business dealing with large datasets, customer directories, or internal databases, this case proves that AI-driven semantic search can be a game-changer.