AI & Search Systems
Build search and discovery experiences that understand intent, return relevant results, and drive conversion.
Common use cases
Doctor/Provider Search
Match patients to providers based on specialty, location, availability, and insurance
Marketplace Search
Help buyers find the right products with natural language queries
Support/Q&A Search
Surface relevant help articles and answers to customer questions
Internal Knowledge Search
Build company knowledge bases that teams actually use
How we approach AI & Search
We combine traditional search technology (Elasticsearch) with modern semantic search and LLM capabilities to build systems that actually work.
Audit current search behavior and user intent patterns
Design hybrid architecture (keyword + semantic + filters)
Build relevance evaluation framework
Tune ranking and implement feedback loops
Work
AI-Powered Search & Discovery
E-Commerce Platform
Problem
Product search had high zero-results rate and poor relevance. Users couldn't find what they needed.
Outcomes
- Zero-results rate reduced by 60%
- Search-to-purchase conversion up 35%
Frequently asked questions
Do you build custom AI models?
Usually no. We focus on adoption, workflow, and retrieval. Models are a means to an end. We use existing LLM APIs (OpenAI, Anthropic, etc.) and focus on the hard parts: evaluation, relevance, and user experience.
How long does a search implementation take?
Depends on scope. A hybrid search MVP can be built in 4-8 weeks. Full production rollout with evaluation framework takes 8-12 weeks.
Ready to improve your search?
Let's discuss your search challenges and how we can help build a better discovery experience.