Searchplex provides a retrieval foundation for production AI, enabling reliable AI search, retrieval‑augmented generation (RAG), and agent‑driven workflows at scale. The platform modernizes search infrastructure, offering hybrid multilingual capabilities and cost‑optimized performance for teams migrating legacy systems or building new AI solutions.
Funding
Funding not disclosed
Founders
Product
Problem
Many organizations rely on legacy keyword search stacks or ad‑hoc vector bolt‑ons that cannot meet the precision, scale, and reliability demands of modern AI‑driven search, retrieval‑augmented generation (RAG), and agent workflows. As retrieval layers become bottlenecks, relevance plateaus, costs rise, and systems become difficult to maintain in production.
Solution
Searchplex offers a specialist retrieval engineering service that redesigns and modernizes the core search architecture for production AI applications. By integrating lexical, semantic, and behavioral signals into a unified retrieval foundation, the company enables fast, multilingual, and cost‑optimized document retrieval for AI search, RAG, and agent‑driven use cases. Their expertise spans indexing, ranking, relevance tuning, and AI‑native retrieval, allowing teams to migrate from brittle legacy stacks or improve existing pipelines with a reliable, enterprise‑grade solution. The resulting systems deliver higher precision, predictable performance, and easier operational control, supporting large‑scale deployments across legal tech, finance, publishing, and e‑commerce domains.
Target Audience
Primary customers are technology teams in enterprises and platforms that require high‑precision, scalable search or retrieval capabilities—such as legal technology providers, financial services, publishing houses, and e‑commerce operators building AI‑enhanced products.
Features
- End‑to‑end redesign of the retrieval layer, including candidate generation, ranking, filtering, and relevance evaluation
- Hybrid search architecture that combines lexical keyword matching with vector‑based semantic similarity for multilingual corpora
- Continuous relevance tuning through query analysis, field design, and production‑grade evaluation loops
- Migration services that replace fragmented legacy pipelines with a clean, scalable retrieval foundation
- Integration with AI workflows such as retrieval‑augmented generation and autonomous agents, providing grounded and controllable results
- Expertise in cost optimization and performance scaling for high‑throughput, enterprise environments