The Retrieval Foundation for Production AI
Build on a retrieval foundation that actually works. We design and modernize retrieval for AI Search, RAG, and Agents — delivering reliable answers and discovery at costs that scale.
For teams building new AI infrastructure or migrating legacy search.
Trusted by teams running serious search systems
LexisNexis
Primer AI
Binance
Splore AI
Curated AI
IPRally
Achmea Impact Ventures
gem.app“The Vespa audit was thorough, clearly structured, and addressed exactly what we needed—performance, scalability, and cost optimization. Their depth of search expertise and clear communication stood out throughout the engagement.”
“Searchplex delivered a hybrid, multilingual search solution that elevated our document retrieval system, resulting in faster & more precise searches across multiple languages. This capability was crucial for our legal tech needs.”
“Excited about this partnership. Many of the customers we speak to are held back by not having the development capacity in-house to build the solutions they want on Vespa. Searchplex can help with that.”
Find your entry point.
Teams usually arrive at Searchplex when their search stack begins limiting what their product or business needs to deliver.
Search quality is inconsistent but the cause is unclear.
Test RAG or semantic retrieval ideas before committing.
Building AI search or RAG from scratch.
If platforms like Coveo, Algolia, or Bloomreach are starting to constrain you, explore the capability gaps in our solutions section. If the issue runs deeper, start with a Search Stack Audit.
Retrieval problems force a choice.
Retrieval problems show up differently, but they usually lead to the same decision: diagnose the bottleneck, validate a new approach, or rebuild the stack.
Customers miss relevant products, attach suffers, and paid traffic has to compensate for poor discovery.
Teams spend hours finding information that should take minutes because retrieval quality is inconsistent or untrusted.
Copilots, RAG, and agentic systems break in production when the retrieval layer cannot ground answers reliably.
Retrieval engineering for systems that can't afford to fail.
These are the capabilities behind our core engagement paths: audit, proof of concept, production build, and Vespa-focused modernization.
Agentic & AI-Native Retrieval
Search Modernization
Relevance & Revenue
Capabilities buyers look for when the stack starts to constrain delivery.
Buyers describe commerce search, enterprise RAG, or agentic AI. Underneath, it comes down to one question: the right retrieval foundation.Searchplex designs the retrieval layer that powers all three.
AI Search & Discovery for Commerce
Enterprise RAG Systems
Agentic AI: Built on Reliable Retrieval
Search at the center of the business.
Where retrieval quality directly affects revenue, productivity, or answer quality.
Commerce & Discovery
Retrieval and ranking that converts high-intent traffic and supports margin-aware discovery at scale.
↑ Conversion · Revenue per visit · AOVLegal & Regulated Knowledge
Search over authoritative content where precision and recall carry compliance weight.
↑ Researcher throughput · Answer qualityEnterprise & Workplace
Internal knowledge retrieval that improves self-service containment and reduces duplicated effort.
↑ Containment · Agent productivityCustomer Support
Retrieval systems that ground AI-assisted support and deliver consistent answers across channels.
↑ First-contact resolution · CSATNews & Media
High-velocity content retrieval and relevance systems for publishers serving time-sensitive audiences.
↑ Engagement · Content discovery · RecencyFinance & AI-Native Products
Precision retrieval for financial data and product teams building retrieval-dependent experiences from the ground up.
↑ Accuracy · Scalability · Evaluation disciplineCase Studies.
A select set of production engagements showing how Searchplex operates in practice.
CuratedAI: From Semantic Search to Hybrid Multilingual Legal Retrieval
Splore AI: Elasticsearch to Vespa for Semantic and Hybrid Retrieval
Latest blogs.
How AI Can Turn Your Publishing Archives Into a New Source of Engagement
Surface hidden gems from your archive, automatically, right before you hit publish.
Introducing Find & Mind: The Search & AI Meetup for Netherlands
Launching Find & Mind, a new Amsterdam-based community meetup for Search, IR, RAG, and Applied AI.
Berlin Buzzwords 2025: What the Conversations Revealed
Search infrastructure, AI, and what changed in real conversations at Berlin Buzzwords 2025.
Searchplex starts from the retrieval problem and chooses the architecture accordingly.
Vespa.ai is our preferred platform for production builds because it unifies lexical retrieval, vector search, ranking, ML serving, and real-time updates in one serving system.
We are a certified Vespa Project and Implementation Partner.
Choose the right starting point
Some teams need a Search Stack Audit. Others need a focused POC, a greenfield build, or Vespa-specific support. Start where the constraint actually is.