Official Vespa.ai Partner

Build production retrieval systems on Vespa.ai

We design and build new Vespa-based systems and focused production extensions: retrieval, ranking, and serving that carry search, recommendation, or AI workloads beyond a demo.

Run a focused Retrieval PoC
OverviewAuditMigrateBuildExtendOptimizeSupport
Build path

Build the right Vespa system the first time

We work with teams building new retrieval systems on Vespa when the architecture needs to be right from the start. That can mean greenfield search, a new recommendation stack, or an AI-native system where retrieval, ranking, and serving need to hold up in production.

Confident to move ahead?

Move into a production build

Design and implement a new Vespa-based retrieval system with the architecture, ranking logic, and delivery rigor needed for production.
Need proof first?

Run a focused Retrieval PoC

Validate the architecture on your data before committing to a full production build.
Retrieval PoC →
Already on Vespa?

Add focused build extensions

Extend an existing Vespa system with custom behavior, integrations, or new retrieval capabilities as part of the build path.
Focused Vespa extensions →

Replacing Elasticsearch, OpenSearch, or Solr? That is a migration. Build is for new systems. Not sure Vespa is the right direction? Start with the Migration Readiness Audit.

What we build

Systems we build on Vespa

New Vespa systems, and focused extensions to existing deployments.

AI Search & Personalization

Build hybrid search and personalized ranking on Vespa for product discovery, recommendations, and enterprise knowledge search.
Explore AI Search & Personalization →

Visual Search

Combine image similarity with text and catalog filters on Vespa to power image search, similar products, and saved visual alerts.
Explore Visual Search →

Enterprise RAG

Build retrieval pipelines on Vespa that surface current, authorized evidence for assistants, copilots, and knowledge systems.
Explore Enterprise RAG →

Agentic AI

Give agents a Vespa retrieval layer that supplies relevant, authorised evidence across multiple steps.
Explore Agentic AI →

Visual search: GEM. A Vespa-based inverse image-search system that matches incoming listings against saved visual searches across 200M+ listings. Read the case study →

RAG: CuratedAI. Hybrid multilingual legal retrieval on Vespa across EU and Belgian legislation in English, French, and Dutch. Read the case study →

“Searchplex delivered a hybrid, multilingual search solution that elevated our document retrieval system, resulting in faster & more precise searches across multiple languages.”
Luca Campanella, CTO, CuratedAI
“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.”
Jon Bratseth, CEO, Vespa.ai
How an engagement runs

From architecture to production

Stages are scoped to the project. A team with a settled approach can skip ahead.

1. Architecture and scope

Agree the use case, data shape, ranking approach, and success criteria. Where a critical assumption is unproven, a focused PoC tests it first.

2. Build

Ingestion and connectors, content understanding pipelines, and hybrid retrieval and ranking, built as one system rather than assembled afterwards.

3. Evaluate and tune

Relevance evaluation and tuning designed in from the start, so quality is measured against your queries before launch.

4. Serve and launch

Low-latency search APIs and deployment workflows for production, on Vespa Cloud or self-hosted, with latency, cost, and monitoring engineered in rather than added after launch.

After launch we can stay involved through optimization, extension, or production support.

Why Searchplex

Why teams bring Searchplex into Vespa builds

01

Retrieval engineering, not generic app development

We design production retrieval systems around ranking, relevance, and serving behavior, not just feature delivery.
02

Grounded Vespa implementation experience

Official Vespa partner, delivering Vespa work in production since 2023, including visual search over 200M+ listings and multilingual legal retrieval.
03

A clear path after launch

Optimization, extension, or production support once the system is live, or we operate it for you.
FAQ

Vespa build — common questions.

What teams typically ask before committing to a new Vespa implementation.

Related resources

Case study

GEM: Saved Image-Search Alerts on Vespa

How Searchplex built and shipped saved image-search alerts on Vespa as a new capability inside GEM’s existing secondhand-fashion search product.
Read case study →
Use case

Enterprise RAG for Workplace Knowledge

A production pattern for permissioned, versioned, and changing knowledge spread across documents, tickets, messages, and systems.
Explore use case →
Guide

When AI Search Becomes an Architecture Decision

A framework for recognizing when retrieval has become a shared production foundation rather than another application feature.
Read guide →
Ways of working

Work with us the way that suits your team

Either way, you own the result, and the aim is a team that can run it without us.

Project delivery

Searchplex delivers the project

Our engineers handle day-to-day delivery within Searchplex. We meet regularly with your business and leadership stakeholders to share progress and agree on key decisions.
Embedded

Specialists paired with your engineers

We lead the retrieval work inside your team and processes, so the expertise stays with you.
Next step

Moving to production on Vespa?

Searchplex helps teams move from concept to production system with a clearer retrieval foundation, stronger implementation choices, and a realistic delivery plan.

Vespa build — greenfield search, RAG systems, recommendation engines, and production rollout.
Run a focused Retrieval PoC