Outmatic
Solutions
We build custom software and AI systems, from full platforms to generative features, and ship them to production.
“We've cut the time it takes to issue a work permit, reduced the margin for error, and replaced the old signing devices with more modern tools. Outmatic took the time to understand our industrial context before the technology, and that's where the difference shows”
What we build
We work across a few areas, and most projects touch more than one of them.
We build generative features into products where there's a clear use for them: agents that do real work inside your systems, copilots that sit next to your team, or a language model handling something a person used to do by hand. We pick the right model for the job, connect it to your data and tools, and treat evaluation, deployment, monitoring and guardrails as part of the build — not an afterthought.
We automate the workflows that eat up people's time, from routine back-office steps to long chains that run across several systems. When work spans multiple tools, services or AI agents, we handle both process orchestration (sequencing steps, routing, retries and failures, keeping a person in the loop) and agent orchestration (coordinating several agents and the tools they call). Some of it uses AI and some of it doesn't, it depends on what the process actually needs.
We build search, recommendation and RAG systems that answer from your own data. That covers vector and hybrid retrieval, ranking models trained on your content, and the pipelines that keep everything fresh. Answers come with citations back to the source, and inference stays fast as the data grows.
We also build plenty of software with no AI in it at all. Web and backend systems, integrations between tools that were never meant to talk to each other, the APIs and platforms that hold it together, and the slow, careful work of modernizing things that have aged badly, without stopping the business that depends on them.
We build generative features into products where there's a clear use for them: agents that do real work inside your systems, copilots that sit next to your team, or a language model handling something a person used to do by hand. We pick the right model for the job, connect it to your data and tools, and treat evaluation, deployment, monitoring and guardrails as part of the build — not an afterthought.
Our platforms
Over time we have built two platforms of our own that speed up the search and agent work, and clients can build on top of them.
AI search & retrieval
Our search and retrieval platform. It runs on Vespa and handles vector and hybrid search, trained ranking, and RAG retrieval across very large document sets.
AI agent platform
Our agent platform. It covers deploying agents, versioning them, watching what they do, and keeping a person in the loop, all in one place you can audit.
Outmatic is an official Vespa Implementation Partner. Vespa is the AI search engine behind Perplexity, Spotify, and Yahoo, battle-tested at billions of documents. We deliver Vespa deployments end-to-end: architecture, integration, ranking, production handover. Our team created and maintains the .NET SDK for Vespa, the client that brings this engine into the .NET ecosystem.
Discuss a Search ProjectHow we work
01
Problem definition
We talk to the people close to the problem and write down a clear statement of it before any design starts.
02
Solution landscape
We look honestly at the options, whether that means building, using tools you already have, buying something, or not building at all.
03
Signed scope and milestone build
We agree what is in and what is out, note the assumptions, and break the work into milestones with proper change management.
04
Handover and knowledge transfer
We hand over the code with architecture documentation, and we can train your team if that helps.
FAQ
Quite often, no. AI is worth it when you are working with language, messy unstructured data, ranking or prediction, or a volume of work that cannot be done by hand, and when you can measure whether it is doing its job. When none of that applies, a simpler and cheaper approach usually wins, and that is what we will suggest.
It depends. Sometimes the quickest route is starting from one of our platforms, Clevora for search or Daish for agents. Sometimes it is better to build from scratch, and sometimes you do not need to build much at all. We work that out with you during discovery.
Products with generative and language-model features, agents and automation, search that answers from your own data, and AI added into software you already run. We also build a lot that has no AI in it.
We decide up front how we will judge whether it is good, and we keep testing against that as we build. We also build for production from the start, so things like speed, cost, and monitoring are handled properly instead of being left to the end.
Ready when you are
Let's start from what you're trying to solve.
The first conversation is a real assessment, not a pitch. Tell us what you're trying to solve, and we'll be direct about whether to build from scratch, start from one of our accelerators, or do nothing at all.
Talk to us