AI development that reaches production.

What we build

LLM systems, agents, and pipelines that change how the business runs.

The work worth doing is rarely a chat box. It is everything around the model: the data it reads, the permissions it inherits, the decisions it is allowed to make on its own, the point where a person signs off, and what happens when it is wrong.

• Agent systems that own a process end to end, from the trigger to the outcome.
• Company brains: permission-aware retrieval across the documents, repositories, and systems a business already runs on.
• Production pipelines that ingest, enrich, score, and route work that used to move through a room of people.
• Generative AI built into the product itself, where it changes what you can sell rather than what you can demo.

How we work

A small pod moves faster than a large AI transformation programme.

Our default shape is one or two engineers who can reason from business intent to production code. We start with the process that costs you the most, work out where AI changes its economics, and get a real version running against real data early enough for people to argue with it.

• Name the process worth rebuilding before choosing models or vendors.
• Get a working version in front of the people who run that process, early.
• Add evaluation, observability, permissions, and failure handling before it carries load.
• Stay after launch, because usage is where the rest of the design gets decided.

Why Algosoup

AI-native speed without outsourcing the product brain.

Many AI agencies sell demos. Many consultancies sell decks. We sell engineering ownership. The pod is embedded enough to shape decisions live and technical enough to ship the software itself.

• Direct access to the engineers building the system.
• Judgment and implementation held in the same heads.
• A bias toward systems that carry production load over systems that demo well.
• Clear tradeoffs when AI is the wrong tool for the job.