AI development that reaches production.

What we build

LLM apps, AI workflows, and product features that create leverage.

The useful AI work is rarely just a chat box. It is the workflow around the model: permissions, data retrieval, evaluation, escalation, human review, product UX, and production monitoring.

• LLM applications and copilots for internal teams or customers.
• Generative AI features embedded into existing SaaS, fintech, or operations products.
• AI agents and workflow automation where autonomy is useful and bounded.
• Retrieval, structured data pipelines, and integrations with the systems your team already uses.

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 product intent to production code. We start with the business workflow, identify where AI changes the economics, then ship the smallest useful version quickly.

• Scope the first valuable workflow before choosing models or vendors.
• Build a working prototype fast enough for stakeholder and user feedback.
• Add evaluation, observability, data handling, and failure modes before launch.
• Stay close after launch so the product improves as usage exposes reality.

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 product.
• Product judgment alongside implementation, not a ticket queue.
• A bias toward production-ready systems over impressive prototypes.
• Clear tradeoffs when AI is the wrong tool for the job.