---
title: "en:twine vs SPAIK: which AI partner fits?"
description: Compare AI agencies SPAIK and en:twine. SPAIK offers a fixed 4-week
  programme and AI training for whole teams. en:twine chooses custom work over a
  package.
lang: en
canonical: https://www.en-twine.ai/en/ai-agencies/spaik/
updated: 2026-10-02T17:19:37.000Z
---
"Which AI agency fits us?" is a difficult question, because almost everyone promises the same thing: AI that actually delivers value. SPAIK and en:twine are close in that promise. Both revolve around adoption, around AI your team actually uses, and both work for SMEs. Yet they take a different route to that goal.

## Where SPAIK is strong

SPAIK has turned implementation into a product. The Kickstart delivers a business case, a proof of concept, and a team that understands the subject, within four weeks, at a fixed price. On top of that, AI Fundamentals trains entire teams in AI literacy, with the EU AI Act woven in. For an organisation that wants to get started quickly, predictably, and at scale, that is a useful, proven formula.

## How en:twine differs

en:twine does not deliver a standard product or a fixed package. We start with your organisation: where does work get stuck, what information does your team lack, where do errors arise, and where is there enough value to put AI to serious use? This is not only about saving time. It can also mean better output, more reliable processes, new services, or better management information because systems and data sources start talking to each other.

We only choose the technology once it is clear which process needs to work better. Then we connect to the systems and data sources you already use. That can be within the Microsoft ecosystem, through integrations, or with targeted connections to your CRM, ERP, documents, and databases. The goal is a process that demonstrably runs better.

Adoption is part of that work. We start by mapping the opportunities with the [AI Readiness Scan](/en/ai-readiness-scan/), then design and build the solution, and adjust it as your team starts working with it. This keeps AI from remaining an isolated experiment and makes it part of a process that runs measurably better.