# Vsourz Digital

> We are a digital agency offering end to end technology, design \& marketing services\. 20\+ Yrs Experience\. 2000\+ Projects Delivered Successfully

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## Pages
- [SharePoint Consultancy]()
- [SharePoint Consultancy]()
- [SharePoint Consultancy]()
- [SharePoint Consultancy]()
- [AEO GEO]()

## Posts
- [How Enterprises Can Choose Between Custom LLMs and Off the Shelf AI Solutions](): \<p\>The question arrives in two very different circumstances\. In the first, an enterprise has just seen a competitor announce an AI capability that seems to give them an edge\. The instinct is to match it quickly, visibly, decisively\. The conversation in the boardroom moves fast: we need our own AI\. Custom models are discussed\. Timelines are set\. The strategic intent is real, but the decision is being made before the right questions have been asked\.\</p\>
\<p\>In the second, a team has spent several months building workflows on top of a general\-purpose AI API\. It works well enough, but cracks are appearing: the model does not understand their terminology, it produces outputs that need extensive human review, it cannot be trained on proprietary data for compliance reasons, and the cost per query at scale is making the economics uncomfortable\. The question arrives from necessity, not ambition\.\</p\>
\<p\>Both conversations end at the same place, should we build something custom, or is off\-the\-shelf the right answer? But they are asking it for very different reasons, and they should receive very different answers\.\</p\>
\<p\>This is a genuine decision with genuine consequences either way\. Getting it wrong in the direction of over\-building costs millions and delivers years of delay\. Getting it wrong in the direction of under\-building means deploying something that cannot do what the business actually needs, and eventually rebuilding it anyway, at greater cost, with greater organisational frustration\.\</p\>
\<p\>The framework that follows is designed to cut through the noise and land on the right answer for each specific situation\.\</p\>
- [How Enterprises Can Choose Between Custom LLMs and Off the Shelf AI Solutions](): \<p\>The question arrives in two very different circumstances\. In the first, an enterprise has just seen a competitor announce an AI capability that seems to give them an edge\. The instinct is to match it quickly, visibly, decisively\. The conversation in the boardroom moves fast: we need our own AI\. Custom models are discussed\. Timelines are set\. The strategic intent is real, but the decision is being made before the right questions have been asked\.\</p\>
\<p\>In the second, a team has spent several months building workflows on top of a general\-purpose AI API\. It works well enough, but cracks are appearing: the model does not understand their terminology, it produces outputs that need extensive human review, it cannot be trained on proprietary data for compliance reasons, and the cost per query at scale is making the economics uncomfortable\. The question arrives from necessity, not ambition\.\</p\>
\<p\>Both conversations end at the same place, should we build something custom, or is off\-the\-shelf the right answer? But they are asking it for very different reasons, and they should receive very different answers\.\</p\>
\<p\>This is a genuine decision with genuine consequences either way\. Getting it wrong in the direction of over\-building costs millions and delivers years of delay\. Getting it wrong in the direction of under\-building means deploying something that cannot do what the business actually needs, and eventually rebuilding it anyway, at greater cost, with greater organisational frustration\.\</p\>
\<p\>The framework that follows is designed to cut through the noise and land on the right answer for each specific situation\.\</p\>
- [How Enterprises Can Choose Between Custom LLMs and Off the Shelf AI Solutions](): \<p\>The question arrives in two very different circumstances\. In the first, an enterprise has just seen a competitor announce an AI capability that seems to give them an edge\. The instinct is to match it quickly, visibly, decisively\. The conversation in the boardroom moves fast: we need our own AI\. Custom models are discussed\. Timelines are set\. The strategic intent is real, but the decision is being made before the right questions have been asked\.\</p\>
\<p\>In the second, a team has spent several months building workflows on top of a general\-purpose AI API\. It works well enough, but cracks are appearing: the model does not understand their terminology, it produces outputs that need extensive human review, it cannot be trained on proprietary data for compliance reasons, and the cost per query at scale is making the economics uncomfortable\. The question arrives from necessity, not ambition\.\</p\>
\<p\>Both conversations end at the same place, should we build something custom, or is off\-the\-shelf the right answer? But they are asking it for very different reasons, and they should receive very different answers\.\</p\>
\<p\>This is a genuine decision with genuine consequences either way\. Getting it wrong in the direction of over\-building costs millions and delivers years of delay\. Getting it wrong in the direction of under\-building means deploying something that cannot do what the business actually needs, and eventually rebuilding it anyway, at greater cost, with greater organisational frustration\.\</p\>
\<p\>The framework that follows is designed to cut through the noise and land on the right answer for each specific situation\.\</p\>
- [How Enterprises Can Choose Between Custom LLMs and Off the Shelf AI Solutions](): \<p\>The question arrives in two very different circumstances\. In the first, an enterprise has just seen a competitor announce an AI capability that seems to give them an edge\. The instinct is to match it quickly, visibly, decisively\. The conversation in the boardroom moves fast: we need our own AI\. Custom models are discussed\. Timelines are set\. The strategic intent is real, but the decision is being made before the right questions have been asked\.\</p\>
\<p\>In the second, a team has spent several months building workflows on top of a general\-purpose AI API\. It works well enough, but cracks are appearing: the model does not understand their terminology, it produces outputs that need extensive human review, it cannot be trained on proprietary data for compliance reasons, and the cost per query at scale is making the economics uncomfortable\. The question arrives from necessity, not ambition\.\</p\>
\<p\>Both conversations end at the same place, should we build something custom, or is off\-the\-shelf the right answer? But they are asking it for very different reasons, and they should receive very different answers\.\</p\>
\<p\>This is a genuine decision with genuine consequences either way\. Getting it wrong in the direction of over\-building costs millions and delivers years of delay\. Getting it wrong in the direction of under\-building means deploying something that cannot do what the business actually needs, and eventually rebuilding it anyway, at greater cost, with greater organisational frustration\.\</p\>
\<p\>The framework that follows is designed to cut through the noise and land on the right answer for each specific situation\.\</p\>
- [Why Legacy System Migration Matters in the Age of AI](): \<p\>Many organisations still rely on legacy systems that were once reliable, stable, and central to daily operations\. Over time, these systems have become increasingly difficult to maintain, integrate, and scale\.\</p\>
\<p\>The conversation around legacy system migration is no longer limited to modernisation\. As AI continues to influence how businesses operate, analyse data, and make decisions, outdated systems are beginning to restrict growth in more visible ways\. This shift positions migration not just as a technical upgrade, but as a strategic move\.\</p\>
\<p\>To understand why this shift matters, it helps to look at where legacy systems begin to create friction for modern businesses\.\</p\>

## Categories
- [Shopify](https://www.vsourz.com/blog/category/shopify/)
- [Outsourcing](https://www.vsourz.com/blog/category/outsourcing/)
- [Magento](https://www.vsourz.com/blog/category/magento/)
- [eCommerce](https://www.vsourz.com/blog/category/ecommerce/)
- [Design](https://www.vsourz.com/blog/category/design/)

## Optional
- [Sitemap index](https://www.vsourz.com/sitemap_index.xml)