Why a Ground-Up Approach is Necessary

Let’s face it, if your software was built more than a year ago, it’s old. Once LLMs hit the market, everything before it immediately became outdated.

A purpose-built tool already has all of its logic and algorithms baked in. Adding AI on top may make them more efficient, but it will never fully leverage the power that AI could offer. You will always have half measures. Let’s drill into some of the differences


Existing tools already have prescribed workflows so any added automation will only align with those embedded workflows.

New tools don’t have those constraints. Automations are free to be intelligent enough to move through the smartest workflow based on all given context unique to that specific task or contact.

Data-Driven insights

Adding AI to an existing tool will certainly make it easier to pull data and analyze it to spot trends. But, again, it is only analyzed within the confines of what you have already told it to look for.

By removing those restrictions and, instead, feeding the proper context, AI is free to spot and surface all kinds of patterns that humans wouldn’t even think to look for. And, by giving the bots the freedom to do what they are really good at, they will not only provide reactive analytics, but predictive analytics as well.

It is these hidden and predictive insights that have the potential to make a tremendous impact on a business.

Context Limitations

Adding AI to a single tool necessarily limits its capacity to spot trends across information sources.

Instead, the AI needs to be fed data across all sources so that it can spot problems and identify opportunities, regardless of where that information lives and regardless of whether it is structured or not.

Atteria is solving these problems and more. We are going back to first principles and starting with a completely clean slate to imagine what work looks like without constraints and with all of your business context.

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