Data isn’t your constraint. Disconnected context is.
Every few weeks, a new AI model drops, and the industry scrambles to keep up. The benchmarks improve. The reasoning gets sharper. The demos get more impressive.
For RIAs, living in the trenches, most of that is noise.
The limiting factor at your firm is not whether the model is smart enough. It is whether it knows anything about your business.
Today’s models can already draft memos, summarize documents, and explain complex strategies. What they cannot do on their own is understand your clients, your data, or your workflow. Without that, even the smartest model is just a capable outsider.
A brilliant generalist who has never seen your CRM is not a better advisor. It is a very helpful stranger.
That gap is where most AI tools fall short.
Every meaningful question an advisor asks depends on the context that lives across systems: portfolio data, meeting notes, emails, CRM entries, and compliance records. None of that is visible to a general-purpose chatbot unless it is manually provided.
It’s not an intelligence problem. It is a context problem. Solving it requires more than a better prompt.
It requires a system that can pull the right data from the right places, apply the right permissions, and present it in a way the model can actually reason through. It also needs to reflect how work actually happens at an RIA, from client meetings to investment committee decisions to follow-ups and documentation.
When we were looking for a tool like that, one didn’t exist.
So, we decided to talk to someone and build Praktikant.
Instead of asking advisors to move between systems and assemble context themselves, we wanted to connect the tools firms already use and bring that information together. We still use systems like Addepar. Practifi, and Monday, but we’re able to take the information from them and use it in way more actionable ways.
Praktikant makes our workflow a single, connected process. Notes, data, and communications are no longer scattered. Outputs are tied to inputs. Actions follow insights.
And importantly, there is a clear record of what happened and why.
This is not about making AI smarter. It is about making it useful.
