AI is already in your workflow. The question is whether you understand where and how.
Ask anyone–across industries–and they’ll tell you AI is here. And it’s here to stay.
Even in firms with compliance standards and sensitive data, everyone is using AI for something.
It might not be formal. It might not be firm-wide. But it’s there. Maybe you use it to draft emails, beef up your summarized notes, or speed up routine work before your next call.
Instead of fighting the giant, why not make it work for you?
As we know, most firms aren’t struggling to use AI, but they are struggling to see where it’s being used.
A quick summary tool here. A note-taking app there. Maybe an advisor pastes something into ChatGPT or Claude to save time.
Individually, none of this feels risky. But together, it creates a lack of visibility.
Do you know who on your team is using AI? For what? Can you answer where that information goes afterward?
When we built Praktikant, we talked to people in firms from the ground up. We work in the industry every day. We learned the answers to those difficult questions.
No one is actively trying to cut corners, but we are all interested in saving a little time.
AI is awesome at that–it reduces manual work and speeds up repetitive tasks. But without a defined process, everyone ends up using it a little differently.
One advisor documents notes one way. Another does it differently. Follow-ups might be captured in one place or not at all.
That inconsistency creates risk over time, especially when firms need to demonstrate how work is handled.
We made Praktikant standardize workflows–all our notes, tasks, and communications are captured and organized in a consistent way, without requiring advisors to change how they work.
For compliance teams, the concern isn’t just that AI is being used. It’s whether that use can be explained.
If an examiner asks how your firm uses AI, would the answer be clear?
Or would it depend on who you ask?
We know who to ask–our developer. That’s right, even though we’ve developed an AI product, we can go right back to a person to ask how things work and where information is stored. (Hint: securely.)
By organizing activity and creating a clear record of what happened and what followed, Praktikant helps firms move from uncertainty to clarity. That makes it easier to answer questions, demonstrate consistency, and move forward with confidence.
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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.
AI isn’t taking suggestions anymore. It’s taking action.
The newest generation of tools doesn’t just draft emails or summarize documents. It completes tasks. Opens accounts. Drafts emails. Reconciles complex data. Prepares trades.
For most businesses, that’s incredibly productive.
For RIAs, it’s a little more complicated.
AI’s shift from assistant to agent isn’t a small promotion–it’s a categorical change.
And while we all hope AI agents show up in work, we all *don’t* want to do like:
These are exactly the kinds of tasks where mistakes carry real consequences.
The difference between “helpful” and “risky” is not the agent itself.
It is what sits around it.
Before any system takes action on behalf of your firm, three things need to be clear:
Who authorized it?
What kind of action is it taking?
And what record does it leave behind?
Without those answers, you are not automating work. You are introducing risk.
And that is where we noticed most general-purpose AI tools fall short.
They can execute tasks, but they don’t understand firm structure, workflows, or obligations. They treat every action the same without accounting for approvals, supervision, or audit trails.
That might be fine for internal productivity.
It does not work in a fiduciary business.
So, when we talked about creating Praktikant, we wanted to approach this pain point differently for our industry.
Instead of focusing only on what the AI can do, Praktikant focuses on how that work is controlled. Actions are scoped. Higher-risk tasks require approval. Every step is captured and recorded.
Our goal isn’t about making AI more powerful–it’s about making it accountable, too
