“Basedash closed the gap between having data and actually using it in day-to-day decisions.”

Purpose is a tech-driven financial services company managing billions in assets, whose businesses span asset and wealth management and small business financing. Founded by entrepreneur Som Seif and headquartered in Toronto, Purpose is reinventing financial services across a family of businesses — from outcome-oriented investment management to a wealth-as-a-service platform for independent advisors and a financial operating system for small businesses.

A company with that much surface area generates a constant stream of operational questions. As part of a broader transformation to make internal operations more efficient, more data-driven, and AI-native by default, Purpose went looking for a tool that could put those answers within reach of every team — not just the people who write queries.

“The problem we set out to solve was the gap between having data and actually using it in day-to-day decisions.”

Before Basedash

Like most established financial services firms, Purpose had no shortage of data. What it had was friction: traditional reporting workflows that put a process between a business question and a useful answer. Getting from one to the other meant queues, handoffs, and turnaround times that made smaller questions not worth asking.

That friction was exactly what the company’s transformation effort set out to remove. The goal wasn’t another BI tool layered on top of the existing workflow — it was to make data exploration feel faster, more intuitive, and more embedded in how teams work every day.

“We found Basedash interesting because it aligned with a core part of our transformation at Purpose: re-imagining our internal operations to be more efficient, more data-driven, and AI-native by default.”

Why Basedash

Purpose evaluated Basedash against a clear set of selection criteria: speed, usability, AI-native exploration, governance, and the ability to move from business question to useful answer without the friction of traditional reporting workflows. For a regulated financial services company, governance carried real weight — self-serve access only works if it comes with the right controls.

Basedash fit. The conversational interface meant anyone could go from a question to a useful answer in one sitting, and the exploration model was AI-native from the ground up rather than an AI feature bolted onto a legacy tool.

“Our selection criteria focused on speed, usability, AI-native exploration, governance, and the ability to move from business question to useful answer without the friction of traditional reporting workflows.”

A modern product partner

One factor in the decision wasn’t on the feature checklist at all: the Basedash team itself. Through the evaluation and rollout, the team’s responsiveness, pace of shipping, and willingness to quickly fix issues stood out — the working relationship felt less like adopting another vendor tool and more like partnering with a product team that moves at the same speed Purpose does.

“A key factor was also the Basedash team itself: their responsiveness, pace of shipping, and willingness to quickly fix issues made it feel like we were working with a modern product partner, not just adopting another tool.”

Impact

Today, Basedash is used broadly across teams at Purpose as part of the company’s AI-native way of working:

• Teams across the company explore operational data directly, instead of routing questions through reporting queues
• Practical views and analysis get created by the people who need them, in the flow of their day-to-day work
• The distance from business question to useful answer dropped from a workflow to a conversation
• Data exploration became embedded in how teams work every day, advancing the company’s AI-native transformation

“Today, Basedash is used broadly across teams at Purpose to explore operational data, create practical views, support analysis, and help teams move from questions to answers much faster.”