If you run a transport company, you're juggling a lot: orders coming in, drivers and subcontractors to coordinate, invoices to send, and customers chasing ETAs every five minutes. So, Qargo built an intelligent transport management platform to handle all of this; think of it as mission control for logistics companies.

But running operations is only half the picture. Transport operators also need to see what's happening across their business, i.e. where in the workflow are delays, empty miles, or admin tasks eating into margin?

That’s why Qargo wanted analytics built directly into their platform, sitting alongside the operational tools their customers use every day.

Embedded analytics is harder than it looks 

BI tools are typically built for internal use, not for embedding inside a customer-facing product. When you try, you quickly run into these challenges:

• Dealing with thousands of end users instead of a small internal team

• Figuring out multi-tenant data where customer A never sees customer B’s data

• Trying to manage users or licences per customer

• Difficulty building a seamless UX

• Pricing that explodes when you add new users

Most BI tools fall apart on at least one of these requirements (usually more). 

Why Qargo chose Lightdash

Before Lightdash, Qargo embedded dashboards using Superset. It worked, but the UX didn’t meet customer expectations. Updating or adding dashboards took too much time, and the team needed a solution built for both embedded analytics and internal BI.

Qargo evaluated alternatives and landed on Lightdash because it solved their biggest problems, starting with the most important:

1. Multi-tenant data security

Qargo runs a multi‑tenant architecture: all customer data sits in shared BigQuery datasets, separated by a tenant_id column. Every query must be filtered correctly, or users could see other customer data.

This is where most embedded BI solutions force you into annoying workarounds. Maybe you create separate dashboards per tenant (which doesn't scale), or manually add filters to every chart (which is error-prone), or build a complex middleware layer (which is time-consuming).

Lightdash makes this simple:

• Qargo defines SQL filters in dbt using: WHERE tenant_id = {{ user_attributes.tenant_id }}

• Qargo’s backend includes the tenant ID in each embed token

• Lightdash automatically applies the filter to every dashboard and every query

If you add a SQL filter to a dbt model, it automatically applies to every dimension or metric from that model. This makes it easy to maintain permissions and prevents accidentally forgetting to apply a filter on a specific chart.

“From a security point of view, it’s quite nice. You can enforce that all the data is filtered correctly without having to make changes in all different places.”

This is the kind of thing that sounds basic but is actually really hard to get right, and it's the difference between embedded analytics that works and embedded analytics that keeps you up at night worrying about data leaks.

2. Token-based authentication

Lightdash’s embedding features mean that Qargo doesn’t need to manage a separate user-sync and license model. Instead, Qargo generates short‑lived JWT tokens when a user opens a dashboard.

Here's what happens:

1. User logs into Qargo

2. They click on the dashboards tab

3. Qargo's backend generates an embed token (valid for maybe 30 minutes)

4. Lightdash loads the dashboard filtered to the right tenant

5. The user sees their data, securely filtered to just their company

“We define the filter parameters in the token, which are automatically applied across all tables. From a security point of view, it’s a really effective way to ensure data isolation.”

With Lightdash, there’s no need for license management. 

3. Self-hostable

Qargo wanted to control their deployment and avoid giving third‑party tools direct access to customer data. So, Lightdash’s self‑hosting option fit perfectly.

This flexibility matters when you’re embedding analytics into a product that processes operational data at scale.

4. Deep dbt integration

Qargo already used dbt, so Lightdash’s dbt‑native approach meant their existing models became analytics-ready with almost no extra work.

5. Dashboards-as-code

With Lightdash, dashboard definitions live alongside dbt code. Qargo can change models and dashboards in the same pull request, review everything together, and deploy confidently.

“Dashboards as code means we have model changes and dashboard changes in the same PR. From a dev workflow, it’s really nice.”

6. Pricing that scales with usage

Most Qargo users check dashboards occasionally, not all day. So, Lightdash’s enterprise usage‑based embedding model made financial sense for them. There were no exploding costs as their product grew.

8. Modern, intuitive UX

Lightdash’s modern dashboard UI improved customer satisfaction among Qargo’s customers compared to previous solutions. 

7. Open source

Being an open source platform, Lightdash gave Qargo full control over their data and architecture. 

"There weren't that many BI solutions out there that supported both of our use cases in the way we wanted. The fact that Lightdash is open source helped, and we wanted something we could host ourselves to avoid a third party getting access to all of our customer's data."

8. Embedded dashboards that feel native

Qargo uses Lightdash's React SDK to style their embedded dashboards to match their platform. They can tweak colors, adjust spacing, and control the layout so dashboards don't look out of place in their app.

The impact 

The migration over to Lightdash was smooth for Qargo. Since they already used dbt, it only took one person on their team ~2 weeks to migrate over to Lightdash.

They’ve seen improvements across the board. Dashboard usage among customers has increased by approximately 50% since switching to Lightdash and half the company is using Lightdash for internal use.

The improvements extend beyond numbers though, Qargo's customers really  like the dashboards now: 

“Since switching to Lightdash, customer feedback has completely flipped. They love the modern interface, the filters actually make sense, and we can respond to feature requests without it being a whole engineering project. It's made embedded analytics a strength of our platform instead of a pain point."

Date filters in particular made a huge difference. It's one of those small UX things that compounds when you have users who aren't SQL experts and just want to see "last month's numbers" without thinking too hard.

There’s a big operational win too. Creating and updating embedded dashboards is faster, which means Qargo can respond to customer requests without it being a whole engineering project every time.

Their data team can iterate quickly. Push changes to production with confidence. Spend less time fighting with BI configuration and more time actually building useful features for their customers.

What’s next for Qargo?

Qargo is exploring AI‑powered embedded analytics. They want customers to ask questions like “Which trips were most profitable last quarter?” and get instant answers without Qargo needing to build dozens of dashboards. That's the promise of AI for embedded analytics, and it's something Lightdash is actively working on.

For now, Qargo's focused on adding new dashboards and refining what they've built. The embedded setup is working, their customers are happy, and their engineering team isn't spending all their time fighting with BI configuration - which is exactly how embedded analytics should work.

Want to embed Lightdash in your own product?

Learn more about Lightdash’s embedding capabilities on our website or in our docs.

Want to talk about how Lightdash can help your team move faster, cleaner, and smarter? Just book a demo with a member of our team to start your free trial.