If you build enterprise SaaS with any kind of permissions model, you've heard this feature request before: "Our admins need to see what a user sees." The motivation is always the same — a user reports they can't access something, or they're seeing the wrong data, and the admin has no good way to verify what's going on from the user's perspective.

The workarounds your customers resort to are painful. They ask their users for screenshots, or schedule screen shares, but these are high-friction interactions. They try to reproduce the issue with test accounts, but test accounts never have the same permissions, the same data, or the same organizational context as the real user.

So sooner or later, the feature request lands on your backlog: let admins log into the app as another user — see the same dashboards, the same data, the same permissions. Even before users complain about missing access.

But the moment you say "admin impersonation" in an enterprise context, alarm bells go off. Can the admin modify the user's data? Can they access private information? Is there an audit trail? How do you prevent this from becoming a backdoor?

These are fair concerns. At Pigment, we built a user-facing impersonation mode that lets admins review what users see safely. It's been in production for a while now, and our customers love it. Here's how we designed it.

We’re delighted to announce that Mélissa Djuimo and Matthieu Allegre, two of our Engineering Program Managers (EPM), were invited to the Tech Lead Corner podcast to share their experience and discuss the specific EPM role at Pigment⁠.

🎧 Tech Lead Corner | Mélissa Djuimo and Matthieu Allègre - Being EPM at Pigment.

At Pigment, we provide a central platform for organizations to model and plan using their most valuable data. As part of that mission, enabling predictive insights is key. Our first implementation of predictions worked well for smaller datasets, but as more users began applying the feature to larger and more complex data models, we quickly hit the limits of a single-machine setup in terms of memory and compute.

To address this, we redesigned our infrastructure around a distributed Dask cluster, allowing us to scale horizontally and support much larger workloads. In this post, we’ll share the architecture of our new solution and key takeaways from adopting Dask in production.

Agentic AI, in which one or multiple AI agents can pursue goals autonomously or collaboratively, represents a new frontier in generative AI. Unlike traditional large language models (LLMs) that operate independently, these systems combine specialized agents with unique roles to handle complex, open-ended tasks. Here, we’ll explore how Pigment uses this approach for data analysis, empowering clients to gain deeper insights.

Unit tests, integration tests, end-to-end tests — like many modern development teams, at Pigment, we rely heavily on these methods to minimize the risk of functional regressions in our codebase. In this post, we'll explore how we added image snapshots to our Cypress end-to-end tests as an extra safeguard for catching visual regressions.