How Magnific added AI support to its existing Salesforce setup with Fin

Following a quick and easy setup, Fin is now involved in 68% of all customer conversations at Magnific and fully resolves 40% of all support volume (around 18,000 conversations every month) without any human involvement. The team's support reps still use the same Salesforce case routing and workflows as before. Nothing was ripped out. Fin just integrated seamlessly with their existing setup.

With Fin working so smoothly, the next phase is already in motion: expanding action-based flows, widening coverage, and putting Fin's procedural capabilities to work so that any team member, not just engineers, can configure and adapt the system in real time.

Magnific is not a traditional SaaS company adding AI as a feature. The company has spent years building generative AI tools (image generators, video tools, upscalers, character systems) at a scale that now serves more than 100 million users and nearly one million paying subscribers. AI is the product.

That context shaped everything about how the team approached customer support. As the product suite expanded from stock content into AI creative tools, support became more complex alongside it. Billing questions became more nuanced. AI credit consumption needed context. Subscription tiers multiplied. And the volume of these queries continued rising year-over-year.

The support team's mandate was to professionalize the function and scale it, not by adding headcount but by doing more with what was already there. The aim was to provide a better service with the resources the team already had, rather than to adopt AI for the sake of AI.

For a company with AI at its core, there was an expectation set by the COO and investors that the support operation reflected the product's ambitions.

The existing model was heavily email-based. It worked for structured queries, but it introduced friction: back-and-forth threads to clarify issues, slow resolutions, and no way to gather context quickly. Live human chat at scale wasn't feasible without a much larger team. The only realistic path was deeper automation, leveraging a system that could answer questions and take action.

Salesforce was already central to how Magnific ran support. Cases came in through email-to-case and web-to-case. Billing integrations with Chargebee and Stripe were live. Refund automations were already built.

Chema had spent five years building out that infrastructure. Any AI tool the team brought in would need to fit into it.

Magnific ran a structured comparison between Fin and the other AI options it had already been testing, including the lightweight AI chatbot it had been running. The evaluation criteria weren't about features in isolation. The team wanted to know which tool they could configure and maintain without sustained engineering effort to handle their full operational reality that consisted of informational queries, backend actions, and reporting visibility.

The setup process showed the difference quickly. "With Fin, you just need to put the help center URL and it will scrape the knowledge base, and you can select the articles you want," says Chema. From there, adjustments could be made by anyone.

Within a few days of integration, Fin was answering at the quality the team had been working toward for months. It was fast to implement and easier to maintain, and team members without an engineering background could change things themselves.

For Chema, who had spent years inside Salesforce's architecture, the integration was the bigger surprise. "Fin is an external system, so we expected the integration to add complexity. But it felt like Fin was inside Salesforce," he says.

Fin also didn't require Magnific to rebuild their operating model. Support reps stayed in Salesforce. Case routing continued to work as before. Handoffs from Fin landed in the existing Salesforce queue. The Chargebee and Stripe integrations the team had spent years building remained in place. There was no rip-and-replace. Fin connected on top of the existing helpdesk rather than competing with it.

That mattered. Years of work had gone into the Salesforce setup, and Fin let the team build on it.

A mindset shift

Support teams are often afraid that tools like this will take over their jobs, and Magnific's team shared that concern at first. Once Fin was live, they saw it was there to help. What changed was where their time went.

Routine interactions, like templated email responses, information gathering, and repetitive FAQ queries, were handed over to Fin, and human time focused on issues that required judgment, context, and experience. The team didn't shrink. Its work changed.

Action-based flows are expanding beyond billing and subscription management. Coverage is increasing across more surfaces. Guardrails for more complex financial and account scenarios are being refined. And the team is preparing to use Procedures, which let teams describe a process in plain language for Fin to follow, to open configuration up to the whole team.

Magnific's support operation runs around the clock. But the people who know it best, who understand the customer questions, the edge cases, the moments where guidance needs updating, aren't always the ones who can change the system. That gap is what the next phase is designed to close. If more people can safely manage and adjust Fin, the team can improve faster.

The shift is already underway. "We have a lot of functional people who are not focused on the technical part, and it is very easy for them to use the platform," says Chema.

That's where the trajectory points: a support operation where the team that knows customers best is also the team that can shape the system that serves them.