How LISA Hockey Cuts Support Workload by 50% with Fini

From burned-out support leader to strategic product partner, in just two weeks.

Snapshot

• Company: LISA – The Dutch platform powering Hockey, Tennis, and Padel federations.

• Challenge: Seasonal spikes and tools that couldn't handle multi-brand context.

• Resolution Rate: 51% of tickets now handled completely by Fini, no human needed

• Time Saved: 50% reduction in manual support hours (from ~80 to ~40 hours/week).

• Implementation: Live in < 2 weeks.

About the Company

LISA is the digital backbone for major sports federations. If you play Hockey, Tennis, or Padel in the Netherlands, you likely use LISA to book courts, check match schedules, and manage team memberships.

The Breaking Point

Danique didn't become Head of Support at LISA to be a human copy-paste machine.

But that's exactly what the job had become.

LISA powers the sports life of the Netherlands. If you play Hockey, book a Tennis court, or check your Padel schedule, you're using LISA. That means support never stops. When Hockey season winds down, Tennis season kicks in. There's no break. Just an endless queue of questions.

The worst part? Most questions already had answers in the Help Center.

"How do I add a member?"

"Where's my schedule?"

"Why can't I book a court?"

The answers were right there. But people wouldn't read them. So Danique and her team spent 80+ hours every week typing the same responses over and over.

She wasn't managing support. She was trapped in it.

With three brands (Hockey, Tennis, Padel) and no time to breathe, Danique couldn't do the work she was actually hired for: making the platform better.

Why Previous "Solutions" Failed

LISA tried to escape this nightmare twice before.

First attempt: Zendesk's built-in bot

It was so complicated that setting it up became a full-time job. The team couldn't keep up with the constant tweaking it needed just to work.

Second attempt: Other AI tools

These tools got the brands confused. They'd give Hockey answers to Tennis players. They'd mix up rules between sports.

In a world where federations have strict regulations, giving the wrong answer isn't just annoying, it's dangerous.

Both times, the team gave up and went back to doing it all manually.

Why Fini Was Different

Danique needed two things:

1. Accuracy: The AI had to get the brands right, every time

2. Support: She didn't have a tech team or extra hours to build and fix things

Fini delivered both.

Built for Accuracy: The RAGless Difference

Here's where most AI tools break down.

Traditional AI chatbots work by searching through your help articles every time someone asks a question, grabbing a few "relevant" chunks of text, and then writing an answer based on those fragments. It's like having a fast reader who finds the right page but doesn't really understand your business.

That's why they confuse brands, mix up policies, and sound confident while being completely wrong.

Fini works differently. It uses what's called a "RAGless" approach:

Instead of frantically searching documents every time, Fini first takes your entire knowledge base, every help article, every policy document, every brand-specific rule, and compresses it into a structured, organized system that actually understands your business.

Think of it this way:

• Other AI tools are like a librarian who has to search for the right book every single time

• Fini is like a senior support specialist who already knows your entire operation by heart

For LISA, this meant:

• Hockey questions got Hockey answers, never Tennis or Padel

• Regional rules stayed separate (Dutch Hockey vs. Belgian Hockey)

• Policy differences between sports were crystal clear

• Multi-brand context was automatic: "Can I use my Hockey membership for Tennis?" gets a different answer than "Can I book a Tennis court with my Hockey account?"

• No mixing. No mistakes. No brand confusion.

The AI didn't just sound like a trained LISA agent. It thought like one.

And because Danique controlled exactly what knowledge went into Fini's system, she could trust it to never go rogue or make up answers.

Why RAGless Matters for Multi-Brand Operations

LISA's three-brand setup (Hockey, Tennis, Padel) is exactly where traditional AI chatbots collapse.

With document-based systems, every question triggers a search across all your content. That means:

• A Hockey question might pull Tennis policies if the wording is similar

• Seasonal rules get mixed up between sports

• Brand-specific edge cases slip through

Fini's compressed knowledge layer keeps everything organized by brand, region, and context. When someone asks a question, the AI already knows:

• Which brand they're using

• What rules apply to their specific situation

• Which policies are universal vs. brand-specific

This is why Fini could handle LISA's complexity in two weeks while other tools spent months and still got it wrong.

A True AI Partner, Not Just Another Vendor

Most AI vendors ask for your help docs and call it a day. Fini learned LISA's business like a new hire would.

Week 1: Learning LISA's World

The Fini team spent the first week understanding how LISA's three brands actually worked:

• "When can Hockey members also book Tennis courts?"

• "What happens if someone's payment fails during peak season?"

• "How do Belgian federation rules differ from Dutch ones?"

They weren't configuring software. They were becoming LISA experts.

Week 2: Refining Together, Not Apart

Instead of going live immediately, Fini ran alongside the support team. Every AI response was reviewed by Danique before being sent. When edge cases appeared, the team jumped on Slack to refine the logic together, often within the hour.

By the end of week 2, Fini went live with a crucial advantage: it was already learning from real support interactions, not just static help docs.

By the end of week 2, Fini went live, trained on LISA's nuances, not generic help articles.

Onboarding That Actually Works

After trying 4-5 different AI agents, Danique had low expectations. Most vendors promise support but disappear after setup.

Fini was different.

The onboarding wasn't just fast, it was hands-on. The Fini team stayed engaged throughout implementation and beyond, treating LISA's success as their own. When questions came up, responses came in minutes. When edge cases appeared, the team worked through them together in real-time.

It wasn't just about getting to "go-live." It was about getting it right.

As Danique put it: "Speed is nice, but your support during and after implementing was for me the main thing."

Months later, that partnership continues. In Danique's words: "We truly have a new partner. Not a supplier. Big difference." That's what turned a two-week implementation into a system that keeps improving and a relationship that keeps delivering.

What It Actually Feels Like

The support queue isn't a war zone anymore. It's calm.

The team isn't typing the same answers on repeat. They're handling complex issues that actually need human judgment.

And Danique? She's finally doing the job she signed up for: making LISA better for hundreds of thousands of athletes across the Netherlands.

The AI Gets Smarter Over Time

Here's what surprised Danique most: Fini keeps improving without her having to do anything.

When the team does encounter an edge case that Fini can't handle, they resolve it normally. Fini's system watches that resolution, learns from it, and handles similar cases automatically next time.

For example, there was a complex scenario about membership transfers during federation changes. The first time it happened, Danique's team handled it manually. The second time? Fini resolved it automatically, following the exact same logic.

No one updated help articles. No one "retrained" the AI. Fini had simply watched and learned.

This is what self-learning actually means: Your best agents are indirectly teaching the AI just by doing their jobs.

Outcome it generated

The impact was immediate. Fini didn't just deflect tickets; it resolved them, effectively removing the "noise" from the queue.

• 51% Resolution Rate: Fini now fully resolves over half of all incoming inquiries without human intervention.

• 50% Reduction in Workload: The team went from spending ~80 hours a week on the queue to ~40 hours.

• Leadership Time Reclaimed: Danique personally saved ~30 hours a week. She shifted from answering tickets to focusing on product strategy and improving the platform.

• Zero hallucinations since launch. No cases reported without policy adherence.

In Danique's Words

💡 "I've tried 4-5 AI agents for this, and Fini is 10 out of 10 with onboarding. Simply put, it was absolutely outstanding. Speed is nice, but your support during and after implementing was for me the main thing. They act like an extension of our team. We've cut our weekly support hours in half, and I finally have time to focus on improving our product instead of just answering tickets.." — Danique, Head of Support at LISA

Is Your Team Trapped Too?

If you're spending hours answering questions that are already documented...

If you're drowning in "simple" tickets while important work sits untouched...

If you've tried other AI tools that either don't work or give wrong answers...

You're ready for Fini.

👉 Book a demo to see how we can give you 40+ hours back every week.