AI, but make it human
Designing automation that feels like someone cared.
Designing automation that feels like someone cared.
Customers are confused.
Sales conversations are dying.
Your team doesn't trust the answers.
And you're paying for resolutions that aren't actually resolving anything.
That customer was ready to buy. The AI just kept sending links.
It's hurting sales instead of helping them.
Handoffs are messy.
Trust is eroding — with your customers and your team.
Now your team has a new job too: apologizing for the AI.
You wanted it to work like your best rep.
Instead, it's answering without listening.
Here's what most teams don't realize: Fin doesn't just create these problems. It makes them visible.
The content gaps you didn't know were there. The complexity in your processes that your team navigates instinctively, but Fin can't. The context a human would read between the lines — but Fin has no way to guess.
Let's teach it how to care — for your customers.
"I just about cried when I read this. Sherice, you're a stellar strategist and I'm so grateful we have your help through this."
— Justine Bloom, Hailey Happens Fitness
Work that works for you — not the other way around.
A wellness brand had done everything right — built a loyal community, created thoughtful programs, and genuinely cared about their customers' experience. But Fin was pulling from content it was never meant to answer from, and because it sounded confident, customers didn't always question it.
The content simply hadn't been structured with AI in mind. Nobody had taught Fin where to stop.
We rebuilt the foundation — reorganized around how customers actually ask questions, created clearer boundaries, and built guidance that helped Fin respond with more care and less assumption. Now when something's nuanced, Fin knows when to hand it to a human.
A client's team lead reached out looking for chat etiquette resources. She'd already looked online — "be friendly," "respond quickly" — nothing that would actually change how someone shows up in a conversation. Some of the reps didn't come from a sales background, and when conversations turned toward a purchase, they didn't always know how to move with it.
I wrote a relational framework instead of a script, then connected it to their Intercom workspace and ran it across real conversations. Every agent got a personalized review — what they were doing well, where to grow, and which parts of the playbook to focus on.
Their most direct rep saw her CX score climb 16 points in under two weeks. And in the data, we found revenue conversations already happening — customers were signaling interest, the team just needed help recognizing those moments.
A client wanted better visibility into what customers were asking about. With hundreds of products, tagging would mean agents remembering an extra step every time. So we set up AI-driven categorization instead — Fin assigns attributes automatically, agents just confirm and close.
Once the data started flowing clean, something showed up: 60% of support volume was concentrated around one issue. It had been there all along, just buried under messy data.
I flagged it and wrote a small set of macros to handle it. Their team lead said: if these had been in place months ago, think of how much time we would've gotten back.
A software company turned on Fin, watched customers get confused and sales conversations stall, and pulled it back. They thought Fin was the problem.
When I looked under the hood, a lot of what was being counted as "AI resolutions" wasn't actually AI answering questions. The content Fin was drawing from had gaps, outdated information, and no clear structure. Fin wasn't failing. It had never been set up to succeed.
Their head of support put it in the simplest possible way: to go on vacation, come back, and trust that the conversations marked resolved actually were. That's what we're working toward.
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