---
title: "Announcing Fin Apex: The age of vertical models is here"
description: "Fin announces Apex 1.0, a custom model trained by its AI Group for customer-service conversations, replacing frontier-lab models as the core answering model for nearly all English-language chat and email traffic. The post says Apex improves resolution, speed, hallucination rates, and cost, citing one gaming customer’s resolution rate rising from 68% to 75% overnight and describing a 22% reduction in unresolved conversations. Its development relies on proprietary evaluations and billions of human and agent interaction data points generated by Fin’s resolution engine, creating a training flywheel for further domain-specific improvement. The launch is presented as evidence that customer-service applications may need to become full-stack AI companies rather than relying on shared general-purpose models. It also argues that frontier labs face pressure to offer cheaper specialized models as post-training and task-specific data become more important."
---

# Announcing Fin Apex: The age of vertical models is here

[Intercom](https://yomu.fyi/company/intercom) · Eoghan McCabe · Mar 26, 2026

**Type:** Announcement

## Summary

Fin announces Apex 1.0, a custom model trained by its AI Group for customer-service conversations, replacing frontier-lab models as the core answering model for nearly all English-language chat and email traffic. The post says Apex improves resolution, speed, hallucination rates, and cost, citing one gaming customer’s resolution rate rising from 68% to 75% overnight and describing a 22% reduction in unresolved conversations. Its development relies on proprietary evaluations and billions of human and agent interaction data points generated by Fin’s resolution engine, creating a training flywheel for further domain-specific improvement. The launch is presented as evidence that customer-service applications may need to become full-stack AI companies rather than relying on shared general-purpose models. It also argues that frontier labs face pressure to offer cheaper specialized models as post-training and task-specific data become more important.

## Context

Fin had used frontier-lab models, including GPT versions and Sonnet 4.0, as the core answering model for its customer-service agent. The post argues that general-purpose models may overserve specific use cases and that proprietary evaluations and domain data can provide durable differentiation.

## Approach / What changed

Fin’s 60-person AI group trained Apex 1.0 using proprietary customer-service evaluations and billions of human and agent interaction data points generated by the Fin resolution engine. Apex became the core answering model for nearly 100% of English-language chat and email conversations.

## Takeaways

- A gaming customer’s resolution rate increased from 68% to 75% after Apex was introduced, which the post describes as a 22% reduction in unresolved conversations.
- Apex is described as faster, less prone to hallucinations, and cheaper than other available models, alongside its higher customer-issue resolution rate.
- The post argues that domain-specific post-training, rather than pre-training alone, will drive specialized models and pressure frontier labs to offer cheaper models for particular tasks.

**Tags:** [AI](https://yomu.fyi/topic/ai), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning)

- Source: [Intercom](https://www.intercom.com/blog/announcing-fin-apex-the-age-of-vertical-models-is-here)
- Source URL: https://www.intercom.com/blog/announcing-fin-apex-the-age-of-vertical-models-is-here
- Ingested by Yomu: 2026-09-09T08:22:12.527Z

[Read original post](https://www.intercom.com/blog/announcing-fin-apex-the-age-of-vertical-models-is-here)