Intercom

With @fin_ai resolving a growing share of your support volume, you need a new way to plan for the work that lands with your team.

Workforce Management, built with Intercom Helpdesk, forecasts schedules and plans around total demand, with full visibility into what AI resolves and what requires human support.

Here’s what Workforce Management can do:

1 Forecasting to predict the demand that will reach your team, and is built from real conversation data with Fin-resolved volume automatically excluded.

2 Centralized staffing to help to build and maintain your team’s schedule, with reusable shift templates and two way Google calendar sync.

3 Teammate coverage views that map your scheduled headcount against required staffing hour by hour, so you can plan to match demand before the queue fills up.

4 Team Performance that shows how well your team delivers against their planned responsibilities, with individual scorecards for adherence, conformance, and shrinkage.

Learn more about Workforce Management via the link in the replies

Tickets are where support work goes when it can’t be resolved in one reply, like a refund that needs finance or a bug that needs engineering.

We recently shipped nine improvements to tickets. These updates are designed to improve the teammate, customer and cross functional teams experiences from the moment a teammate creates a ticket, to the way teams track that work and customers follow its progress.

Three of the updates:
→ Fin autofills in the ticket title, description, and attributes from the conversation, so creating a ticket stops being a second round of data entry
→ A new lifecycle report shows how tickets move between teams, where they loop, and how long they sit with each one
→ You choose which status updates are worth an email, so customers stop tuning out the ones that matter

Live for everyone today. Watch the full product updates video at the link in the comments.

There’s a reality that’s starting to take shape: for some teams, AI is already handling the majority of customer conversations.

At our recent Fin x @AnthropicAI meetup, we shared how this is already happening in practice. Fin now resolves over 80% of support queries end to end. The technology is here, but adoption is still catching up.

That progress is unlocking something bigger. Fin is moving beyond support toward a broader vision, where one agent can deliver a consistent, high-quality experience across the entire customer lifecycle, from day zero to year ten.

Watch more below to hear how close we are to that future, and what it takes to get there.

Most AI Agents treat product catalogs like documents.

That breaks when a shopper asks for anything with real constraints (like price, size, or availability) – because the system can return something that looks relevant but doesn’t actually satisfy what the customer asked for.

Shopping queries have structure: price limits, sizes, stock, variants.

So the Fin AI Group built a RAG system to handle both semantic intent and hard constraints in one system.

This new system powers Fin for Ecommerce, a new role for Fin the Customer Agent

How it works ↓
fin.ai/research/struc…

“Trust has been a multi-year journey.”

At our recent Fin x @linear meetup, we explored what it actually takes to build trust in AI Agents, and why it starts with giving systems room to prove themselves, limiting their blast radius early on, and gradually expanding their role as customers see what they can reliably do without risk.

Watch more below to hear how teams are measuring and earning that trust in practice.