What are Fin AI sequences? A practical guide for 2025

Not too long ago, if you worked on a finance team at a company like Uber, getting a simple answer about revenue was a whole ordeal. It meant digging through complicated SQL queries, jumping between different platforms, or getting in a queue for the data science team's help. These delays weren't just annoying; they slowed down important business decisions.

This is where a new type of specialized AI agent is starting to make a difference. We’ll call them Fin AI Sequences: AI-powered systems that run through a series of steps to answer a financial question or get a task done. Think of them as a smart assistant that doesn't just fetch information, but actually works through a problem for you.

This guide will walk you through what these systems are all about. We'll look at where they’re being used, from personal budgeting apps to big company analytics, and talk about the real challenges you should think about before adopting them.

Breaking down Fin AI Sequences

First things first, "Fin AI Sequence" isn't some off-the-shelf product. It’s more of a concept describing an AI-driven workflow built for a financial job. Under the hood, they usually mix a conversational Large Language Model (LLM) with a bunch of tools and actions to accomplish a goal.

Let's look at a few examples to make this less abstract:

• For your own money: A tool like Sequence lets you create rules that automatically split your paycheck into different savings "pods" for taxes, rent, or a holiday fund. It's a simple sequence: money comes in, rules are applied, money moves.

• For big company analytics: Uber’s internal tool, Finch, lets finance analysts ask questions in plain English, like, “What was the Gross Bookings value in the US & Canada in Q4 2024?” The AI agent then runs its sequence: find the right database, write the necessary code, run it, and deliver a specific number.

• For AI-powered billing: The way Intercom's Fin AI handles its pricing is a great example. Customers only pay when the AI actually solves their problem. The sequence here involves understanding the issue, finding a solution, confirming it worked, and only then triggering the charge.

A look under the hood of modern Fin AI Sequences

These systems can feel like magic, but they’re built on a pretty logical, step-by-step structure. Uber's agent, Finch, gives us a good blueprint for how they're put together.

The supervisor: Figuring out what you want

This is the part that deciphers your request. When you ask a question, the supervisor’s job is to figure out what you really mean. It looks at your request and decides the best way to handle it. Are you asking for a specific number? Trying to move money around? Or just need a definition? The supervisor points the request to the right tool for the job.

The knowledge layer: Connecting to your data

An AI is pretty useless without information. This layer is all about connecting the agent to the right data sources. For a financial agent like Finch, that means hooking into data warehouses, analytics platforms, and other financial systems.

This idea of pulling information from all over the place isn't just for finance. While a Fin AI agent connects to databases, an internal support agent from a platform like eesel AI connects to your company’s collective brain, no matter where it’s stored. It can pull answers from Google Docs, Confluence, and old support tickets to give any team an accurate answer, not just the finance folks.

The action engine: Doing the actual work

This is where the "sequence" part of the name comes from. Once the AI understands the request and has access to the data, it starts doing things. That could be writing and running a SQL query, shifting funds between accounts, or pulling a specific clause from a legal document.

In a customer support setting, an AI agent from eesel AI would follow a similar set of steps: use an API to look up order details, tag the ticket correctly, and then draft a personalized reply for a human agent to quickly check over. It's all about breaking a big job into smaller, automated steps.

Where Fin AI Sequences are used (and where they go wrong)

These AI sequences are showing up in more and more places, but they come with their own set of real-world headaches. Knowing the common uses and the challenges that come with them is the key to deciding if they’re a good fit for you.

Use case 1: Automating personal and small business finances

Tools like Sequence want to put your finances on autopilot. You can set up rules to manage your cash flow, put money aside for taxes, and pay off debt without thinking about it. It’s a compelling idea.

But there’s a downside.

As people on platforms like Reddit have noted, handing over control of your money to a third-party app can be nerve-wracking. Users have reported awful customer service, a lack of clear communication, and in some really worrying cases, having their accounts shut down with barely any warning.

Reddit

Use case 2: Building custom agents for big companies

At the other end of the scale, large companies like Uber are building their own AI agents to sort out their internal data chaos. We're also seeing benchmarks from places like vals.ai that test an agent's ability to do heavy lifting, like digging through SEC filings.

The main problem here is that this is a slow, expensive, and difficult path. Building a tool like Finch isn't a weekend project; it requires a full-time team of AI engineers, data scientists, and product managers. It's a massive undertaking that can take months (or even years) and cost millions in salaries and infrastructure. For most companies, that’s just not practical.

Use case 3: Streamlining internal financial questions

Let’s be honest, finance teams spend a ton of time answering the same questions again and again. "What's our travel policy?" "How do I get approval for new software?" "Where's the P&L report from last quarter?"

The answers are usually scattered across dozens of documents on different platforms. You could try to build a custom Fin AI agent to fix this, but there's a much simpler way. Platforms like eesel AI are designed to solve this exact problem right out of the box. You connect your knowledge sources from Confluence, Google Docs, and PDFs, and you can launch an "AI Internal Chat" agent in Slack or Microsoft Teams. The best part? You can have it working in minutes, not months. It gives your finance team a secure way to let people help themselves, without the cost and complexity of a custom build.

An eesel AI chatbot streamlining internal financial questions directly within Slack.

How to choose the right Fin AI Sequences for your needs

So, you're convinced that AI could help your finance workflow. What now? You generally have three options: build a custom solution yourself, buy a niche tool that does one thing, or use a flexible AI platform that you can set up on your own.

The table kind of speaks for itself. For most teams, a flexible AI platform is the smartest way to go.

Platforms like eesel AI take a lot of the risk out of trying AI. You can connect your knowledge sources and launch an AI assistant without writing any code. You can even simulate its performance on past questions before you go live, so you have a good idea of how it will behave. You can start small by automating just one or two tasks and then expand as your team gets more comfortable. It's a completely different world from the high-stakes gamble of a custom build or the tight constraints of a niche tool.

The eesel AI platform allows users to simulate performance to understand how Fin AI Sequences will behave before going live.

Fin AI Sequences: Automation is the future, but how you get there matters

Fin AI sequences are more than just a buzzword; they’re a real shift in how financial work gets done. They are changing everything from how we handle our personal budgets to how big companies analyze their performance.

But whether an AI project succeeds isn't just about the technology, it’s about the implementation. The hurdles of building custom tools are huge, and the risks that come with single-purpose apps are very real.

For the majority of teams, especially finance departments drowning in repetitive internal questions, a flexible, self-serve platform is the quickest and safest way to get value from AI. It's about finding a solution that fits with the tools you already use and gives your team a boost from day one.

Start automating your internal support today

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