If your team spends hours a week reading documents, copying data between systems or answering the same requests, you've probably been contacted by an AI automation agency, or started looking for one. This post is for founders, COOs and operations leads who want to know what a good agency does and what to check before signing. I run SiteFusion, which builds internal tools, SaaS products and AI automation for B2B companies, so we're one of the companies you might compare (our AI automation service page explains how we work). The checklist works for whoever you hire.
What an AI automation agency builds
Most useful AI automation work is plain software with a language model in one or two steps. A typical project takes something that arrives in an unstructured form (an email, a PDF, a form with free text, a support ticket), uses a model to read or classify it, and then does something with the result in the systems you already use: creates a record in the CRM, routes a request to the right person, drafts a reply for someone to approve, or flags an exception.
The model is usually the smallest part of the build. The larger part is the integrations, the rules around the model, the screen where a person checks the output, and the logging that tells you whether it's working. If an agency's proposal is mostly about which model they'll use, ask what happens around it. Our workflow automation guide covers the kinds of processes teams usually automate first, and AI explained covers the terms you'll see in proposals.
Decide whether you need an agency at all
Some automation doesn't need custom work. If the process is simple and both systems are popular products, an off-the-shelf integration tool or a feature inside software you already pay for may do the job. That's cheaper and you can change it yourself.
An agency makes sense when the inputs are messy (scanned documents, long emails, mixed languages), when the rules are specific to your business, when the data is sensitive and you need control over where it goes, or when the automation has to sit inside an internal tool or customer portal. Our build vs buy guide goes through that decision. A good agency will ask this too, and will sometimes tell you a tool you can buy is enough.
What to check before you hire
They start with one process. Strong proposals pick one high-volume task, describe it step by step, and say how they'll measure it before and after. Be careful with proposals that promise to automate "operations" or "the back office" in one go.
They plan for a person to review output. Models make mistakes, and the mistakes are often confident. A sensible first version sends output to a person for approval, logs every correction, and only removes the review step for a category once the error rate is known and acceptable. Ask how they'll decide when that is.
They can explain the running cost. Every model call costs money, and the bill grows with usage. Ask for an estimate of cost per request, the worst case, and what limits will stop one account or one bad loop from running up the bill. We've written about how to keep LLM feature spend under control.
They talk about your data. Ask where documents and prompts are sent, whether the model provider can train on them, how long anything is stored, and who can see logs. For buyers in the EU and UK this connects to GDPR, and buyers in the Gulf may have their own data residency rules. You want specific answers in writing.
You own the accounts and the code. Model provider accounts, cloud hosting, integration keys and the code repository should be in your company's name. If the agency leaves, the automation should keep running.
They show real work. Ask for examples of automations they've built and run in production, and if possible speak to a client. Demos built for the sales call tell you much less. One of ours you can try is Farita, the AI phone agent we built to answer calls for service businesses.
How to judge a proposal
A useful proposal for a first automation usually includes:
The process in scope, with the steps and the systems it touches.
How success will be measured, using numbers you can collect today (volume per week, minutes per item, error rate).
The review step and the rule for removing it.
An estimate of monthly running costs, including model usage, hosting and any paid integrations.
What happens after launch: monitoring, who fixes things when an API changes, and how prompts and rules get updated.
If you want to check the numbers yourself, our post on measuring AI workflow automation ROI shows how to set a baseline before the build starts. For document-heavy processes, the AI document workflow automation playbook goes into more detail.
Red flags
Promises of full automation from day one with no review step.
A fixed price before anyone has looked at real samples of your documents or requests.
No answer on running costs, or "it's very cheap" without a figure per request.
The agency wants to keep the model or hosting accounts in its own name.
No plan for what happens when the model gets something wrong.
Results quoted from other clients that they can't let you verify.
After launch
AI automation needs more attention after launch than most software. Model providers change and retire models, inputs drift as your customers and suppliers change their formats, and costs move with volume. Plan for someone to review error logs and corrections regularly, and to adjust prompts and rules. That can be the agency on a support retainer or your own team if you have developers. Our post-launch support page describes how we handle this.
Frequently asked questions
What does an AI automation agency do? It designs and builds software that uses AI models to handle steps in a business process, such as reading documents, classifying requests or drafting replies, and connects that software to the systems you already use. A good agency also sets up review, logging and monitoring, and supports the automation after launch.
How much does AI automation cost? There are two costs: the build, and the running cost. The build depends on how many systems are involved and how messy the inputs are. The running cost includes model usage per request, hosting and paid integrations, and it grows with volume. Ask any agency for both before you sign.
How long does a first AI automation project take? It depends on the integrations and how much sample data is available. A first version for one process is usually a matter of weeks, with time added if access to systems or data takes a while to arrange. Ask for a plan that puts a working version with real data in front of your team early.
Is it safe to send company data to an AI model? It can be, if it's set up properly. Check where data goes, whether the provider can use it for training, how long it's retained and who can access logs. Some providers offer business terms or regional hosting, and some workflows can strip personal data before it reaches the model.
Should we hire an AI automation agency or an in-house developer? For a first automation, an agency gets you a team that has done similar work before. Once you have several automations running and a steady stream of changes, hiring someone in-house can make more sense. Many companies do both: an agency builds the first version and hands it over with documentation.
Can an agency automate a process inside a custom internal tool or portal? Yes, and that's often where AI automation works best, because the review screen and the business rules live in the same place. We build these as part of our full-stack development work.
Talk to us
If you have a process you'd like to automate and want a second opinion on whether it's a good candidate, you can book a call. If an off-the-shelf tool would do the job, we'll tell you. You can also see examples of our work in the portfolio.
