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It takes months to train a good Tech Support agent, and turnover in this field is notoriously high. Some studies report a staggering 37% turnover rate (Source: The Quality Assurance & Training Connection), while the best figures I've seen still show over 13%, which is alarmingly high. (Source) Support organizations invest in training, only to see agents leave once they become valuable.

So, what can be done?

Imagine having a low-cost, in-house expert that could assist every new tech support agent. With Artificial Intelligence, that's possible—and it won’t break the bank with 'token costs' to OpenAI or Google. Plus, you don't have to worry about sending your private data to these organizations. Because, if needed, we can build you a powerful AI agent using entirely open-source Large Language Models (LLMs).

You Don’t Need the AI to be Customer Facing to get an Immediate ROI

You might also be concerned about allowing customers to interact directly with an AI. What if it does something unexpected?

There’s a lot of potential in the idea of creating an AI Tech Support Agent that could eventually handle the job of human agents. This could cut staffing costs and offer your customers 24-hour tech support at a lower price. And if that’s what you're aiming for, we can certainly help. But what I’m proposing as a first step is something simpler and far more affordable.

I'm talking about an open-source, low-cost AI that every new human agent can interact with—an AI that already knows everything about your products. When a new tech support agent encounters a customer issue, they can chat with the AI, describe the problem, and the AI will quickly pull up the most similar cases from your tech support database. It will then summarize them and suggest solutions for the agent to offer the customer.

With this AI, the cost of training effective human tech support agents drops significantly. Your agents become valuable almost immediately, while your experts are freed up for more complex tasks. Plus, the AI helps train new staff through real-time use. The return on investment is clear.

A Real Life Example of an AI Tech Support Agent

For example, here is an example query used with one of our actual AI agents:

“Vehicle is a 2004 Ford Excursion (2003 engine). The customer is complaining about a rough idle and there are a couple of codes that are set that talk about the high-pressure oil system. They are using an 85200 product and running at economy mode for better fuel consumption.”

After typing that our AI Agent comes back with a summarized list of similar issues:

Case Id: 8767

Customer's Ford has gone through multiple high-pressure oil pumps. Edge product is not likely the cause, as it would set codes if there was an issue with pressure.

Case Id: 42254

Customer experienced rough idle and rough start, with P1233 and P0191 codes indicating fuel pump and fuel line pressure issues. Recommended updating the device and checking the cold air intake.

Case Id: 36396

Customer's truck had a high idle, reaching over 700 RPM. Issue was resolved by reinstalling EGT sensors in the exhaust.

Case Id: 7750

Customer's truck had high and rough idle while running on 100% vegetable oil with an Evolution device. Recommended updating the Evo and checking injector health.

Case Id: 57533

Customer's truck had a high idle for seven months.

Then, using that list, it puts together a punch list of things to have the customer try to resolve their issue:

1. Update the product to the latest version. (36396, 7750, 57533)
2. Check for any updates to the product's firmware. (36396)
3. Check the cold air intake. (42254)
4. Reinstall the EGT sensors back into the exhaust. (36396)
5. Check for any other potential hardware failures on the vehicle. (8767)

Notice how the AI references the actual cases so that the human can refer to them or look them up in their support system.

Finally, the AI summarizes its suggestions in a friendly way for the human agent:

“Based on the information provided, it appears that the customer's vehicle may have a problem with the high-pressure oil system. There are two codes set that talk about the high-pressure oil system: P0191 and P1233. These codes indicate that the fuel pump and fuel line pressure are out of range. Similar problems in the past have been resolved by updating the device software (Case Id: 57533). In other cases, the issue was resolved by reinstalling the EGT sensors back in the exhaust (Case Id: 36396). Therefore, I recommend that the customer update the device software and reinstall the EGT sensors back in the exhaust.”

The AI Agent not only has access to your current support database—allowing it to reference similar cases—but it also has access to all of your technical manuals. This means it can instantly search your entire support database as well as your tech manuals for solutions to the user's problem. It can then display or summarize the relevant information, helping your support agents provide faster and more accurate assistance.

For example, here is a real line from a manual that explains how to troubleshoot common problems:

This manual has been added to the AI’s database. When we inform the AI that a user is reporting their camera is frozen, here is the response it provides:

“Based on the user guide, a possible cause of the camera picture freezing is bad connections. To troubleshoot this issue, it is recommended that you cycle the key off and on, and double check the fuse and connectors.”

A Low Cost Open-Source AI Solution

Building an AI Agent like this can be accomplished using primarily open-source models. If needed, we can run the AI locally, ensuring that your private data never leaves your environment. This way, you aren’t sending sensitive information to OpenAI, Google, or any external service—everything stays securely behind your own firewall.

Alternatively, we can train your in-house developers to build something like this themselves. Mindfire TECH offers a wealth of free AI tutorials and sample code on our blog, giving your team the tools and knowledge to get started. This way, you can create and maintain your own AI solutions in-house, with full control over the process.

I recently gave a presentation on our low-cost open-source AI stack to an AI Special Interest Group. You can check out the slide deck here (Link), which provides much more detail on how we developed this affordable AI solution.