By Leonid Kukuyev, Founder of Red Rose City Tech

A Technology Solutions feature piece from the 2025/2026 Thriving Magazine issue!

Customer service, staff support, and workflow automation with today’s LLM tools.

Large language models (LLMs) are the engine behind many of the new “smart” features showing up in software — tools that write, summarize, or answer questions from a plain-English request. For a small business, that means faster customer responses, quicker marketing content, and easier access to your own information. The pattern is the same: let the model do the routine language work so employees can focus on the parts you can’t automate.

Areas of Opportunity

Customer-Facing Chatbots and Assistants

An easy starting point is customer service. Add an AI chatbot to your website or ordering page to handle the questions you get every day — hours, location, inventory, bookings, order status. Most people have seen these already: an LLM lets the bot understand normal questions and reply clearly, which means faster answers for customers and less inbox time for staff.

Internal Knowledge Assistants

The same idea works inside the business. In retail, tech, or service settings, an LLM can sit on top of your employee handbook, HR policies, new-hire training, and store procedures so staff can ask, “What’s the return policy on sale items?” or “How do I log inventory adjustments?” and get an answer right away. That cuts down on manager interruptions and enables staff to unblock themselves. In healthcare settings, a private assistant can surface visit notes or care instructions quickly and even draft parts of the note, so more of the visit is spent with the patient instead of in the EMR.

Marketing and Content Creation

Generative AI is effective at repetitive content: social posts, promo emails, product descriptions, event writeups. A local restaurant, boutique, or nonprofit can keep a steady posting schedule by having AI draft the first version and a human adjust tone and local references. That’s how you stay visible without adding staff hours.

Two Ways to Add AI to Your Stack

Most businesses will do one of two things: put an AI assistant in front of customers/staff, or let AI help run a process in the background. OpenAI’s AgentKit is used for the first case — you define what the assistant can do (answer from your data, call a tool, complete a task), give it a chat-style interface, and monitor it, which is ideal for support or “chat with our docs.” n8n is used for the second case — you draw the workflow (“when an email comes in → have the LLM classify it → send leads to the CRM, send support to tickets”) and n8n runs it. AI is one step inside the process, not the whole product.

Implementation Considerations

• Data/security: if you handle sensitive info, use business/enterprise or hosted models; HIPAA-friendly setups exist but cost more.
• Start simple: prototype non-sensitive tasks with public models, harden later.

• Integration is the lift: connecting AI to your site/CRM/EMR is where tools like n8n help.
• Keep supporting documentation current: chatbots/RAG systems can only answer from up-to-date documents.

About Red Rose City Tech

Red Rose City Tech helps Lancaster-area organizations turn the current wave of AI into real, everyday improvements. That means looking at how work actually gets done — customer requests, internal emails, reporting, content creation — and using AI to speed up the parts that are repetitive or slow. It could be customer-facing responses, internal knowledge access, or content and documentation — the focus is always on getting tangible productivity gains quickly. The goal is to enable teams to spend more time on the higher-value work that grows the organization.

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