Building an AI Customer Support Assistant That Actually Escalates to Humans
We started InBoxIA because we kept hearing the same problem from small business owners: they're drowning in customer messages across WhatsApp, Instagram, and Messenger, but can't afford to hire a full support team.
The interesting part about building this wasn't the AI itself—it was figuring out how to build an AI that knows when to shut up and call a human.
The Problem We Solved
Most chatbots are terrible because they either:
Try to answer everything, creating frustrated customers
2. Transfer everything to humans, defeating the purpose of automation
We built InBoxIA to handle the 70% of routine questions (availability, pricing, reservations, policies) while intelligently escalating edge cases to your team.
How It Works
You configure InBoxIA with your business info, and our AI gets trained on your specific context. When customers message, the AI:
Understands their intent
- Checks if it's something it can confidently answer
- Responds automatically for routine stuff
- Escalates conversations that need human judgment
What We Learned
The hardest part wasn't the LLM integration—it was building the confidence scoring system. A customer asking "How much do you charge for X?" seems simple, but our AI needs to know if there are edge cases or exceptions before answering.
Why It Matters
We're currently helping service businesses (salons, restaurants, gyms) handle ~60% of their customer inquiries automatically. That's real time back in your team's day.
We're still early, and we'd love to hear what would actually make your life easier. What's your biggest customer support pain point?
It is a major point of friction for service businesses when a chatbot provides an incorrect price or policy because it failed to recognize a nuanced exception, leading to a defensive customer service situation later.
The real innovation here is the focus on the "escalation trigger" because the value of an AI assistant isn't just in the questions it answers, but in its ability to protect the brand by handing off a sensitive or complex conversation to a human before the customer gets frustrated.
Are you finding that the confidence scoring needs to be adjusted based on the specific industry, such as being more "cautious" with medical or legal service inquiries compared to restaurant reservations?