Transforming veterinary & healthcare support with intelligent, 24/7 AI guidance.

AI Chatbot for Patient Care Using RAG Architecture

Client Background

Veterinary clinics and partner pet stores faced an increasing number of patient queries:

  • Minor but repetitive questions

  • Basic symptom checks

  • Emergency concerns

  • Follow-up requests

  • Medication reminders

Staff members were overwhelmed, and pet owners often experienced delays.

The client wanted a smart, always-available assistant that could reduce workload, improve response times, and support patients with reliable guidance based on doctor-approved documentation.

a white robot with blue eyes and a laptop
a white robot with blue eyes and a laptop
man in blue crew neck shirt covering his face
man in blue crew neck shirt covering his face

The Problem: High Volume of Repetitive Queries & Slow Response Times

Before the AI chatbot was introduced, clinics struggled with:

  • Heavy load of recurring questions: “Why hasn’t my dog eaten today?” “Is this medication safe?” “What symptoms should I watch for?”

  • Long response times: Busy phone lines and delayed emails frustrated pet owners.

  • No structured triage: Clinics had no way to screen symptoms before a vet appointment.

  • Lack of continuity: When patients returned for follow-ups, staff had to start from scratch.

  • No 24/7 support: Night-time and weekend queries often went unanswered.

The client needed a unified AI system that provided instant support, reduced routine workload, and improved patient experience.

Solution Overview

Tenplus delivered an end-to-end RAG-based AI assistant with:

  • Automated document ingestion.

  • Embedding-based knowledge search.

  • LLM-powered conversational layer.

  • A safe, neutral recommendation engine.

  • A Streamlit front end for fast deployment.

  • Logging, audit trails, and escalation features.

Data
Analysts

Our in-house data analysts have a hands-on approach to data.

5

45

Companies analyzed

Last year, we were able to help nearly 50 companies.

person catching light bulb
person catching light bulb

Process

Step 1: Retrieval-Augmented Generation (RAG) Architecture

Step 2: Streamlit-Based User Interface

Step 3: User Journey From First Question to Follow-Up

Step 4: Technical Architecture

Step 5: Automated Alerts and Reporting

Data designers

Our designers have an extensive background in data analysis.

3

1340

Visualizations made

From presentations to marketing materials and beyond.

23

Ongoing projects

Data changes, but our team stands by your side when you need us.

Results

  • Faster Help for Pet Owners: Immediate answers instead of waiting hours.

  • Reduced Workload for Staff: Many recurring questions no longer require human intervention.

  • 24/7 Access: Support available day or night.

  • Better Patient Triage: Emergency symptoms flagged early.

  • Smooth Escalations: Doctors receive detailed conversation logs.

  • Improved Patient Experience: Pet owners feel supported and informed at every step.

yellow and white trophy
yellow and white trophy

⭐⭐⭐⭐⭐

“The AI chatbot changed the way we communicate with pet owners. It handles routine questions smoothly, flags emergencies, and gives our team more time to focus on real patient needs. Tenplus delivered a safe and reliable system that is easy for our staff and customers to use.”

Veterinary Clinic Director

We’ve helped them

We've partnered with many household brand names to deliver insights and solutions to their problems with big data.

Happy clients

Don't just take our word for it – our clients frequently stay in touch with us and work with us on future projects that require big data insights.

John Bjerrand

I work in agricultural consulting, meaning that we often need to use big and complex datasets to justify expensive investments for our clients. Lilo's consultants helped us see the big picture with their visualization tools and expertise.

Anna Claudio

As the in-house supplier manager, it's sometimes had to understand which manufacturers work best for our needs. Lilo's data analysis helped us identify where we could save and which manufacturers worked the best for us in the long term.

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