Home

Our Work

AI Front Desk

Success Story

AI-Powered Front-Desk Assistant for Automated Visitor Support

A cloud-native GenAI solution that personalizes interactions and reduces manual workload.

60%

reduction in receptionist workload through automation

Personalized

chat-based assistant for clients, candidates, researchers & support.

Technologies

Vertex AI

BigQuery

Dialogflow CX

Cloud Run

Firebase

LangChain

LlamaIndex

Chroma DB

Expertise

AI/ML

Gen AI

Cloud

Have a Similar Problem?

Explore how AI-driven assistants can streamline your front-desk operations and enhance visitor engagement.

Contact Sales

Need for personalized user experience

The goal was to build an automated front desk solution to deliver a highly personalized user experience to site visitors and boost conversion rates.

Heavy reliance on human receptionists

Routine inquiries placed an unnecessary load on human receptionists and slowed interactions.

Accurate information across visitor types

The solution aimed to provide relevant and accurate information about the company to various types of visitors, including potential clients, candidates, and researchers.

Have a Similar Problem?

Explore how AI-driven assistants can streamline your front-desk operations and enhance visitor engagement.

Contact Sales

Zoolatech is a senior-heavy engineering firm with Silicon Valley roots and a Miami HQ, specializing in legacy modernization, system re-architecture, and AI deployment to drive long-term, compounding value.

2017

Year Founded

600+

Employees

96%

Client Satisfaction

Phase 1

Cloud model flexibility

To support rapid experimentation and avoid lock-in, the team began by establishing a model-agnostic foundation. This ensured the assistant could switch between LLM providers without architectural changes, enabling cost optimization and future adaptability.

Phase 2

Data storage foundation

A centralized data environment was prepared to support fast retrieval and consistent responses. This gave the assistant reliable access to curated knowledge and ensured information remained current across all visitor interactions.

Phase 3

Site assistant structure

The team then designed a multi-assistant architecture to route each inquiry accurately. A supervisor component evaluated incoming messages, analyzed intent, and dispatched them to task-specific assistants, enabling highly targeted and relevant responses for different audience segments.

Phase 4

Chat integration

The assistant was integrated into the website via a persistent chat widget, ensuring visitors could engage with it from any page without interrupting their browsing session.

Phase 5

Site data scraper

To guarantee accurate answers, a background scraper was introduced to continuously gather and embed site content into a vector store, providing the assistant with up-to-date context and eliminating manual data maintenance.

Phase 6

User experience and interaction flows

New conversational logic and fallback flows were added to ensure smooth interactions. Visitors could request real-human support at any time, and the assistant would provide appropriate contact options. This reduced friction and created a predictable, trustworthy conversational experience.

Phase 7

Cost optimization and scaling preparation

The team incorporated automated routing rules and lightweight model selection logic to reduce operational costs. This allowed simple queries to be handled by lower-cost models while reserving larger models for complex or sensitive requests.

Zoolatech built an AI-powered front-desk solution that automated routine inquiries, cut manual front-desk workload by 60%, and enabled consistent, personalized visitor support at scale.

Flexible cloud solutions

LangChain was used to abstract the specific model and cloud, allowing for flexible switching between models and avoiding dependency on a particular cloud provider. Simple tasks could be delegated to basic models, reducing the cost of their execution. What's more, this solution can be deployed as a pre-built solution in various clouds, avoiding vendor lock-in.

Data storage and knowledge management

BigQuery was used as a data warehouse for storing the corporate database.

Site assistant design

The Site Assistant consists of a Supervisor and four types of topic-related assistants:

Supervisor: Analyzes incoming messages from users and decides which type of assistant will handle them.
Potential clients assistant: Addresses questions from potential clients.
Potential candidates assistant: Handles inquiries from job candidates.
Researchers assistant: Responds to questions from researchers.
Technical support assistant: Provides technical support information.

If a user wishes to communicate with a real person, the Assistant will provide the appropriate contact information. In case a message is not related to Zoolatech, the Assistant will provide a standard fallback message to inform the user that their question cannot be answered.

Chat integration

The chat window is integrated into the site as a widget, accessible throughout the site.

Site data scraper

The site data scraper fetches information from the Zoolatech site, processes it, and stores it in a vector-type database.

Option	Risk	Mitigation
Incorrect or irrelevant AI responses	The assistant could deliver inaccurate information or misroute visitors if not properly governed.	Clear topic boundaries, supervised routing logic, and fallback mechanisms ensured controlled and reliable outputs.
Model or cloud dependency issues	Relying on a single cloud provider or model could introduce high costs or operational constraints.	A cloud-agnostic architecture enabled flexible model switching and avoided vendor lock-in.
Data freshness and knowledge accuracy	Outdated website or documentation content could lead to incorrect answers.	A site scraper and centralized knowledge base kept information continuously updated.
Overload on human staff if the assistant fails	Poor automation quality could push more questions back to human reception.	Standardized routing and assistant specialization reduced failure cases and preserved human time.

The new big data platform improved operational visibility, strengthened analytics capabilities, and enabled ML-driven optimization across the retailer’s value chain.

Reduction in routine front-desk tasks

The solution helps automate routine front desk tasks and reduces the load on human receptionists by approximately 60%.

Flexible, cost-efficient deployment model

A flexible solution was achieved in terms of cost (optimum cost-value) and adaptability to different types of cloud environments, depending on the client’s need

Ready for use across organizations

It is used as an in-house solution and is also offered to clients as a pre-built solution that can be easily integrated into their own business ecosystem and customized to their specific needs.