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Enabling a home rental company to build smarter support teams. Beyond mere renting, we're here to support your everyday living, hassle-free.

UI/UX Design

Data Engg & Analytics

Mobile App

Nextway Pvt Ltd. is largest home rental network in India. The company helps urban migrants in finding rental homes of their choice in cities using design and technology. It also serves as a one-stop service provider for tenants and house owners.


Real Estate

Company size

<100+ Employee

What we did


A managed home rental network that mainly deals with house owners and tenants. They collect a lot of data on each tenant and also the houses that they put on rent. One of the key problem areas is that of support. The tenants when encountered with any issue in their houses, call up the customer care helpline. This raises a ticket and is then queued to be resolved. Here’s how Synctactic AI could help

Using NLP to automatically triage the support tickets and assign them to the right departments to be resolved

Clustering houses with specific issue types and predicting maintenance needs to provide better tenant experience

Enabling chatbots to resolve simple queries and escalate more complex queries to support staff


The company uses an IVR based support system, where manual support staff would receive the call and then raise a ticket on behalf of the tenant and the concerned department would look into it. Currently, this leads to a lot of tickets piling up and without proper filtration the SLA to resolve these tickets would be breached which leads to more tickets being raised for the same issue.

Without a proper view on the issues that are prevalent in the homes, it becomes difficult to set up the operations to resolve them.

Days to process a ticket

Large number of duplicate tickets


Smart Ticket Triaging

The support platform was connected to synctactic’s platform where we then moved all the tickets to an RDBMS like MySQL. The data was transformed and structured so that it could be prepped for extracting the right features for the model.

The Model here was a classification algorithm that could scan the contents of the ticket and identify its features and then bucket it under specific categories.

The model based on the sentiment indicators also marked the ticket’s priority. This was configured as a pipe in the Synctactic platform and was triggered anytime a new ticket enters the system

Data Prep for Chatbots

All the previous support ticket queries and responses were extracted into a MySQL DB. This data then was structured as questions and answers so that the bot could identify the right answer when it encounters a specific question.

A pipe was defined to move data from the support platform to a data store and transformed it to be in a Question Answer format. This pipe would also take in multiple other data sources which provided the training data in the same format

Another pipe was defined to train the model to provide named entity recognition and intent detection. The input queries would trigger the model and the output was used to build the responses of the chatbot using another chatbot tool.

Predictive Maintenance

Support ticket data had a lot of indicators on the issues of a particular house. This data was combined with the manual home inspection data and tenant feedback data to identify major recurring issues in a house.

This data provided the input data for training a predictive model to predict which houses are likely going to have issues and the operations team was prepared beforehand to resolve them.

This data provided the input data for training a predictive model to predict which houses are likely going to have issues and the operations team was prepared beforehand to resolve them.


In this way, the company used synctactic’s platform to push their data-driven initiatives in the field of customer support. Without needing a significant tech bandwidth, the data platform was easily deployed and data analysts, engineers and scientists were able to quickly start using the platform to run all their initiatives.

reduction in duplicate tickets
80 %

Faster time to resolve tickets

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