Innovation and technology nurture each other.

We harness the power of data for better decision making within an innovative organizational culture, through our team of Data Architects, Data Scientists and Data Engineers.

This evolves into gigantic volumes of data, which using the tools provided by the cloud allows us not only to improve its analysis but also to implement Machine Learning and AI to increase business intelligence.

Intelligent Forecasting

AI solution that allows the forecasting of business events in advance in order to plan improvements and actions that optimize results.

Data Lakes

Business analysis, manage diverse data sources, and achieve a better understanding of the world through these centralized repositories.

Master Data Optimization

The Master Data Optimization solution uses Machine Learning to automate the creation, remediation and continuous maintenance of master databases.

HealthBot

Virtual health channel that uses cognitive technologies such as artificial intelligence, in order to optimize and speed up the times of health institutions.

Generative AI

Discover the technological disruption that allows you to take your business to a new level and achieve maximum productivity.

Intelligent Automation

Robotic Process Automation (RPA) and Artificial Intelligence that empower a rapid automation of end-to-end business processes and accelerate digital transformation.

Nubiral Cognitive AI Bot

Virtual conversational assistant based on Artificial Intelligence (AI) that enables real-time file processing.

Intelligent Document Processing

An AI solution that allows extracting information from documents and incorporating it into an automatic process, using OCR technology.

Cloud Data META Architecture

Implementation of the first cloud data META architecture on AWS with association to a set of use cases.

Read more

Interconnectivity in the AWS Cloud with AWS Direct Connect

Development of a similar interconnectivity structure in the AWS cloud using the Direct Connect service.

Read more

Intelligent Forecasting for demand planning

An insurance company performs demand forecasting in its various business units and reduces forecasting errors.

Read more

Anomaly detection through Data Lake and Fraud Detector

An important insurance company in Mexico detects anomalies and prevents actions with a probability of being fraudulent.

Read more
Blog

Generative AI in telecommunications: five highly complex use cases

A key technology for the industry to maximize efficiency levels, service quality and customer experience.

Read more
eBooks

GenAI use cases with Amazon Bedrock

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Papers

Trends 2024: Start getting value from generative AI

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Whitepapers

Machine learning recommender systems in digital media companies

Advances in machine learning enable digital media companies to improve their recommender systems and optimize user experience.

Read more

Learn about our architecture that combines at least one public and one private cloud to deliver the highest levels of scalability, flexibility, and performance.

Application modernization by migrating to the AWS cloud

Migration to the AWS Cloud at Telecom Argentina, modernizing obsolete applications with a focus on operational excellence.

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Migration to AWS by a major Japanese automobile company

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An important logistics company migrates its systems to AWS

OCA Argentina relies on legacy systems with limited cloud integration, so it modernized its technology by migrating to AWS.

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Application migration, optimization, efficiency, security, analytics and implementation services, with the aim of simplifying and accelerating the adoption of the latest IT trends in the market.

Planning and Consulting

  • Evaluation and Planning
  • Adoption Strategy Consultancy

Test

  • Test Automation

Execution

  • Design and implementation of CI/CD Pipeline
  • Automation and implementation of processes

App Dev

  • Code (JavaScript, Go, Python)

Telecommunications modernization with AWS technologies

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As ZABBIX Certified Partners, we scale to environments with thousands of items monitored simultaneously.
We also capture data of systems and applications over time to make proactive decisions and to anticipate disruptions in business services.

  • Zabbix Architecture and Implementation
  • Data & Analytics Monitoring

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Migration of monitoring tool to an automated system for host discovery, dashboards, and scalability over time.

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Telephone exchange monitoring, usage metrics, and channels

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Read more

IT Resource Monitoring Platform

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eBooks

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Whitepapers

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We help innovate by preparing our clients against current cyber threats.

We fulfill the responsibility of protecting data to maintain trust and comply with regulations.

  • Cloud Assessment & Consulting
  • Security Frameworks & Best Practices
  • Penetration Testing
  • Cloud Security
  • DevSecOps
Blog

Cybersecurity: Key Pillar for a 360º Digital Experience

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Whitepapers

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A strategy to anticipate and prevent technological problems that may impact the business.

Read more

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Generative AI in telecommunications: five highly complex use cases

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Connect+ is a great tool to incorporate knowledge and stay up to date with the latest technological developments.

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AWS + Nubiral

As an Advanced Consulting Partner of the AWS Partner Network, we think outside the box, daring to go where no one has gone before.
We constantly challenge ourselves to be better, providing your company with AWS solutions in a holistic and tailored way.

Microsoft + Nubiral

As Cloud Gold Partner, we work together with Microsoft every day to offer our clients the most innovative solutions based on the different microservices and capabilities that the Azure cloud offers.
Our team is constantly training and certifying on Azure’s services.

Zabbix + Nubiral

As ZABBIX Certified Partners, we scale to environments with thousands of items monitored simultaneously.
We also capture data of systems and applications over time to make proactive decisions and to anticipate disruptions in business services.

Data
& Innovation

General Info

Innovation and technology nurture each other.

We harness the power of data for better decision making within an innovative organizational culture, through our team of Data Architects, Data Scientists and Data Engineers.

This evolves into gigantic volumes of data, which using the tools provided by the cloud allows us not only to improve its analysis but also to implement Machine Learning and AI to increase business intelligence.

Solutions

Intelligent Forecasting

AI solution that allows the forecasting of business events in advance in order to plan improvements and actions that optimize results.

Data Lakes

Business analysis, manage diverse data sources, and achieve a better understanding of the world through these centralized repositories.

Master Data Optimization

The Master Data Optimization solution uses Machine Learning to automate the creation, remediation and continuous maintenance of master databases.

HealthBot

Virtual health channel that uses cognitive technologies such as artificial intelligence, in order to optimize and speed up the times of health institutions.

Generative AI

Discover the technological disruption that allows you to take your business to a new level and achieve maximum productivity.

Intelligent Automation

Robotic Process Automation (RPA) and Artificial Intelligence that empower a rapid automation of end-to-end business processes and accelerate digital transformation.

Nubiral Cognitive AI Bot

Virtual conversational assistant based on Artificial Intelligence (AI) that enables real-time file processing.

Intelligent Document Processing

An AI solution that allows extracting information from documents and incorporating it into an automatic process, using OCR technology.

Success Stories

Cloud Data META Architecture

Implementation of the first cloud data META architecture on AWS with association to a set of use cases.

Read more

Interconnectivity in the AWS Cloud with AWS Direct Connect

Development of a similar interconnectivity structure in the AWS cloud using the Direct Connect service.

Read more

Intelligent Forecasting for demand planning

An insurance company performs demand forecasting in its various business units and reduces forecasting errors.

Read more

Anomaly detection through Data Lake and Fraud Detector

An important insurance company in Mexico detects anomalies and prevents actions with a probability of being fraudulent.

Read more

Connect

Blog

Generative AI in telecommunications: five highly complex use cases

A key technology for the industry to maximize efficiency levels, service quality and customer experience.

Read more
eBooks

GenAI use cases with Amazon Bedrock

Discover the potential of digital transformation with Generative AI.

Read more
Papers

Trends 2024: Start getting value from generative AI

Over the next 12 months, we will witness an incremental adoption of generative AI, higher levels of maturity and new use cases.

Read more
Whitepapers

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Read more

Hybrid
Multi Cloud

General Info

Learn about our architecture that combines at least one public and one private cloud to deliver the highest levels of scalability, flexibility, and performance.

Success Stories

Application modernization by migrating to the AWS cloud

Migration to the AWS Cloud at Telecom Argentina, modernizing obsolete applications with a focus on operational excellence.

Read more

Modernization of multimedia content with AWS Migration

Successful migration to AWS cloud to modernize Claro Video’s multimedia content infrastructure.

Read more

Migration to AWS by a major Japanese automobile company

The smooth migration of Toyota to AWS unleashes performance, cost efficiency, and user satisfaction.

Read more

An important logistics company migrates its systems to AWS

OCA Argentina relies on legacy systems with limited cloud integration, so it modernized its technology by migrating to AWS.

Read more

Connect

Blog

5 benefits of serverless architectures

Higher levels of scalability and an absolute focus on digital business transformation, are just some of the many advantages of this model.

Read more
eBooks

Cloud 4.0: A phenomenon in exponential growth

A tour of the main opportunities that arise from a correct and timely migration of workloads to the cloud, and the trends that are being visualized in the cloud universe.

Read more
Papers
04 October , 2022

Cloud 4.0: A phenomenon in exponential growth

A tour of the main opportunities that arise from a correct and timely migration of workloads to the cloud, and the trends that are being visualized in the cloud universe.

Read more

DevOps
& App Evolution

General Info

Application migration, optimization, efficiency, security, analytics and implementation services, with the aim of simplifying and accelerating the adoption of the latest IT trends in the market.

Solutions

Planning and Consulting

  • Evaluation and Planning
  • Adoption Strategy Consultancy

Test

  • Test Automation

Execution

  • Design and implementation of CI/CD Pipeline
  • Automation and implementation of processes

App Dev

  • Code (JavaScript, Go, Python)

Success Stories

Telecommunications modernization with AWS technologies

A leading telecommunications company modernizes its applications to respond more quickly and agilely to market changes.

Read more

App Modernization in Telecommunications and Entertainment

A prominent telecommunications and entertainment company achieved billing app availability levels of 99.95% in Colombia.

Read more

Azure Governance & App Modernization

A leading telecommunications and entertainment company achieved application billing availability levels of 99.95%.

Read more

Migration deployment of Core Banking hosted in the AWS cloud

Fintech deploys the Core Banking of its platform allowing the integration of new services in an effective and easy way.

Read more

Connect

Blog

GitHub Copilot: the new way to code

GitHub Copilot is revolutionizing software development: a key helper for faster, more effective and bug-free code.

Read more
eBooks

Agile & DevOps

A review of the meanings of each of these concepts, how they integrate with each other and what benefits they bring.

Read more

Monitoring
& Intelligence

General Info

As ZABBIX Certified Partners, we scale to environments with thousands of items monitored simultaneously.
We also capture data of systems and applications over time to make proactive decisions and to anticipate disruptions in business services.

Solutions

  • Zabbix Architecture and Implementation
  • Data & Analytics Monitoring

Success Stories

Monitoring solution upgrade using Zabbix

Migration of monitoring tool to an automated system for host discovery, dashboards, and scalability over time.

Read more

Telephone exchange monitoring, usage metrics, and channels

Monitoring of AVAYA phone system through the implementation of Zabbix, executing the only method of information extraction via telnet manager.

Read more

Implementation of monitoring solution with Zabbix

A leading payment solutions company implements a new automated monitoring platform equipped with a real-time messaging alert system for incident prevention.

Read more

IT Resource Monitoring Platform

Monitoring & Intelligence • Insurance • Zabbix IT Resource Monitoring Platform Integration of Zabbix with the VMWare solution and monitoring of PABX trunk lines from providers, UPS equipment, and Chillers....
Read more

Connect

Blog

How to implement IT observability and track applications?

A strategy to anticipate and prevent technological problems that may impact the business.

Read more
eBooks

Compliance: the evolution of monitoring

A key paradigm for anticipating and solving problems in increasingly complex IT infrastructures.

Read more
Whitepapers

OpenSearch and its log agents

OpenSearch is a comprehensive solution for centralizing and analyzing logs from various sources, ideal for managing complex IT scenarios.

Read more

Cybersecurity

General Info

We help innovate by preparing our clients against current cyber threats.

We fulfill the responsibility of protecting data to maintain trust and comply with regulations.

Solutions

  • Cloud Assessment & Consulting
  • Security Frameworks & Best Practices
  • Penetration Testing
  • Cloud Security
  • DevSecOps

Connect

Blog

Cybersecurity: Key Pillar for a 360º Digital Experience

To mitigate the risks associated with cyberattacks and protect data is essential to survive and lead in the era of digital transformation.

Read more
Whitepapers

Cybersecurity in your company: The 360º digital solution from Nubiral

How to develop a cybersecurity plan? Which are the main threats? Which are the best and most modern technologies to face these threats?

Read more

Partners

Solutions

AWS + Nubiral

As an Advanced Consulting Partner of the AWS Partner Network, we think outside the box, daring to go where no one has gone before.
We constantly challenge ourselves to be better, providing your company with AWS solutions in a holistic and tailored way.

Microsoft + Nubiral

As Cloud Gold Partner, we work together with Microsoft every day to offer our clients the most innovative solutions based on the different microservices and capabilities that the Azure cloud offers.
Our team is constantly training and certifying on Azure’s services.

Zabbix + Nubiral

As ZABBIX Certified Partners, we scale to environments with thousands of items monitored simultaneously.
We also capture data of systems and applications over time to make proactive decisions and to anticipate disruptions in business services.

Success Stories

Cloud Data META Architecture

Implementation of the first cloud data META architecture on AWS with association to a set of use cases.

Read more

A medical center implements a chatbot and cognitive services

Improvement in patient care times and reduction in administrative staff dedication costs for routine tasks.

Read more

Monitoring solution upgrade using Zabbix

Migration of monitoring tool to an automated system for host discovery, dashboards, and scalability over time.

Read more

Connect

Blog

How to implement IT observability and track applications?

A strategy to anticipate and prevent technological problems that may impact the business.

Read more

GitHub Copilot: the new way to code

GitHub Copilot is revolutionizing software development: a key helper for faster, more effective and bug-free code.

Read more

Generative AI in telecommunications: five highly complex use cases

A key technology for the industry to maximize efficiency levels, service quality and customer experience.

Read more

Technological innovation in media companies: The role of Microsoft Fabric

Audiovisual and entertainment content providers find in this tool the key ally to modernize and capture all the value of their data.

Read more

eBooks & Papers

GenAI use cases with Amazon Bedrock

Discover the potential of digital transformation with Generative AI.

Read more

MLOps: powering the value of machine learning

A comprehensive guide to MLOps, a key discipline that guarantees the success of Machine Learning (ML) projects in organizations.

Read more

DataOps: everyone plays their own game

Discover how this discipline provides a framework and tools to align the engineering and analytics teams to improve the management of the data ecosystem in the organization.

Read more

GenAI Services: A land of opportunity for organizations.

The new user-friendly way to adopt generative artificial intelligence to power business.

Read more

Connect+

Connect+ is a great tool to incorporate knowledge and stay up to date with the latest technological developments.

Access new innovative audiovisual content, quickly and easily. Explore and get to know the technological universe in a different and agile way!

Whitepapers

Machine learning recommender systems in digital media companies

Advances in machine learning enable digital media companies to improve their recommender systems and optimize user experience.

Read more

Cybersecurity in your company: The 360º digital solution from Nubiral

How to develop a cybersecurity plan? Which are the main threats? Which are the best and most modern technologies to face these threats?

Read more

Microsoft Fabric Guide: Use case end-to-end Deployment

Banks and financial services companies can benefit in numerous ways by deploying Microsoft Fabric.

Read more

How to Deploy Microsoft Fabric in Multicloud Infrastructures

Microsoft Fabric’s data analytics combined with the power of the multi-cloud architecture, drives decision making and empowers users.

Read more
Whitepapers

AI-based conversational assistant

Step by step, from the requirements request to the continuous improvement, how to develop a conversational assistant based on artificial intelligence.

Home / AI-based conversational assistant

1. Why are AI-based conversational assistants so important?

In a world where automated and high-level experiences are essential to business success, the importance of AI-based conversational assistants is undeniable.

From identifying requirements and objectives to continuous improvement iteration, here is a detailed step-by-step approach to building these key business tools.

2. Development guide

1. Identification of requirements and objectives

Before diving into the technique, it is essential to define the purpose, the type of questions to be answered and the scope of the virtual assistant. The same goes for the sources of information that we will provide so that it has the necessary knowledge and can give appropriate answers. 

Also, it is necessary to define the channel in which the assistant will be available for users. It can be a frontend or it can be an app integrated to the chat (Slack, Whatsapp, Microsoft Teams, etc).

 

2. Data collection and preprocessing

There can be structured or unstructured databases or plain files such as PDFs. The important thing is to detect where the data sources are that will give context to the helper. From there, the processing to be done is defined.

 

3. Construction of the embedding

Embeddings are a collection of vectors that capture the essence of the content. This allows the files to be accessible and usable in real time by users. There are different embedding models, such as Microsoft Ada or Amazon Titan. We must select the one that best suits our requirements.

 

4. Creating a vector database

A specialized vector database such as Azure Cognitive Search or Amazon OpenSearch for storing and retrieving embeddings, allows users to quickly search for answers or suggestions based on semantic similarity.

 

5. LLM configuration and tuning

One of the most important steps. Design prompts that guide the model to generate answers aligned with the wizard’s purpose. To do this, experiment with different LLM models, analyze which one best fits the needs and configure the prompt so that the assistant behaves assertively. In some cases, fine-tuning is required to adjust the LLM model for the defined task.

 

6. Integration of the model with an API

For the wizard to be accessible and integrated on different platforms, the LLM model must be wrapped in an API, with tools such as FastAPI, Flask or Django. This should be able to receive an input from the user, process it through the model and return a response. It can then be deployed in a cloud environment for a secure and scalable application.

 

7. Interface development and application integration

If the use case requires it, a friendly and functional user interface (UI) can be developed for users to interact with the wizard. It can be a web app, a mobile app or an app that is integrated into other software. Then the necessary integration with backend APIs must be done to bring it to life.

 

8. Monitoring and alerts

Monitoring and logging tools track user interactions and wizard responses. Key performance metrics (KPIs) and thresholds for alerts are set. For example, if the wizard cannot answer a certain percentage of questions.

 

9. Iteration and continuous improvement

Data collected through monitoring allows users to identify areas for improvement. Feedback could also be required from users and from this feedback adjustments could be made.

This may include adjusting the prompt, fine-tuning the embedding or expanding the database with new sources of information.

These steps provide a solid framework for creating, implementing and maintaining an AI-driven conversational assistant. 

3. Conclusions

– The first step before building an AI-based conversational assistant is to understand the value it will bring to the business.

– With that in clear, the requirements and the specific objectives and identification of the data sources that will give context to the assistant, will move forward.

– Then, embeddings are built to read the content of the documents and vector databases to search for answers or suggestions based on semantic similarity.

– The next step is essential: designing prompts that guide the model to generate answers aligned with the purpose. Once this is done, APIs are used to make the model available to users.

– The development of a user-friendly interface is the key to a greater deployment. However, thanks to monitoring and logging, it is possible to verify if the wizard is working as expected.

And this is just the beginning of the journey: there are always opportunities to improve the assistant.

AI-based conversational assistant: Development guide

Why are AI-based conversational assistants so important?

In a world where automated and high-level experiences are essential to business success, the importance of AI-based conversational assistants is undeniable.
From identifying requirements and objectives to continuous improvement iteration, here is a detailed step-by-step approach to building these key business tools.

 

Guide

1. Identification of requirements and objectives

Before diving into the technique, it is essential to define the purpose, the type of questions to be answered and the scope of the virtual assistant. The same goes for the sources of information that we will provide so that it has the necessary knowledge and can give appropriate answers.
Also, it is necessary to define the channel in which the assistant will be available for users. It can be a frontend or it can be an app integrated to the chat (Slack, Whatsapp, Microsoft Teams, etc).

 

2. Data collection and preprocessing

There can be structured or unstructured databases or plain files such as PDFs. The important thing is to detect where the data sources are that will give context to the helper. From there, the processing to be done is defined.

 

3. Construction of the embedding

Embeddings are a collection of vectors that capture the essence of the content. This allows the files to be accessible and usable in real time by users. There are different embedding models, such as Microsoft Ada or Amazon Titan. We must select the one that best suits our requirements.

 

4. Creating a vector database

A specialized vector database such as Azure Cognitive Search or Amazon OpenSearch for storing and retrieving embeddings, allows users to quickly search for answers or suggestions based on semantic similarity.

 

5. LLM configuration and tuning

One of the most important steps. Design prompts that guide the model to generate answers aligned with the wizard’s purpose. To do this, experiment with different LLM models, analyze which one best fits the needs and configure the prompt so that the assistant behaves assertively. In some cases, fine-tuning is required to adjust the LLM model for the defined task.

 

6. Integration of the model with an API

For the wizard to be accessible and integrated on different platforms, the LLM model must be wrapped in an API, with tools such as FastAPI, Flask or Django. This should be able to receive an input from the user, process it through the model and return a response. It can then be deployed in a cloud environment for a secure and scalable application.

 

7. Interface development and application integration

If the use case requires it, a friendly and functional user interface (UI) can be developed for users to interact with the wizard. It can be a web app, a mobile app or an app that is integrated into other software. Then the necessary integration with backend APIs must be done to bring it to life.

 

8. Monitoring and alerts

Monitoring and logging tools track user interactions and wizard responses. Key performance metrics (KPIs) and thresholds for alerts are set. For example, if the wizard cannot answer a certain percentage of questions.

 

9. Iteration and continuous improvement

Data collected through monitoring allows users to identify areas for improvement. Feedback could also be required from users and from this feedback adjustments could be made.

This may include adjusting the prompt, fine-tuning the embedding or expanding the database with new sources of information.
These steps provide a solid framework for creating, implementing and maintaining an AI-driven conversational assistant.

 

Conclusions

– The first step before building an AI-based conversational assistant is to understand the value it will bring to the business.
– With that in clear, the requirements and the specific objectives and identification of the data sources that will give context to the assistant, will move forward.
– Then, embeddings are built to read the content of the documents and vector databases to search for answers or suggestions based on semantic similarity.
– The next step is essential: designing prompts that guide the model to generate answers aligned with the purpose. Once this is done, APIs are used to make the model available to users.
– The development of a user-friendly interface is the key to a greater deployment. However, thanks to monitoring and logging, it is possible to verify if the wizard is working as expected.
And this is just the beginning of the journey: there are always opportunities to improve the assistant.

We would like to assist you in the development of your AI-based conversational assistants to enhance interactions with your customers

 

Arrange a meeting with a specialist!

Nubiral

About Nubiral