BYTNEX

Practice areas

React
Next.js
TypeScript
Node.js
Python
React Native
FastAPI
LangChain
PostgreSQL
pgvector
AWS
Docker
Kubernetes
Terraform
Tailwind CSS
JavaScript
HTML5
CSS3
Redis
GraphQL
Linux
Nginx
React
Next.js
TypeScript
Node.js
Python
React Native
FastAPI
LangChain
PostgreSQL
pgvector
AWS
Docker
Kubernetes
Terraform
Tailwind CSS
JavaScript
HTML5
CSS3
Redis
GraphQL
Linux
Nginx
React
Next.js
TypeScript
Node.js
Python
React Native
FastAPI
LangChain
PostgreSQL
pgvector
AWS
Docker
Kubernetes
Terraform
Tailwind CSS
JavaScript
HTML5
CSS3
Redis
GraphQL
Linux
Nginx
Vue.js
NestJS
Express
MongoDB
Supabase
Flutter
Swift
Kotlin
Java
Go
C#
PHP
.NET
n8n
GitHub Actions
Metabase
Power BI
Figma
Docker
GA4
Google Tag Manager
Grafana
Vue.js
NestJS
Express
MongoDB
Supabase
Flutter
Swift
Kotlin
Java
Go
C#
PHP
.NET
n8n
GitHub Actions
Metabase
Power BI
Figma
Docker
GA4
Google Tag Manager
Grafana
Vue.js
NestJS
Express
MongoDB
Supabase
Flutter
Swift
Kotlin
Java
Go
C#
PHP
.NET
n8n
GitHub Actions
Metabase
Power BI
Figma
Docker
GA4
Google Tag Manager
Grafana
The Bytnex practice areas and what each one has already delivered in a real client project.

A record of our trajectory

How Bytnex evolved by practice area: what it started delivering, with which technology and for what kind of operation.

Active

Practice area

Applied Artificial Intelligence

Active

Autonomous agents, RAG over internal knowledge bases, predictive scoring and decision automation. Applied in the Solar Livre and Agro 9 cases.

  • Feasibility assessment with cost and timeline estimates
  • Pilot measured against the current baseline of the process
  • Integration with the systems the team already uses (ERP, CRM, WhatsApp)
PythonLangChainOpenAIpgvectorFastAPI
Practice area

Systems & Applications

Active

Custom software for processes that do not fit off-the-shelf tools. Web, mobile and integration with the existing system landscape.

  • Requirements gathering with a written scope
  • Custom web system or app, delivered in two-week sprints
  • Automated tests on critical flows
ReactNext.jsNode.jsReact NativePostgreSQL
Practice area

Data Science & Analytics

Active

Data pipelines, data lakes and dashboards leadership actually uses. Predictive models when the use case justifies one.

  • Data pipeline unifying the existing sources
  • Dashboard with the metrics leadership actually uses
  • Predictive model when the use case justifies one
PythonPandasdbtMetabase/Power BIPostgreSQL
Practice area

Cloud, DevOps & Security

Active

Infrastructure as code, CI/CD pipelines, observability and data protection compliance. It underpins everything we put into production.

  • Infrastructure as code, documented and version controlled
  • CI/CD pipeline running tests before every deployment
  • Observability: logs, metrics and alerts
AWSDockerKubernetesTerraformOWASP

Sectors we have
already delivered in

Solar energy, civil construction, agribusiness, food and media. Every case study started from a concrete operational bottleneck, and the result was measured against the previous baseline.

Solar Energy

Solar Energy

Solar Livre & Elesol — AI lead qualification: +150% qualified leads.

Civil Construction

Civil Construction

2R Engenharia — construction management with predictive AI: -65% delays.

Agribusiness

Agribusiness

Agro 9 Tecnologia — AI-powered data lake: -92% harvest analysis time.

Retail & Food

Retail & Food

Delícias do Trigo — e-commerce with WhatsApp ordering: +240% sales.

Media

Media

Nossa FM 104,9 — analytics and automation: +180% online audience.

End to end

End to end

From requirements gathering to ongoing support.