● user@neuralcore:~$ ./init

Cloud, Data & AI engineering, built for production.

NeuralCore is an engineering company: we design, build and operate cloud, data and AI platforms at production scale.

// 01 — services

Five disciplines, one engineering team

[cloud]

Cloud Platforms

Cloud-native foundations that are secure, scalable and reproducible by default.

[data]

Data Platforms

Lakehouses and pipelines that turn raw data into governed, real-time products.

[platform]

Platform Engineering

Internal developer platforms with golden paths, CI/CD and built-in observability.

[ai]

Artificial Intelligence

Production AI — from classical ML to LLM systems, agents and retrieval.

[mlops]

MLOps

The lifecycle that keeps models reliable in production: train, serve, monitor, retrain.

Security and FinOps across everything we build.

// 02 — process

How we work

01
discovery
understand the challenge
02
build
prototype and build
03
ship
to production
04
operate
measure, scale and run

// 03 — cases

Projects with results

[data]

Enterprise Data Platform

Challenge

A fragmented architecture with many data sources and manual processes.

Solution

A cloud-native platform on Azure and GCP with distributed processing, automation and centralized observability.

Results
  • 50+ TB processed monthly
  • 90% fewer manual tasks
  • Multi-cloud Azure + GCP
[mlops]

AI / MLOps Platform

Challenge

Taking machine learning models from experimentation to reliable production.

Solution

End-to-end MLOps pipelines automating training, validation, deployment and monitoring.

Results
  • Deployment from weeks to hours
  • Models monitored in real time
  • Production ML for multiple clients
[finops]

Cloud Cost Optimization (FinOps)

Challenge

Oversized infrastructure with rising costs and poor operational visibility.

Solution

FinOps analysis, resource right-sizing and scaling automation — including removing zombie Cloud Run revisions.

Results
  • USD 19,500/year saved
  • Better resource utilization
  • More predictable budgeting
[cloud]

Cloud Architecture for a SaaS Platform

Challenge

Building a scalable platform able to support accelerated growth.

Solution

A Kubernetes-based architecture with managed services and automated deployments.

Results
  • High availability
  • Continuous, zero-downtime deployments
  • Automatic horizontal scaling
[genai]

Generative AI Platform

Challenge

Embedding generative AI capabilities into existing business processes.

Solution

Intelligent agents, RAG and LLM-based architectures.

Results
  • 100,000+ daily requests
  • Automation of complex processes
  • Lower response times
[bigdata]

Large-Scale Distributed Data Processing

Challenge

Processing tens of terabytes of data efficiently and cost-effectively.

Solution

A distributed architecture using Spark, BigQuery and serverless services.

Results
  • 90h → 5h processing time
  • Much lower operational cost
  • Near real-time analysis

// 04 — expertise

Areas of Expertise

Cloud ArchitecturePlatform EngineeringData PlatformsArtificial IntelligenceMachine LearningMLOpsEnterprise IntegrationDevOps & SREDistributed SystemsCloud FinOps

Technologies

Azure·Google Cloud·Kubernetes·Spark·Databricks·BigQuery·Vertex AI·Azure AI·TensorFlow·PyTorch·Apache NiFi·Airflow·Terraform·Docker·Python·Scala

// 05 — contact

Let's build something together?