Applied Computing Tech Stack

AI platform for real-time energy operations optimization

Technology, Information and Internet London, London 11–50 employees Privately Held

Applied Computing builds Orbital, a physics-informed AI system for energy operators to extract actionable insights from production data in real time. The stack—Python, PyTorch, Kafka, Kubernetes, RAG with Claude and Gemini—is shaped for streaming data pipelines and LLM-augmented decision support. Hiring velocity is accelerating across engineering and product (7 of 14 roles filled in the last month), while pain points cluster around production reliability, sensor noise handling, and scaling deployments across geographies—typical friction for a platform moving from early customer sites toward repeatable delivery.

Tech Stack 25 technologies

Core StackPython PyTorch React Svelte JavaScript TypeScript Node.js FastAPI Express Docker Kubernetes Kafka RabbitMQ AWS Jira Django PostgreSQL Linux OpenAI Databricks RAG Azure AWS EKS Claude Gemini

What Applied Computing Is Building

◆Challenges

Building pipeline in new geography
Process theatre
Operational reliability in production
Rigid agile dogma
Handling noisy sensor data
Deployment in complex enterprise ecosystems
Scaling across customers and sites
Continuous retraining in production
Delivering complex technical projects on time
Meeting delivery deadlines for complex projects

▲Active Projects

Orbital deployments at customer sites
Orbital’s foundational time-series modelling stack
Custom ui component development
Uncertainty quantification and robustness under sensor faults
Scalable product platform
Developer facing apis
Shared ui components
Delivery frameworks that scale across customers and geographies
Physics-informed and probabilistic model design
Pipeline development

Hiring Activity

Accelerating15 roles · 8 in 30d

Department

Engineering

Product

Sales

Finance

Research

Security

Seniority

Lead

Mid

Senior

Manager

Director

Notable leadership hires: Finance Director

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About Applied Computing

Applied Computing develops Orbital, a multi-foundation AI platform purpose-built for energy industry operations. The product enables engineers and plant operators to use 100% of their operational data for real-time optimization via physics-grounded, trustworthy AI models. The company targets downstream energy and critical infrastructure sectors where legacy systems and fragmented data currently prevent rapid decision-making. Based in London with 11–50 employees, the team is structured around engineering-led product development (custom UI, time-series modeling, uncertainty quantification, probabilistic models) paired with a small sales and product organization. Active work spans customer deployments, foundational model stacks, scalable platform architecture, and delivery frameworks to replicate success across sites and regions.

HeadquartersLondon, London

Company Size11–50 employees

Hiring MarketsUnited Kingdom, United States

Frequently Asked Questions

What tech stack does Applied Computing use?

Python, PyTorch, React, Svelte, Node.js, FastAPI, Docker, Kubernetes, Kafka, PostgreSQL, AWS, and LLM integrations (Claude, Gemini, OpenAI) for data pipelines and AI inference.

What is Applied Computing working on?

Orbital deployments at customer sites, time-series modeling infrastructure, uncertainty quantification under sensor faults, scalable product platform, developer APIs, and delivery frameworks for multi-geography rollout.