Cogna automates the software consultancy process using AI to translate business pain points into working applications. The stack—Azure, Kubernetes, Temporal, Dagster, Go, Python, FastAPI, plus GPT-3.5 and RAG—reveals a system designed to orchestrate complex backend services and agent-based code generation. Active projects span cloud infrastructure, observability, and an AI-powered software factory, targeting utilities, construction, and logistics where legacy systems and siloed tooling create the friction Cogna is built to eliminate.
Cogna is a precision software solutions provider based in London that enables organisations to build custom applications without traditional IT consultancy timelines or costs. The company uses AI to identify operational inefficiencies in infrastructure-heavy sectors—utilities, construction, facilities management, logistics—and generates working software that customers can deploy in days rather than months. The product combines cloud-native infrastructure (Azure, Kubernetes) with generative AI capabilities to automate the design-to-delivery cycle. Hiring is accelerating across product, engineering, and sales, focused on scaling the platform.
Cogna builds on Azure and Kubernetes for cloud infrastructure, uses Temporal and Dagster for workflow orchestration, Go and Python for services, and integrates GPT-3.5 and RAG for AI-powered code generation and retrieval-augmented systems.
Cogna serves utilities, construction, facilities management, and logistics—industries where legacy systems and complex infrastructure create operational inefficiencies. The company addresses gaps traditional software and custom development timelines cannot solve.
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Cogna's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.