"Converting Complex Problems into Easier Computations by Turing Intelligence"
Turing Intelligence is a software engineering company based in Prague. We deliver production-ready systems from web platforms and backend infrastructure to advanced simulation and computational modeling.
Structured across three engineering disciplines. Each offering is scoped, delivered, and maintained as a real engineering engagement.
Corporate and product websites built for performance, SEO, and maintainability. We deliver clean, structured code not templated output.
Browser-based applications with real business logic dashboards, internal tools, data portals, and workflow systems.
Multi-user platforms handling inventory, resource scheduling, reporting, or operations management with structured access control.
Native and cross-platform mobile applications connected to a backend for field teams, service delivery, or customer-facing workflows.
Bespoke software built around a specific operational requirement. We scope, design, and deliver without off-the-shelf constraints.
Provisioning and configuring cloud environments on AWS or GCP compute, networking, storage, and security aligned to your workload profile.
Server-side systems built for correctness and scale: REST and async APIs, worker services, scheduled jobs, and data persistence layers.
Structured, versioned APIs with documented contracts built to integrate reliably with third-party systems, mobile clients, or partner services.
CI/CD pipeline configuration, containerisation with Docker and Kubernetes, environment management, and integration of third-party services.
Architecture review, technology selection, and engineering advisory for teams making structural decisions under time or budget constraints.
Designing the components, data flows, and integration points of a system before implementation — reducing rework and structural debt.
Scripting, data processing, automation pipelines, and scientific computation using Python including NumPy, SciPy, and PyTorch where applicable.
Identifying and resolving bottlenecks in existing systems query optimization, caching strategies, concurrency fixes, and load analysis.
Beyond standard software delivery, we maintain working capability in a range of technical disciplines. These are applied where the project requires them.
Applied machine learning for classification, regression, and inference tasks. We work with PyTorch-based models and integrate AI components into production systems where there is a clear, defined use case.
Structuring, cleaning, and analysing operational data to support decision-making. We build analytics pipelines, reporting systems, and summary dashboards grounded in accurate data engineering.
Software development for constrained hardware environments microcontrollers, sensor integration, and low-level C/C++ implementations where reliability and resource efficiency are required.
Security review of web applications and backend systems, including input validation, authentication design, secure configuration, and identification of common vulnerability patterns.
Text processing pipelines, language model integration, tokenization, and structured information extraction from unstructured natural language sources for downstream analytical use.
Image classification, object detection, and feature extraction pipelines using convolutional architectures. Applied in contexts where visual data is a primary input to system logic.
Computational models that replicate the behaviour of physical or digital systems under variable conditions enabling controlled analysis without disrupting live operations.
Our approach extends beyond traditional software development: rather than asking “does the system work?”, we focus on how it behaves under conditions that cannot be safely or practically reproduced in production. This becomes critical when designing systems that must operate reliably at scale, optimizing workflows where field testing is expensive, or validating architectures before committing to infrastructure spend. We build mathematical models of these systems, run controlled simulations, and deliver decision-grade insights.
A structured process applied to every engagement from initial scoping to production delivery.
Define the operating constraints, system boundaries, inputs, outputs, and integration points before any design begins.
Produce architecture documentation: component structure, data flows, API contracts, and infrastructure requirements.
Implement the system. Where simulation is applicable, construct and run the model in parallel with or prior to implementation.
Verify behavior against defined requirements. Load test where relevant. Review edge cases and failure modes systematically.
Deliver to production using defined infrastructure. Provide documentation, handover materials, and transition support as scoped.
If you have a software engineering requirement a system to build, an architecture to review, or a simulation problem to formalize GET IN TOUCH. We respond to every enquiry personally.
GET IN TOUCH