The Engineering Behind LoadMagic

Performance engineering has a preparation problem. Building reliable, correlation-complete scripts from real user flows is the hardest, most time-consuming part of the discipline. We built the engineering to solve it.

Our Approach

Most AI tools in the performance space treat script generation as a text problem — feed in a recording, get out code. But anyone who has built production-grade performance scripts knows the real challenge is dynamic value correlation: identifying which values change between sessions, where they originate, how they propagate, and what breaks when they're not handled correctly.

LoadMagic approaches this as an engineering problem, not a prompt engineering problem. Our platform combines deep domain knowledge of performance tools — JMeter, Locust, and beyond — with AI systems designed to learn, adapt, and compound their understanding of the applications they analyse.

We don't build AI to replace performance engineers. The judgment, experience, and risk awareness that skilled engineers bring is irreplaceable.

We build for experts — removing the grind so they can focus on what matters most: reducing risk. Our goal is to multiply the output and quality of experienced engineers, not to substitute for the skills that make them valuable.
E-PORT Enterprise Portability
E-PORT enterprise portability — 100% internal AI, keep data safe, secure, controlled
The Challenge

Regulated industries, data sovereignty requirements, and air-gapped environments mean many AI-powered tools simply cannot operate where the work needs to happen. Teams are forced to choose between AI capability and compliance.

Our Approach

E-PORT is LoadMagic's portability framework. A single codebase deploys as a managed SaaS service, inside customer VPCs, or in fully air-gapped environments with no mandatory data egress. Enterprises maintain full control over where their data lives and how it's processed, without sacrificing AI-powered capabilities.

E-CORE Inference Abstraction
E-CORE inference abstraction — model-agnostic reasoning layer
The Challenge

AI platforms typically lock customers into specific inference providers. GPU availability varies across environments. Enterprises need control over where and how AI inference runs — especially when operating in sovereign or restricted infrastructure.

Our Approach

E-CORE is LoadMagic's model-agnostic reasoning layer. It abstracts inference across cloud APIs, on-premises GPU infrastructure, and CPU-based fallback — without changing application logic. This is the foundation that makes E-PORT possible: the platform adapts to whatever inference backend is available in the target environment.

M.I.N.D. Memory-Informed Neural Dynamics
M.I.N.D. — Memory-Informed Neural Dynamics learning system
The Challenge

Large language models don't learn from experience. Once trained, they repeat the same mistakes regardless of how many times they've encountered similar problems. Meanwhile, the applications under test evolve constantly — new authentication flows, changing token lifecycles, shifting API patterns. Every engagement starts from zero.

Our Approach

M.I.N.D. is LoadMagic's persistent learning system. It maintains confidence-weighted observations about the applications it analyses — which patterns work, which approaches fail, which behaviours are common across similar systems. Validated knowledge earns trust over time; unverified observations naturally decay. Insights that prove reliable across multiple application domains are promoted, building a compounding knowledge base that improves preparation quality with every session.

The Correlation Challenge

Dynamic value correlation is the hardest problem in performance engineering preparation. When a browser records a user session, hundreds of values — tokens, session IDs, CSRF parameters, timestamps — are captured as static snapshots. In a real load test, these values must be dynamically extracted and injected, or the script breaks silently.

LoadMagic's approach spans a spectrum of correlation strategies, from deterministic pattern matching to AI-driven analysis, adapting its technique to the complexity and novelty of each value it encounters.

Read more: The Correlation Spectrum — Five Approaches to Dynamic Data Handling in Performance Engineering →

Design Principles

These are structural commitments, not aspirations. They shape every architectural decision we make.

Stability before autonomy Deterministic, reproducible behaviour comes first. Automation is earned through verified outcomes, not assumed by design.
Portability before scale The platform works where the customer needs it — SaaS, on-premises, or air-gapped — before optimising for volume.
Explainability before automation Every automated action is evidence-backed, confidence-scored, and auditable. No black-box decisions.
Human approval before irreversible action The platform recommends, prepares, and validates. It does not silently decide on behalf of the engineer.
Built for engineers, not instead of them We amplify expert output — removing the grind of repetitive preparation so engineers can focus on risk reduction, analysis, and the judgment calls that define quality performance engineering.

Want to learn more about our approach?

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