Try CoTester: Ditch Manual QA. Test, Debug, Execute in a Snap!
In modern software delivery pipelines, test failures are routine.
A commit is pushed, CI runs, and something breaks. The response is also routine: engineers examine logs, interpret stack traces, review test output, and attempt to determine whether the failure is legitimate, environmental, or non-deterministic.
The cycle consumes a disproportionate amount of engineering time. Most teams rely on a mix of experience, heuristics, and repeated runs to isolate the root causes. Even when observability is in place, and test history is available, the process isn’t as easy as it should be.
Why LLMs Should Help But Often Don’t
The recent wave of interest in applying Large Language Models (LLMs) to software development has brought new possibilities to the testing and Quality Assurance (QA) domain.
Using general-purpose models to summarize, extract patterns from logs, and assist in triage workflows is the norm.
However, despite that, the effectiveness of LLMs in CI/CD contexts remains limited. You see, CI/CD environments produce structured, semi-structured, and unstructured signals that must be parsed differently.
A failing UI snapshot requires a different form of analysis than a backend exception. A test marked as flaky shouldn’t be treated the same way as a consistent regression.
Most LLM implementations in this space apply a uniform model across all inputs without meaningful context or prioritization. They tend to overfit to the wrong signals or underfit entirely. That’s why it’s important to start diagnosing with AI built for CI/CD.
Meet Quantum-Core™: The Intelligence Layer Behind the Future of Software Delivery
It’s a proprietary fusion engine that integrates frontier and compact models optimized for specific diagnostic tasks. The architecture prioritizes interpretability, performance, and cost-efficiency across high-volume, high-complexity pipelines.
Its key features include:
- High precision from large-scale models
- Speed and cost-efficiency from compact models
- A proprietary inference splitter and memory engine
- Real-time adaptability across tools and workflow memory
So, How Does Quantum-Core™ Work?
Quantum-Core™’s core workflow is built around four coordinated steps:
1. Classification
At its core, Quantum-Core™ is a lightweight, T5-based classifier. All failure signals, whether a trace, log, screenshot, or test report, are passed through it.
The model tags the data with one of 42 QA-relevant task labels, determining the required analysis and how the task will be decomposed.
2. Decomposition and routing to specialized models
Each task is broken into atomic subtasks based on type and complexity. For example, a UI test failure might be divided into visual diff analysis, component load checks, and layout consistency scoring.
A backend error could be divided into stack trace parsing and service-level dependency tracking. The subtasks are then dispatched to a curated set of large and compact language models. Selection is driven by task fit.
3. Parallel execution
Once routed, the subtasks run concurrently across a constantly-updated multi-LLM agent pool comprising GPT-4o, Claude 3.5, Gemini 2.5, Mistral, and many more. These agents have been trained or fine-tuned on domain-specific failure types. For instance:
- Dedicated trace-parsing models process stack traces
- Regression-aware vision agents interpret visual diffs
- Statistical and historical context assesses flaky test patterns
This minimizes latency and optimizes throughput for large-scale pipelines.
4. Fusion and ranking
Outputs are aggregated using Reciprocal Rank Fusion, which balances confidence scores across models to identify consensus without defaulting to majority voting. This fusion step produces a consolidated result that is both traceable and ranked for reliability.
Who Quantum-Core™ Is For And Why It Helps
If your biggest pain point is software delivery failures, Quantum-Core™ is apt for you. These are environments where velocity is non-negotiable, and triage delays compound quickly. Here’s how it supports key roles on high-velocity teams:
| Role | Challenge | What Quantum-Core™ Delivers |
| QA Lead | CI failures piling up with no clear root cause | Faster, clearer diagnostics across high-volume test suites |
| Release Manager | Time-consuming regression tracking | Automated regression isolation with domain-specific insights and scope clarity |
| Product Owner | Lacks confidence before deployment | Full-stack visibility into frontend components, backend services, and infrastructure layers |
Think of it like this: The output produced by Quantum-Core™ is precise and relevant to the system under test. It’s built for teams that can’t afford to guess!
Make Way For Quantum-Core™: A More Efficient Way to Ship
Failures will keep happening. That’s the nature of software at scale.
The question is how quickly you can understand them and move forward efficiently. Quantum-Core™ is a workload-specific inference layer designed for teams that need clarity at the point of failure.
It aims to surface the truth faster, with less noise, fewer assumptions, and root cause insights. Find out more about Quantum-Core™.
If you want to know about TestGrid, visit our website.
