Making AI CodingMeasurable forBusiness Software
Evaluation-first infrastructure for coding agents working on auditable, long-lived business systems.
Business software does not only live in the cloud. It can also run close to machines, robots, edge devices, and industrial systems - wherever business rules and audit trails matter.
Measured before automated.
Framed token, text, test, trial, today, tomorrow - deterministic by design.
Why This Matters
Business software must be correct, reviewable, and auditable. Automation without evidence creates risk; measurement builds confidence.
What We Evaluate
- Functional correctness
- API adherence
- Audit coverage
- Framework discipline
- Token efficiency
How We Measure
- Deterministic evaluation
- Reproducible environments
- Structured metrics
- Human-reviewed results
- Public reports
Controlled Evaluation
TeaQL keeps the main path deterministic: stable tasks, known APIs, traceable execution, and baselines that teams can compare across time.
The goal is not to let agents move faster in the dark. The goal is to make their work measurable before it becomes operational software.
Autonomous Evaluation
No-gate experiments still matter. TeaQL can expose failure modes, unsafe shortcuts, missing guardrails, and places where an agent ignores the business API boundary.
Those results should inform adoption decisions, not be hidden behind a single success demo.
Evaluation Across Stacks
TeaQL uses the same evidence discipline across the Java stack, the Rust runtime, generated business APIs, database providers, and agent-facing development workflows.
Evaluation Report 001
The first public TeaQL autonomous evaluation report is available for review. We also published the rationale and raw evaluation data so the summary can be checked against the evidence behind it.