Engineering-Led AI Evaluation
“Acadify AI is built by an in-house team spanning software engineering, QA, AI engineering and data workflows. We apply engineering discipline to AI evaluation through defined tasks, versioned datasets, explicit rubrics, controlled runs, failure analysis and regression testing.”
Practice of Acadify Solution · Evaluated under mutual NDA
Acadify AI is the dedicated AI evaluation and model-training practice of Acadify Solution, an enterprise software engineering company that builds production platforms, distributed microservices, and cloud backends.
We established Acadify AI to address a fundamental gap in the AI industry: standard public benchmark leaderboards do not predict whether a foundation model or autonomous agent will hold up inside production software. Our team applies software engineering rigor and quality assurance discipline to evaluation—testing models not as standalone demos, but as components operating under strict schema contracts, environmental latency, and regression risks.
For enterprise software development, architecture, and full-stack engineering, visit Acadify Solution.
Building deterministic test harnesses, containerized sandbox runners, API mocking layers, and multi-file code diff analyzers.
Designing edge-case test cases, negative testing rubrics, fault injection scenarios, and continuous regression defense pipelines.
Model alignment, task-specific SFT, prompt calibration, LLM-as-a-judge rubric tuning, and comparative model benchmarking.
Domain dataset curation, hard-negative mining, schema normalization, version control, and multi-annotator agreement scoring.
Acadify AI is an active practice building its public evaluation portfolio with full transparency. Because our evaluation engagements frequently involve confidential architectures, pre-release models, and sensitive domain datasets covered by Non-Disclosure Agreements, we do not publish confidential client names, customer logos, or proprietary metrics.
We do not make unsupported claims about scale or age. We build credibility through:
Documented 6-stage workflows with deterministic assertions and regression testing.
Tri-modal grading combining unit tests, model judges, and domain-expert review.
Clear reporting of error bounds, known failure modes, and conditions where models break.
Concrete demonstration artifacts illustrating exactly how findings and metrics are delivered.
Confidentiality Standards
Client data handling, access controls, storage, retention and deletion requirements are defined according to the individual engagement and applicable confidentiality requirements, including NDA terms where applicable.
Scope an Evaluation with Our Team
Discuss your models, target workloads, and evaluation criteria with our in-house engineering team under mutual NDA.