The data engine
behind frontier AI
AxionX gives AI labs the evaluation frameworks, expert datasets, and reproducible training infrastructure needed to build models that actually perform beyond benchmark scores.
Built for research teams who demand rigor
Every capability is designed around reproducibility, traceability, and the trust required to publish, deploy, and iterate on frontier models.
LLM Evaluation Frameworks
Rubric-based, reproducible evaluation systems that benchmark reasoning, instruction-following, and domain knowledge. Every score is traceable, every rubric is human-readable.
Expert Dataset Curation
Domain specialists design and annotate SFT, DPO, and RLHF datasets with multi-stage QA and full lineage. We cover 10+ verticals including healthcare, legal, finance, and code.
Reproducible Training Pipelines
Dockerized, experiment-tracked training runs for fine-tuning open-source and proprietary models. Every experiment links its dataset, config, and artifact, and stays reproducible at any point.
Safety & Red-Teaming
Adversarial prompting, jailbreak testing, and bias audits conducted by human experts. We find what automated scanners miss, and we document every finding with structured reports.
RL Environments & Benchmarks
Custom reinforcement learning environments built from real-world scenarios, and long-context/reasoning benchmarks designed to expose true model capability beyond standard leaderboards.
Evaluation Infrastructure
End-to-end evaluation platforms: automated scoring pipelines, human-in-the-loop review dashboards, and A/B model comparison tooling integrated directly into your release process.
Proxy metrics aren't enough.
Neither are we.
- ✓Domain experts in healthcare, law, finance, and code, not generalist annotators
- ✓Rubrics designed with your research team, not one-size-fits-all templates
- ✓Every eval pipeline integrates directly into your CI/CD, with scores on every checkpoint
- ✓Full data provenance: every annotation links to the annotator, rubric version, and timestamp
- ✓Red-teaming conducted by humans who think adversarially, not just prompt libraries
- ✓Reproducible training environments: same dataset + same config = same model, every time
Evaluation-first
We treat evaluation as a first-class engineering problem, not an afterthought before deployment.
Data integrity
Full lineage on every annotation. GDPR-compliant handling. Versioned, reproducible datasets you can trust.
Speed without shortcuts
Fast turnarounds backed by deep automation, without sacrificing the expert QA your models need.
From first call to eval pipeline in days
Align on eval criteria
We work with your research team to define rubrics, benchmarks, and annotation guidelines that match your model's actual use case.
Build the pipeline
Automated evaluation infrastructure is set up with CI/CD hooks, and every model checkpoint gets scored consistently without manual intervention.
Expert annotation at scale
Domain specialists annotate preference data, adversarial prompts, and evaluation sets with multi-stage QA to guarantee data quality.
Report & iterate
Structured reports with per-dimension scores, failure analysis, and actionable recommendations delivered on your release cadence.
Ready to build AI you can trust?
Book a free 30-minute discovery call with our team. No pitch deck, just a real conversation about your model.