Research Lab
Our research division publishes benchmarks, white papers, and ethical frameworks that set the standard for private enterprise AI. Clients don't just buy our tools — they buy two decades of distilled expertise.
Performance Data
All benchmarks run on client-supplied test sets. We publish methodology alongside results — because transparency is the only benchmark that matters to compliance teams.
General models are trained to be average across millions of tasks. A 7B parameter SLM trained exclusively on your domain corpus will consistently outperform a 100B parameter general model on your specific use cases — because specificity beats scale.
97.3% vs 81.2% on Healthcare NLP →At 68ms p99, our SLMs enable real-time document processing, live conversation, and sub-second decision pipelines. API latency from GPT-4o averages 1.8 seconds — which is 26× slower and disqualifies it from most production workflows entirely.
26× faster than GPT-4o →At 1M queries/day — a common enterprise volume — GPT-4o costs $30,000/day in API fees. Our SLM at the same volume costs $80. The ROI case is not subtle: it's a 375× cost reduction that funds entire engineering teams.
375× cost reduction at scale →Our Stance
We believe that private AI done right is more ethical than public AI done carelessly. Our principles are not a marketing checkbox — they are architectural constraints baked into every system we build.
We train models on the minimum viable dataset required for domain performance. No unnecessary data collection, no retention beyond training windows, and full data lineage documentation for every model artifact.
Every prediction our models make can be traced. We integrate SHAP, LIME, and attention visualization into production deployments — because regulators and auditors need answers, not black boxes.
Before any model goes to production, it undergoes demographic parity analysis, counterfactual fairness testing, and adversarial probing across protected attribute categories. Bias reports are delivered to clients, not hidden.
No AntimKros system makes final decisions that affect people without a human review layer. We architect for AI-assist, not AI-replace, in any workflow touching healthcare outcomes, financial access, or legal judgments.
Training data provenance is fully documented. We do not use scraped data without verifiable consent, and our client agreements include explicit data usage boundaries, ownership clauses, and the right to model deletion.
Our benchmarking methodology, evaluation harnesses, and ethics frameworks are published openly. We believe the enterprise AI ecosystem improves when best practices are shared — even with competitors.
Private AI is not a workaround. It is the only responsible path for industries where data is life, law, or livelihood.
We founded AntimKros on the belief that the most powerful AI system is one your team fully controls — not one that controls you through dependency, opacity, and escalating API costs. Every architectural decision we make reflects this conviction. Sovereignty is not a feature. It is the product.
— AntimKros Research Division, 2025
The Architects
Our research team spans NLP, distributed systems, cryptography, and regulated-industry domain knowledge. We don't hire generalists — we hire people who have solved your exact problem before.
Chief Research Officer
Former NLP lead at a top-5 global bank. PhD in Computational Linguistics. Specialist in financial document understanding and regulatory NLP.
11 published papers · Ex-JPMorgan AI Lab
Head of Model Architecture
Designed training infrastructure for sub-10B parameter models at two unicorn AI startups. Expert in PEFT, LoRA, and quantization pipelines for edge deployment.
7 published papers · Ex-Mistral AI
AI Security Research Lead
Former CISO at a regional healthcare network. Pioneered air-gapped ML deployment protocols now adopted by three national health ministries. CISSP certified.
9 papers · Keynote: NeurIPS Security Workshop
ML Systems Engineer
Built real-time ML inference pipelines processing 800M+ daily predictions at a global logistics firm. Expert in MLOps, drift detection, and zero-downtime model rollouts.
5 papers · Ex-Flexport · Ex-Google Brain
Enterprise buyers who engage with our research first close 3× faster. We publish transparently because we're confident the data speaks for itself. If it convinces you, let's talk.