paterhn builds production AI systems for enterprise. Multi-agent orchestration on your infrastructure, delivered in 12 weeks. You own the code, the models, the IP.
Real outcomes across industries
fraud false positives reduced
Agentic fraud/compliance stack, measurable ROI in weeks.
analyst review time cut
Agentic compliance layer with sub-second evidence packs.
unplanned downtime reduced
inventory carrying costs down
R&D cycle time reduced
Building in these spaces? Reach out, we'll walk you through exactly what we've shipped.
Get in touchWe are your external AI R&D function, measured by shipped systems, not slides.
When internal teams are focused on core roadmaps, navigating governance, or stuck between prototype and production, we step in with a senior engineering team to ship working systems.
We don't sell recommendations. We build auditable AI on your infrastructure, then transfer ownership to your team.
Read more about our workProduction-ready AI systems tailored to your specific needs
Autonomous agents that collaborate, reason, and act, orchestrated to solve complex workflows with verifiable outcomes
Domain-optimized large language models trained on your data and deployed within your infrastructure
Enterprise-grade AI systems with full observability, governance, and compliance
Complete AI systems designed for your industry, from architecture to deployment
From agentic orchestration to industry-proven deployment
From concept to production in 12 weeks or less, powered by our Proof of Value model.
Each engagement follows our PoV approach: build a working AI prototype from first principles; by week 12, you have production-ready software, benchmarked against your operational baselines.
Assessment & Architecture
Core Development & Iteration
Production & Handover
No vendor lock-in. No licensing fees. No hidden dependencies. What we build becomes yours. Completely.
AWS, GCP, Azure, or on-prem
Complete data sovereignty
100% ownership forever
We train them, then step back
Real engineering, applied AI, and lessons learned shipping production AI.
Token consumption is an input, not an output. How to budget machine cognition by cost per accepted unit of work: define the accepted unit, price consequence with crossover arithmetic from public benchmark data, and follow one case through the routing architecture.
Open weights release the graduate, not the school. Kimi K3 shows why the difference from open source matters: the license activates at scale, while the architecture keeps the model swappable. Across paterhn deployments, open models carry at least 60% of production agent tasks.
From kickoff to production system. Not a demo—a working system on your infrastructure.
