AI Infrastructure for Engineering Leaders
Build AI systems that scale with context, maintain consistency, and deliver reliable decisions—without sacrificing engineering velocity.
Built for Production Engineering
Enterprise-grade infrastructure designed for engineering teams that ship fast
Ship Faster
Stop debugging prompts and edge cases. Our structured approach reduces iteration time by 10x.
Production Ready
Enterprise-grade observability, monitoring, and controls from day one. No black boxes.
Scales Linearly
Context management that grows with your system complexity, not against it.
Testable
Deterministic reasoning means reliable unit tests and integration tests for AI behavior.
Maintainable
Version-controlled reasoning frameworks that your team can collaborate on like code.
Self-Improving
Systems that learn from production feedback and get better without manual intervention.
Engineering Teams Using ZUNFT.AI
LLM-as-a-Judge Systems
Build consistent evaluation and quality control systems that scale across your entire platform.
Result: 95%+ decision consistency, full audit trails, continuous improvement loops
Autonomous Agents
Deploy agents that understand context deeply and make reliable decisions without constant human oversight.
Result: 70% reduction in human intervention, transparent reasoning chains
Complex Decision Systems
Handle multi-stakeholder, multi-criteria decisions with explainable AI that stakeholders trust.
Result: Complete decision traceability, stakeholder alignment, regulatory compliance
AI-Powered Workflows
Orchestrate complex workflows where AI needs to understand business context and make judgment calls.
Result: Reliable automation of complex processes, exception handling that scales
How ZUNFT.AI Works With Your Team
Our proven 8-week process takes you from scoping to production deployment with full observability.
Scoping & Architecture Design
- →We analyze your AI use case (LLM-as-Judge, autonomous agents, decision systems)
- →Design your context structure and reasoning frameworks
- →Define success metrics and observability requirements
Integration & Deployment
- →Connect to your LLM providers (OpenAI, Anthropic, Azure)
- →Deploy PRO-Protocol reasoning layer to your infrastructure
- →Set up Intelligence Layer for context management
- →Configure observability dashboards and monitoring
Production Rollout
- →A/B test reasoning strategies against your baselines
- →Activate Cognitive Loop for continuous learning
- →Train your team on framework management
- →Monitor and optimize decision quality
Self-Improving System
- →Your system learns from production outcomes automatically
- →Frameworks refine based on real-world performance
- →Your team iterates on reasoning strategies
- →Full transparency into all AI decisions and improvements
Ready to Build Production-Grade AI?
Join engineering teams shipping reliable AI systems at scale.