aion Research

How do you know your AI system is working?

Enterprise AI systems fail in ways that benchmarks don’t catch. We develop multi-level evaluation frameworks that test at the system, component, and task level, including adversarial and domain-specific test suites that reflect real-world operating conditions, not lab conditions.

How do you make a general model work for your business?

Off-the-shelf models underperform on specialised enterprise tasks. We develop foundational models and research efficient fine-tuning strategies, domain-specific data curation, and knowledge injection techniques that close the gap between general capability and operational precision, without requiring massive compute budgets.

How do you build agents that don’t break at scale?

Multi-step agents are brittle. We study failure modes in agent orchestration (state management, tool-use reliability, error recovery, and cascading failures) and publish architectures and patterns that make compound AI systems production-safe.

How do you build systems that get better through use?

The most valuable AI systems learn from their own production data. We research closed-loop optimisation: how to capture user corrections, detect performance drift, and feed signals back into prompt tuning, retrieval, and model updates, automatically and safely.

How do you deploy AI in regulated industries without slowing down?

Compliance teams kill AI projects that can’t explain themselves. We develop methods for decision traceability, output attribution, and confidence calibration that give compliance, risk, and ops teams the evidence they need to approve and trust AI systems in production.

How do you make AI systems fast and affordable enough to run at enterprise scale?

Production AI is constrained by latency budgets and compute costs. We research inference optimization, model compression, efficient training methods, and hardware-aware deployment strategies that make high-performance AI systems viable at scale, without requiring unlimited GPU budgets. The goal: frontier-level capability at a fraction of the cost and latency.