ryora.ai

An applied AI lab building platforms to define, deploy, and govern agent factories

The cutting edge of applied AI is advancing faster than most enterprises can safely absorb. New models, runtime techniques, memory systems, and reasoning architectures are emerging continuously, while the gap between experimentation and production continues to widen due to risk, cost, and control constraints.

ryora.ai is an applied AI lab focused on closing that gap.

We believe the future of applied AI is not individual models or standalone agents, but agent factories: coordinated systems of many specialised agents, each optimised for a specific task, working together to solve complex problems. These systems promise significant gains in capability and adaptability, but introduce new challenges around visibility, governance, cost control, and operational safety.

To unlock this scale, we build platforms that define, deploy, and govern agent factories as first-class production systems. Agent behaviour, configuration, cost, and risk are treated as observable and operable components of the system itself, enabling organisations to adopt high-risk, high-reward applied AI capabilities without losing control.

Our platforms support structured experimentation at scale, rapid adoption of new models and architectures, and a controlled progression from prototype to production, underpinned by the GRASP risk assessment framework.

The Problems

1

Risk oversight and governance break down

Unlocking real AI value requires agents to operate with autonomy. But as agents become more capable and distributed, organisations lose visibility into where they exist, what they can do, what they have access to, and how risk evolves over time. Without real-time signal, governance defaults to blunt restrictions, removing leverage instead of managing it.

2

Optimising for cost and reliability requires significant experimentation

Getting the best results from AI means testing across models, configurations, and architectures. But setting up and running those permutations is expensive and time-consuming. Token spend rises quickly, experimentation pipelines are brittle or bespoke, and adopting new models often requires re-engineering workflows or re-accepting unknown risk. Organisations struggle to test aggressively, identify the smallest effective model, or keep pace with rapid model evolution.

3

Risk and speed constraints create a structural stall under competitive pressure

When risk cannot be assessed in real time, governance mechanisms default to slowing autonomy. When experimentation is costly or fragile, teams struggle to demonstrate value quickly enough to justify that autonomy. The organisation enters a holding pattern: progress is cautious, deployments are incremental, while competitors continue to move faster by accepting or better managing the same risks. Over time, this compounds into an existential threat, as the organisation loses its ability to compete on speed, cost, and decision quality.

GRASP

The GRASP Framework

The GRASP framework provides a structured approach to reason about AI agent risk. The goal is not to eliminate risk, but to make it explicit, understood, and consciously accepted.

G — Governance

How the agent was developed and approved: Evaluations and testing, SDLC integration, observability and audit trails.

R — Reach

What the agent can access: Explicit tools and APIs, indirect access via system, network, or shell permissions.

A — Agency

How autonomous the agent is: Human-in-the-loop points, triggers and execution boundaries, decision-making authority.

S — Safeguards

What limits the blast radius: Reversible actions, scoped permissions, controls that prevent incidents from becoming catastrophic.

P — Potential Impact

The worst-case impact to the business: Financial, operational, security or reputational.

GRASP provides a shared language for engineers, executives, and risk stakeholders to reason about AI agents with clarity.