Tools
We help you build systems you can trust
Our tools
At I/O, we ensure your AI and software development processes are clean, compliant, and optimised from the ground up. Our tools assess open source and AI model licence compatibility, detect legal conflicts, and generate actionable reports—so your innovation stays safe and scalable. We go beyond licences, streamlining your AI pipeline, validating data integrity, and verifying governance frameworks to ensure quality, accountability, and performance at every stage. From training data to deployment, we help you build systems you can trust.
Open source licence compatibility
Our licence compatibility toolset cross-checks the licences of all third-party open source components in your codebase, automatically flagging incompatibilities, strong and ultra-strong copyleft risks, and obligations that could interfere with proprietary use, distribution, or commercialisation. Designed for agile environments, the system maps complex licence stacks, supports SPDX standards, and generates clear, actionable reports for legal and engineering teams alike. Whether you’re mixing permissive and reciprocal licences or embedding open components into closed systems, we ensure you ship software that’s clean, compliant, and collaboration-ready.
AI models licence compatibility assessment
Our tool helps you navigate the tangled web of licences attached to pre-trained models, datasets, and APIs, so your AI system doesn’t break the rules before it even starts learning. We automatically parse and classify licence terms (commercial use, redistribution, derivative creation, usage restrictions), detect machine-readable opt-outs, detect conflicts between upstream model and data sources, and assess their compatibility with your intended deployment scenario. Whether you're building on foundation models like LLaMA, integrating fine-tuned open weights, or layering proprietary datasets on top, we provide a legal-technical compatibility check tailored for AI development workflows.
AI pipeline optimisation
We fine-tune more than models—we fine-tune the entire lifecycle. Our AI pipeline optimisation service identifies inefficiencies, redundancies, and compliance risks across your machine learning workflow: from data ingestion and annotation, to model training, versioning, deployment, and monitoring. Using customisable diagnostics, we map your technical and operational stack against best practices in scalability, reproducibility, and governance. Whether you need to reduce training time, boost performance, improve data quality, or embed accountability checkpoints, our tech-first team helps rewire your pipeline for speed, safety, and strategic alignment. Fewer dead ends. More intelligent flow.
Data governance verification
Solid governance starts with knowing your system inside out. Our tools bridge the gap between documented procedures and technical implementation by automatically verifying compliance across the data lifecycle. It maps governance policies to system behaviour, checking for alignment in areas like access controls, logging, retention, and deletion. Whether you’re orchestrating datasets for AI training or managing shared data spaces, the tool provides traceable, real-time evidence that your governance framework isn’t just aspirational but operational.
Data set integrity validation
Garbage in, garbage out. That’s the last thing you want for your AI system. Our tools ensures your data sets are trustworthy from the ground up. It performs automated integrity checks across your pipeline, verifying data completeness, consistency, duplication, and tamper-resistance. From ingestion to pre-processing, it detects anomalies, versioning errors, and silent corruptions that could compromise downstream performance or compliance. With detailed audit logs and validation reports, it gives you assurance that your data is not only available, but reliable.
Data training, validation and testing tools
From initial dataset curation to final model evaluation, our tools help you build high-quality training, validation, and testing datasets that meet both performance and regulatory expectations. We support versioning, labelling consistency, class balance monitoring, and statistical testing, making sure your data workflows are as robust as the AI models they shape. No matter if you're building a supervised learning pipeline or refining a foundation model, we equip you with the architecture and oversight to make every iteration count.