Most in-house AI projects begin by configuring agents and writing prompts to try and generate a quick use case, but output quality is largely determined by the state of your data.
AI can only work with what it is given – if sources are incomplete, duplicated or poorly permissioned, you invite inconsistent results and unnecessary risk. Effective preparation for AI addresses data quality, lineage, ownership, access and security from the start to increase the effectiveness of every output.
Computer One’s data preparation service creates a clear path to value – we map your data footprint, apply controls to sensitive information, define what to train on versus what to reference at runtime, and help you choose an appropriate model and hosting approach. This gives you a practical foundation for pilot tests and scaling-up solutions. Explore the key features below.
It’s because we focus so heavily on your data foundation at the front-end of your AI journey. Our team maps the sources of your data, consolidates it, detects and solves duplication, improves its quality, and implements clear permission and privacy controls. You gain a dependable baseline that makes your AI outputs accurate, secure and auditable.
We then engineer practical pipelines and governance that keep data current – with monitoring, review cadences and change control. The result is a ready-to-use data layer for AI that reduces rework and risk, shortens time to an acceptable ROI, and scales with your organisation.
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It covers understanding what data you have, how trustworthy it is, and whether you are allowed to use it for AI. We assess quality, lineage, ownership, consent and retention policies. The output is a prioritised plan to close gaps and unlock the first use cases safely.
Training data should be durable, well-governed and representative of the tasks you want automated. Highly sensitive or fast-changing information is often better kept as reference through retrieval rather than baked into the model. We document these choices with clear inclusion and exclusion rules.
Yes. Hosting and storage options can enforce that both data and AI workloads remain in Australian regions. We map regulatory, contractual and policy requirements to architecture choices and document residual risk and controls.
Create a clear information architecture with purposeful sites, libraries and folders, and remove redundant or duplicate content. Fix permission inheritance so access maps to business roles, then apply sensitivity labels and retention policies to protect confidential material. Add consistent naming and metadata standards, enable versioning, and restrict external sharing where appropriate. This groundwork improves findability, reduces noise, and ensures only the right people and processes can use the right data.
We implement data classification, redaction and boundary controls before any rollout. Policies, prompt guardrails and retrieval filters reduce the chance of disclosing confidential information. We also configure logging scopes and retention to minimise exposure.
Fine-tuning is suitable when you need a model to learn your style or specialised terminology and it will not change frequently. RAG is effective when knowledge changes often or is sensitive, because documents stay outside the model and are fetched at runtime. We evaluate cost, accuracy and maintenance before recommending either approach.
We define quantitative quality targets for completeness, duplication, freshness and consistency, and test against them with sample tasks. We also set acceptance thresholds for output accuracy and explainability. These measures are tracked in dashboards after launch.
A focused discovery, a small number of high-value use cases, and a time-boxed proof of value with production-grade data controls. This produces evidence to refine the business case and the roadmap. We prioritise reuse so early work becomes a foundation, not a throwaway effort.
Nominate accountable data owners for each domain (for example, Finance, HR and Operations) and define their decision rights. Stewardship includes regular quality checks, access reviews, schema and taxonomy change control, and management of consent and retention. Provide playbooks and a RACI so teams know how to raise issues and approve changes. Set review cadences with simple metrics – completeness, freshness and access accuracy – to keep the data AI-ready.
Pipelines, permissions and knowledge sources evolve as your organisation changes. We can schedule regular quality checks, access reviews, and model or prompt evaluations and work to embed that knowledge in your team. We can also monitor cost per task and drift, with pre-agreed triggers for retraining or design updates.
