FineSkyAi

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Technology · 1-2 employees · ABN 23673712920

Responsible AI Certified · Embed tierCertified since 3 Mar 2026 · Valid until 3 Mar 2027

Certification ID: RAIA-2026-0004

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Certification IDRAIA-2026-0004

Expires3 Mar 2027

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RAIA-2026-0004

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Responsible AI profile

Self-declared AI practices, submitted as part of this business's certification.

AI usage

How they use AI

We use AI to assist organisations in developing their custom solutions. Currently we develop bespoke tools, applications and fine-tuned models for this purpose.

AI tools in use

Chatbots or virtual assistants (e.g., customer service)Image recognition or computer vision (e.g., quality control in manufacturing)Natural language processing (e.g., content generation or sentiment analysis)Automation or robotic process automation (RPA) (e.g., workflow efficiency)Marketing or content creation (e.g., personalized ads)Other (specify briefly)

Pre-built models & their purpose

We use both pre-built models such as GPT-5, o3-mini-high and Mistral, and custom fine-tuned versions of open source models (Mistral 7B, Qwen 2.5 VL Instruct and Phi-4)

AI use & risk profile

Low-risk (e.g., internal automation or content generation)Other (specify)

Other risk areas

document analysis

Risk & safety

How they identify and mitigate AI risk

Annually updated internal Ethics Policy, adherence to national and global stands (Australian AI Plan, EU AI Act, etc.), ‘red teaming’ (where appropriate), housing private/sensitive information within Australian borders, placing a human-centred approach as the paramount reason for pursuing any AI project.

Fairness audits & human-in-the-loop review

Ability to contest or override AI decisions

Ethics & governance

Follows ethical AI frameworks

Yes

Frameworks followed

Australian AI Ethics PrinciplesInternal company policyInternational standards (e.g., EU AI Act guidelines)

Maintains AI documentation & oversight

Yes

Responsible for oversight

Yes – detailed logs, comms and evaluations as created by the AI Solutions Developer during development, testing and training (fine-tuning) runs.

Privacy & fairness

Privacy & data security (Privacy Act 1988)

Yes

Methods

Data encryption and anonymizationCompliance with Privacy Act via data impact assessmentsThird-party tools (e.g., secure cloud providers)

Prevents AI bias & discrimination

Yes

Methods

Diverse and balanced training dataBias detection tools or algorithmsInclusive testing with varied demographics

Sustainability & society

Environmental impact

N/A, Low-scale use currently.

Societal benefit & responsible scaling