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These are the requirements for our Discovery engagements, not a claim that every described software capability is already operational. Scope, permissions, providers and retention must be agreed and checked before client evidence is processed.
What AI may assist with
Within approved processing routes, AI may assist with transcription, extraction, organising evidence, reconstructing workflows, identifying gaps, preparing questions, analysis and drafting findings. The tools used depend on the engagement and the suitability of its data.
This describes the intended use of Discovery infrastructure. It is not a claim that every capability is already built or that AI automatically makes an investigation accurate.
A named human remains responsible
A named InnovAIte operator leads the engagement. Material conclusions need traceable supporting evidence and human review before release; they are not approved simply because a model produced them.
Missing or contradictory evidence calls for investigation, not an assumption disguised as a fact. Unresolved uncertainty should be visible in the final findings.
Standard Discovery is not designed to make solely automated decisions about individuals with legal or similarly significant effects. Any future change would require a separate assessment and appropriate information and safeguards before processing begins.
Several models agreeing does not establish truth
Where appropriate, separated AI reasoning and review stages may help challenge a finding against its source evidence. Their agreement alone does not validate it, and shared errors remain possible.
Evidence remains the authority. Material disagreement must be investigated, with further evidence requested where needed, rather than resolved by a vote or by averaging the answers.
AI only where it earns its place
The right intervention might be a process change, an integration, straightforward rule-based automation, conventional software, AI assistance or leaving a human process in place.
Our approach is model-agnostic. Where AI fits, the model and provider should be selected for the task, reliability, cost, oversight and data requirements—not loyalty to a particular vendor. We recommend the simplest approach capable of reliably meeting the business need.