AI case management for the public sector pairs a full case-file system — intake, assessment, approvals, documentation, closure — with an AI layer that drafts the next step with evidence and routes consequential actions to human authorisers. MAIA configures this from its substrate: hash-linked audit trails on every record change, multi-stage approval workflows, no-code reporting, and a copilot grounded in live case data with citations on every answer.
Where case systems fail today
Public-sector case work dies in the gaps between systems: the spreadsheet beside the system of record, the approval that lives in email, the report assembled by hand each quarter. Every gap is lost evidence — and in government, evidence is the product.
A substrate approach closes the gaps structurally: one ontology for cases, assets, parties, and obligations; workflows that carry their approvals with them; documents and communications captured into the file rather than around it.
AI that respects due process
In case work, the AI's job is not to decide — it is to prepare the decision: assemble the file, check it against policy, draft the determination with the governing provision cited, and put it in front of the accountable officer. Four honest verdicts (meets, does not meet, information missing, needs human judgment) keep discretion where statute puts it.
Because every AI contribution is sealed with its evidence, the file that reaches a tribunal or an auditor is stronger, not weaker, for the automation.