01Input
Data substrate
Role-based access, structured records across 261 assemblies.
AI for government & public sector
AI workflows for public institutions that already need to be auditable.
Public-sector work cannot afford unreliable output. We build the operational substrate first — structured records, audit trails, role-based access — then layer AI where it can safely multiply throughput.
Structured
Records and roles before summarization
Standardized
Reporting fields across units
Reviewable
Approval trail before AI output is used
Public-sector references are founder background and method context. They are not presented as Claver Consult client proof.
The problem
Public-sector AI fails when there is no trustworthy substrate.
Most government AI experiments stall on the same problem: fragmented systems, inconsistent records, and no audit trail. You cannot bolt reliable AI onto unreliable foundations. The work of public-sector AI starts with the operational substrate — and only then with the model.
The workflow
The operating path for government work.
Each step has an explicit owner, a controlled AI role, and a review point before the output becomes operational.
02Human
Operational workflow
Replaces ad-hoc spreadsheets with reviewable, recordable actions.
03Approval
Audit trails
Every action reviewable; accountability preserved at national scale.
04Delivery
Standardized reporting
One reporting pipeline across every district in Ghana.
05AI draft
AI augmentation surface
Triage, summarization, drafting layered onto trustworthy data.
Structured data substrate first, audit trails baked in, standardized reporting across every unit — then AI augmentation on a foundation that can be reviewed.
What we ship
Possible workflows for government & public sector.
- 01
Citizen service triage and structured intake
- 02
Standardized reporting across districts or units
- 03
Procurement document drafting with policy guardrails
- 04
Internal knowledge retrieval with audit trails
- 05
Inter-agency handoff and exception escalation
Related example
A sample breakdown in this space.
Government & operations
Sample workflowPublic operations workflow pattern from founder background
A background-informed sample showing how public operations need structured records, role-based access, reporting consistency, and reviewable handoffs before AI belongs in the workflow.
Sample workflow simulation informed by founder background. Not presented as Claver Consult client data.
Deployment footprint
Messy state: Fragmented unit-by-unit recordsStructured state: Shared operating model across units
FAQ
The questions government leaders ask first.
- Can AI be used at all in a public-sector workflow?
- Yes — but only on top of a substrate that supports audit and review. The first engagement question is usually whether the substrate exists. If not, that comes first.
- How do we handle sensitive citizen data?
- Role-based access, redaction at the intake step, and clear retention rules — designed before any model is involved. The AI surface is the last thing we wire up, not the first.
- What's a realistic first workflow?
- Almost always: structured triage. The classification step is where AI safely multiplies capacity without putting decisions in the model's hands.
Next step
Bring one government workflow.
The fastest way to evaluate fit is to start with a workflow your team already knows is expensive, slow, or inconsistent.