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Public sector grant teams can use AI Powered Due Diligence to generate, log, and reproduce a risk summary on every UK company or charity applicant. Auditors get a complete trail of who reviewed what, when, and what data the summary was built from. Flexigrant logs every action automatically with primary UK data residency.
- Auditors expect documented, reproducible due diligence on every public sector grant decision.
- Manual due diligence creates gaps because depth varies per applicant and the trail lives in shared drives.
- AI Powered Due Diligence runs consistent checks and logs every action automatically.
- Flexigrant holds all data primarily in the UK and produces the audit trail public sector accountability requires.
What auditors actually look for in public sector grant due diligence
The audit question is not "did you check this applicant". It is "can you show, end to end, what you checked, when, and what you concluded". An auditor walks through a sample of awards and asks for the documented evidence on each one. If the documentation is thin, the audit finding follows.
The three things auditors check most often:
- Was the same depth of check run on every applicant in scope?
- Is the data source for each signal documented?
- Is the human decision logged with reasoning?
Manual due diligence often fails on item one. Time pressure means depth varies. Items two and three depend on staff discipline.
How AI changes the audit picture
AI Powered Due Diligence runs the same check on every applicant. The check pulls structured signals from documented sources. Every action logs with the user, the timestamp, and the source data. The grants officer reviews the summary and logs the decision. The reasoning is captured at the time, not reconstructed later.
For an auditor, the trail looks the same on applicant 1 and applicant 200. No rationing. No reconstruction.
Audit evidence: before and after AI Powered Due Diligence
| Audit dimension | Manual due diligence | Flexigrant AI Powered Due Diligence |
|---|---|---|
| Depth of check per applicant | Variable | ✓ Consistent across all applicants |
| Documented data source per signal | Notes if remembered | ✓ Logged automatically per source |
| Human decision logging | Email or shared drive | ✓ Logged in system per applicant |
| Reproducibility of the check | Reconstructed for audit | ✓ Reproducible from the audit trail |
| Time to assemble audit pack | Days | ✓ Hours |
| UK data residency | Varies by setup | ✓ Primary UK hosting |
AI audit trail across GMS platforms
| Capability | Flexigrant | Fluxx | Goodgrants | SmartSimple | Salesforce GM |
|---|---|---|---|---|---|
| AI risk summary per applicant | ✓ Built in, standard | × Custom integrations only | × No standard AI summary | × Custom build only | × Einstein AI + custom build |
| Action logging per AI run | ✓ Automatic, full | Platform audit log; varies by config | Basic activity log | Full audit log | Salesforce Field History |
| Reproducible audit pack export | ✓ Per applicant pack | Export from audit log | Varies by plan | Configurable export | Salesforce reports / export |
| UK data residency primary | ✓ Primary UK hosting | US primary; UK via contract | Multi-region; confirm | Canada primary; UK via contract | UK region via Hyperforce |
| Human override on AI | ✓ Required; AI never auto awards | No AI to override in standard | No AI to override in standard | Depends on custom build | Depends on custom build |
✓ Strong | ! Partial or Limited | × Not Available
Methodology: cells researched against publicly available vendor information through May 2025. Refresh against current vendor documentation before publishing.
How public sector teams roll this out
Three steps in practice:
- Define the rule set and the data sources you expect AI Powered Due Diligence to check, in plain language.
- Run AI Powered Due Diligence on a sample cycle, review the summaries, refine the rule set.
- Roll out to live cycles. Auditors get the trail from day one.
Implementation runs 13 weeks from kickoff to go live, including all 4 AI powered features.
What this means for public sector grant teams
Auditors expect documented, reproducible due diligence on every award. Manual processes leave gaps; AI Powered Due Diligence does not. Every check, every signal, every decision logs automatically with primary UK data residency. The audit trail looks the same on applicant 1 and applicant 200.
One useful action you can run today, no software required: run a mock audit on 5 random awards from your last year. For each, try to produce the full evidence trail (data sources checked, decision reasoning, who reviewed it) in under 30 minutes. The awards where you cannot are the awards an external auditor will flag.
If you want to see how Flexigrant can make a meaningful impact on your organisation, book an initial conversation with us. We start by understanding your context, not by pitching features.
