The file is not the hard part. The exceptions are.
The problem is everything that happens after rows fail.
Bordereaux workflows rarely break on upload. They break after bad rows appear and someone has to figure out what failed, what should be fixed, what should be allowed, and whether the file is safe to release.
- • Source formats drift without warning.
- • The same partner-specific issues repeat every reporting cycle.
- • Analysts end up resolving exceptions in spreadsheets, email threads, and one-off scripts.
- • Teams rerun too much work to verify small fixes.
- • Release slows down because there is no clear remediation workflow.
What Qluster does
Qluster sits between inbound bordereaux and downstream release. It gives teams one place to validate incoming files, work failed rows, approve reusable decisions, and keep a visible record of how the data moved from failure to release.
Import
Bring in recurring bordereaux and delegated reporting files without rebuilding intake each cycle.
Flag bad rows
Surface row-level failures immediately and show exactly what broke.
Draft fixes
Use AI to draft row-level corrections and starter policies while keeping humans in control.
Review and approve
Work failed rows in a spreadsheet-like review flow and decide what should change.
Reuse what works
Promote approved decisions into reusable mappings and policies so repeated issues stop being manual work.
Release with confidence
Recheck only the affected rows, keep a full audit trail, and send clean data downstream only when ready.
What changes for your team
Qluster replaces the spreadsheet-and-email cleanup loop with a controlled remediation workflow. Teams ship cleaner bordereaux faster, with a record of every decision.
Less repeated cleanup
Approved fixes become reusable mappings and partner-scoped policies, so the same exceptions stop reappearing every cycle.
Faster reporting cycles
Only the rows that changed get rechecked, so small fixes do not restart the full bordereau.
Release-ready confidence
Every correction, approval, and promotion is logged, so the path from failure to release is visible end to end.
Built for multi-source
Many MGAs, TPAs, and coverholders can feed one shared dataset, with each external user scoped to their own rows.
Built for exception-heavy bordereaux workflows
Structured
Give teams a clear process for investigating failed rows, applying corrections, and confirming resolution.
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Repeatable
Turn approved fixes into reusable mappings and scoped policies so the next reporting cycle takes less work.
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Auditable
Track what changed, who changed it, and why, so the path from failure to release is visible end to end.
Learn moreWho Qluster is for
Capacity-side teams
For carriers, reinsurers, and program teams receiving recurring bordereaux from many upstream sources and needing one controlled review and remediation workflow.
MGA and submission teams
For insurance teams that want to reduce repeated cleanup, respond faster to exceptions, and send cleaner bordereaux upstream.
Qluster is typically delivered as a managed cloud service. Dedicated private deployments may be discussed for qualified teams.
Built by the creator of DeepDiff
Qluster is built by a team with serious data tooling experience. DeepDiff is widely used in Python workflows where correctness and explainability matter.
See Qluster on a real bordereaux workflow
Bring a real file or a representative sample. We will show how failed rows are flagged, how fixes are reviewed, and how approved decisions make the next reporting cycle faster.
Book a Demo