Score prevention projects. Skip the paperwork.
South African municipal officials spend more time reporting on infrastructure collapse than preventing it due to overwhelming layers of compliance, oversight, and approval bureaucracy.
PreventScore ingests municipal infrastructure data and applies a locally calibrated risk algorithm based on flood zones, population density, age, and past failures. It produces a prioritized prevention list and auto-generates all funding applications, compliance certificates, and council submission documents. Officials can now defend prevention budgets with data instead of reacting to collapses after they occur.
South African municipal officials and local government administrators
Risk scoring engine built and validated against actual South African municipal failure data and National Disaster Management Centre frameworks — no international tool has this calibration.
professional
Calculates prevention priority score using 14 SA-specific weighted factors
Live ranked list of assets needing attention with justification
Creates business cases, MIG applications, and council memos in correct formats
Shows cost vs risk reduction trade-offs for different funding scenarios
Shows exactly which regulations each project satisfies before submission
CSV and Excel import with validation against municipal standards
Allow municipalities to tweak scoring weights for local conditions
Charts showing how risk profile changes over time
Secure link sharing with councillors and treasury officials
What-if modeling for climate change and budget cuts
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| province | text | No |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| role | text | No |
| municipality_id | uuid | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| municipality_id | uuid | No |
| name | text | No |
| risk_score | int | Yes |
| risk_factors | text | Yes |
| last_updated | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| municipality_id | uuid | No |
| title | text | No |
| priority_rank | int | No |
| status | text | No |
| generated_docs | text | Yes |
Relationships:
/api/scoreCalculate risk scores for uploaded asset data
/api/projectsReturn prioritized prevention project list
/api/generate-docsGenerate compliance and funding documents for selected projects
Single dataset only
None
None
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 65 | 12% | $226 | $2,712 |
| Month 6 | 380 | 21% | $2,319 | $27,828 |
PreventScore replaces guesswork and mountains of paperwork with a South African risk algorithm that prioritizes prevention and generates every required document automatically.
Identify municipalities that recently lost infrastructure grants due to poor planning. Offer free scoring workshops using their own data. Convert the first three into case studies highlighting grant money protected or collapses avoided, then leverage these stories at SALGA planning conferences.
Strong GIS capabilities
Expensive consulting model, not self-serve
$29 self-service product with instant document generation
Flexible dashboards
No built-in SA risk algorithm or compliance document generator
Purpose-built scoring model and automatic creation of legally required documents
Proprietary risk model continuously improved by anonymized outcomes from participating municipalities, creating a flywheel of better predictions.
National Treasury now requires risk-based infrastructure planning while municipalities face severe budget pressure — creating perfect conditions for a data-driven prevention tool.
Municipalities lack clean asset data to begin with
Include guided data-cleanup wizard and offer initial scoring on partial datasets
Scoring algorithm challenged in council or court
Publish transparent methodology paper and include disclaimers; partner with academic institutions for validation
Success: Model correctly ranks 80% of known failure cases in top 30%
Success: Both produce at least one funded prevention project using generated documents
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