Automate classification, retention and approval workflows for legacy records
Governments and enterprises in Saudi Arabia/MENA waste massive resources on legacy record systems that keep critical knowledge locked and inaccessible
SijilForge guides government teams through digitizing legacy records with built-in Saudi classification schedules and retention rules. AI automatically tags content, routes records for approvals, applies PDPL redaction where needed, and maintains immutable audit trails for every action taken on a record.
Government agencies and large enterprises in Saudi Arabia and the MENA region managing legacy records
Ships with pre-configured Saudi National Records Classification System (NRCS) and PDPL rules, allowing agencies to achieve compliance in days instead of months.
supportive and authoritative
AI that automatically assigns records to official Saudi government categories
Applies correct retention periods and triggers destruction or archival workflows
Configurable multi-level approval routes with digital signatures
Automatically identifies and redacts personal data per PDPL requirements
Blockchain-inspired logging of every access and modification
Process boxes of physical records with barcode tracking
Visual dashboard showing all upcoming retention and review dates
Connect with existing e-government platforms (Absher, Muqeem)
Notifies when Saudi regulations change affecting your records
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| license_number | text | No |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| agency_id | uuid | No |
| title | text | No |
| classification_code | text | No |
| retention_period | int | No |
| status | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| record_id | uuid | No |
| current_step | text | No |
| approver_id | uuid | Yes |
| status | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| record_id | uuid | No |
| action | text | No |
| user_id | uuid | No |
| timestamp | timestamp | No |
Relationships:
/api/records/bulk-uploadProcess batch of physical or digital records
/api/classifyRun AI classification against NRCS taxonomy
/api/workflows/approveApprove or reject record at current workflow step
/api/compliance/reportGenerate PDPL and retention compliance report
/api/records/retention-dueList records requiring action in next 30 days
2 users
10 users
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 35 | 11% | $135 | $1,620 |
| Month 6 | 280 | 27% | $2,646 | $31,752 |
SijilForge automates Saudi classification, retention policies, and PDPL compliance for government agencies and enterprises. Turn chaos into controlled, auditable processes.
Reach out to records managers at Saudi ministries (Interior, Finance, Health) via warm introductions from consultants familiar with Vision 2030 projects. Offer free compliance gap analysis workshops that naturally lead to SijilForge pilots. Target large enterprises in Riyadh and Jeddah through the Saudi Federation for Cybersecurity and the Digital Government Authority network.
Very mature compliance features
Generic, not tailored to Saudi taxonomy and extremely expensive
Purpose-built for Saudi NRCS and PDPL with much faster implementation
Good workflow builder
Limited Arabic AI and no prebuilt Saudi government rulesets
Out-of-the-box Saudi compliance vs months of configuration
Cloud document workflows
No specialized government classification engine
Deep domain knowledge of MENA government record schedules
Embedded regulatory intelligence (Saudi classification schedules and PDPL rules) that improves with every regulatory update. Agencies become dependent on the accuracy of our regulatory database.
The combination of Saudi Vision 2030 digital transformation mandates, the 2022 PDPL enforcement, and the new National Data Management Office standards have created urgent demand for compliant records modernization.
Incorrect classification leading to regulatory penalties
Human-in-the-loop approval at critical steps and extensive testing against official NRCS examples
Slow government procurement processes
Focus initial sales on semi-government entities and large enterprises with faster decision cycles
Keeping regulatory rules up-to-date
Build relationships with regulatory bodies and implement automated policy change monitoring
Success: Confirmed product-market fit and willingness to pilot
Success: 85%+ classification accuracy on test dataset
Success: Contract signed and successful audit log delivered
Success: 3 additional customers within 90 days of first case study
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