Turn disconnected legacy records into an interactive knowledge graph
Governments and enterprises in Saudi Arabia/MENA waste massive resources on legacy record systems that keep critical knowledge locked and inaccessible
WathiqWeave analyzes relationships between people, events, projects, and decisions across thousands of legacy documents to automatically build a living knowledge graph. Decision makers can visually explore how policies evolved, trace project histories, and discover connections that would be impossible to find in siloed archives.
Government agencies and large enterprises in Saudi Arabia and the MENA region managing legacy records
Specialized entity recognition and relationship extraction trained on Arabic governmental, tribal, and historical contexts unique to the MENA region.
insightful and visionary
Identifies people, organizations, projects, and decrees in Arabic texts
Automatically extracts and links relationships between entities
Visual interface to navigate connections between records
Automatically constructs chronological views of related events
Follows how specific policies or projects evolved through records
Advanced search using relationships (e.g. 'show all projects linked to Minister X')
Generate PDF briefings from graph explorations
Allow multiple analysts to annotate and expand the graph
Overlay of major national events on the knowledge graph
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| organization_id | uuid | No |
| name | text | No |
| type | text | No |
| metadata | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| source_entity_id | uuid | No |
| target_entity_id | uuid | No |
| relation_type | text | No |
| confidence | int | No |
| source_document_id | uuid | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| organization_id | uuid | No |
| title | text | No |
| content | text | Yes |
Relationships:
/api/graph/exploreQuery the knowledge graph with relationship filters
/api/entities/searchSearch for entities with autocomplete
/api/timeline/generateGenerate chronological view from graph data
/api/documents/linkAssociate new document with existing graph
Single user
Up to 8 users
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 28 | 14% | $137 | $1,644 |
| Month 6 | 210 | 29% | $2,131 | $25,572 |
WathiqWeave transforms your legacy records into an interactive knowledge graph so you can trace decisions, understand relationships, and uncover insights buried for decades.
Begin with cultural and historical preservation departments in Saudi Arabia and UAE who manage large legacy collections. Use case studies from initial pilots showing discovered relationships to attract planning and strategy departments. Present at academic and government history conferences in the region to build credibility and secure initial contracts.
Powerful graph database
Requires significant data science expertise to populate
Fully automated entity and relationship extraction from Arabic documents
Extremely powerful ontology tools
Way too expensive and complex for most MENA agencies
Purpose-built micro-SaaS for government archives at fraction of cost
Excellent visualization
No automatic knowledge extraction from documents
Combines extraction, graph building, and visualization in one platform
Specialized Arabic governmental NER (Named Entity Recognition) models for names, decrees, and programs that improve with each customer deployment. The resulting knowledge graphs become proprietary assets that are hard to replicate.
Recent breakthroughs in graph RAG and multimodal LLMs combined with Saudi Vision 2030's emphasis on evidence-based decision making have created the perfect conditions for knowledge graph adoption in government.
Inaccurate relationship extraction leading to misleading insights
Confidence scoring, human validation workflows, and continuous model improvement based on user feedback
Target users may not be comfortable with graph visualizations
Provide multiple ways to consume data (visual, timeline, briefing reports)
Solo developer managing complex graph algorithms
Leverage mature open source libraries and LLM-based extraction to reduce custom algorithm development
Success: Positive feedback on discovered non-obvious connections
Success: Users can successfully trace 3 real historical decisions
Success: They load at least 500 legacy records and use graph weekly
Success: Convert 2 additional agencies from case study exposure
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