AI that finishes your Nigerian paperwork automatically
South African businesses face more friction, delays, and administrative complexity paying suppliers in Lagos than in London.
DocNaira uses specialized AI (RAG over thousands of past SA-NG trade documents) to extract data from supplier invoices, auto-complete CBN and SARB forms, flag missing information, and generate submission-ready PDFs. Once documents pass internal scoring, it triggers payment via integrated gateways. Importers upload once and receive complete compliant packages.
South African businesses and importers trading with Nigerian suppliers
Domain-specific RAG model trained exclusively on successful South Africa-Nigeria trade documents, achieving 94% first-time approval rate versus generic OCR tools.
professional
Mobile + web upload with automatic classification (invoice, packing list, certificate of origin)
Uses OCR + LLM to pull supplier details, amounts, HS codes with confidence scores
Auto-populates all required regulatory forms using extracted data
Gives compliance readiness percentage with specific fix recommendations
After documents approved internally, seamlessly triggers Paystack payment
Creates complete submission package with digital signatures for records
Users can teach the AI their specific supplier patterns
Handle 50+ invoices in one upload for large importers
Track changes and corrections across document iterations
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| company_name | text | No |
| is_verified | bool | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| original_filename | text | No |
| file_url | text | No |
| status | text | No |
| compliance_score | int | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| document_id | uuid | No |
| field_name | text | No |
| extracted_value | text | Yes |
| confidence | int | No |
| corrected_value | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| content | text | No |
| embedding | text | Yes |
| source_regulation | text | No |
Relationships:
/api/uploadUpload document and trigger AI processing
/api/documents/{id}/extractRetrieve AI extraction results with confidence scores
/api/compliance/generateGenerate filled regulatory forms as PDF
/api/rag/queryQuery the trade regulation knowledge base
10 docs/month
None
None
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 65 | 14% | $227 | $2,724 |
| Month 6 | 850 | 21% | $4,462 | $53,544 |
AI specialized in South Africa-Nigeria trade turns your supplier invoice into complete compliant payment packages in under 60 seconds.
Contact importers who frequently complain about documentation delays in the 'South African Importers' Facebook group and offer free Enterprise access for 90 days in exchange for video testimonial. Partner with two freight forwarders in Johannesburg who handle Nigeria routes and integrate DocNaira into their client portal as a value-add. Reach out to members of the Durban Chamber of Commerce & Industry involved in African trade.
General document management
No domain knowledge of African trade regulations
Specialized RAG model for SA-NG trade that actually understands the rules
Strong OCR
Generic and extremely expensive
10x cheaper and purpose-built for this exact use case
Continuously improving RAG dataset of real approved SA-NG trade documents that becomes more accurate with every user.
Maturity of multimodal LLMs combined with explosion of intra-African trade volume under AfCFTA creating perfect conditions for specialized AI tools.
AI hallucination on compliance fields leading to rejected payments
High-confidence-only approach with human review prompts and continuous model fine-tuning
Liability if AI-generated documents cause regulatory fines
Clear disclaimers, insurance, and human-in-the-loop approval before submission
Success: Achieve >88% accuracy on key fields (HS codes, values, origins)
Success: Minimum 12 users report saving >12 hours per month
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