Exporiz
AI that makes exporting simple
The problem Exporiz solves
Micro, Small, and Medium exporters face extremely high friction when trying to export globally. Unlike large companies, they don’t have compliance teams, legal advisors, or data tools to pre-check their shipments. As a result, they frequently run into issues that delay, reject, or destroy their shipments.
Our project solves the following problems:
• Lack of clarity in HS Code classification
Most MSMEs guess the HS Code or rely on incomplete Google search results, leading to incorrect tariff calculations, customs delays, and rejected shipments.
• Hidden non-tariff barriers (NTBs) and country-specific rules
Exporters struggle to understand each country’s labeling, packaging, safety, chemical, and certification requirements. Missing something as small as a CE mark, nutrition label, or warning icon can lead to detention.
• High shipment rejection rates due to compliance mistakes
Regulators like FDA, EU RAPEX, and national customs frequently reject or detain small shipments for minor labeling, safety, or paperwork issues. MSMEs lose money because they don't have tools to pre-check compliance.
• No centralized tool for risk analysis
Exporters don’t know the risk profile of their product—logistical, regulatory, or market-related. This leaves them blind to major issues before shipping.
• Fragmented data sources
All required information (documents, tariffs, risks, importer details) exists, but scattered across government websites, PDFs, and portals that MSMEs rarely understand or access.
• Costly dependence on agents and “export consultants”
Most small exporters spend significant money on consultants who often give generic advice, not detailed product-level risk or compliance insights.
• No AI-powered assistant for quick export guidance
There is no simple, unified interface where an MSME can enter a product and instantly get HS Codes, compliance flags, risk levels, top markets, and document readiness.
Challenges we ran into
• Integrating multiple AI outputs into one structured JSON
Ensuring the AI consistently returns correct schema-validated JSON for HS codes, risk scores, compliance issues, and packaging insights required several prompt iterations and schema tuning.
• Handling inconsistent model responses during early testing
Gemini sometimes returned text blocks instead of JSON, which broke our parser. We solved this by enforcing responseSchema in the API.
• No backend framework in the auto-generated frontend
The frontend environment (Lovable/Google AI Studio builder) did not support Express, Node routes, or Next.js API routes. We had to redesign the backend to run completely client-side using Gemini SDK.
• Limited time to build reliable data visualizations
The dashboard needed dynamic charts, but the AI output structure had to perfectly match what Recharts expected.
• LocalStorage integration for cross-page state
Without a backend, we needed a clean way to persist AI results from one page to the dashboard. We solved this by storing analysis in localStorage and loading it on the Dashboard.
• Model hallucination on HS Code predictions
Early prompts returned unrealistic HS Codes or incomplete descriptions. We solved this by constraining the schema and providing context-based instruction.
• Designing a workflow usable in real export scenarios
We had to research what causes real shipment rejections (FDA refusals, EU RAPEX alerts, labeling rules, chemical limits) to ensure our tool solves genuine pain points.
• Time pressure
Building a multi-page AI-driven export intelligence platform with charts, scoring, and compliance checks in less than 24 hours required tight coordination across 4 team members.
Technologies used