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Transcribing and annotating Dutch-language audio for AI speech recognition training at Deepgram, one of the leading speech AI companies. Work involves listening carefully, producing accurate transcripts, and applying consistent annotation according to strict quality guidelines.
This is specialist work — not bulk transcription, but quality-controlled annotation used directly to train production speech models. Accuracy and consistency matter more than speed.
Native Dutch speaker with genuine familiarity with regional speech patterns, accents, and edge cases that a non-native annotator would likely miss or flag incorrectly.
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Validating and correcting PII annotations in synthetically generated Dutch-language conversations, covering both nl-NL and nl-BE (Flemish) locales.
The work goes beyond simple labelling. Each conversation is reviewed for missing or incorrect entity spans, offset accuracy, and whether every instance of a PII entity is consistently tagged across turns. On top of that, the Dutch language quality is assessed — grammar, naturalness, locale-appropriateness, and conversation coherence — with corrections and explanations provided where needed.
PII entity types covered include names, addresses, phone numbers, email addresses, IBANs, ID numbers, dates, usernames, and passwords, each validated against the correct Dutch or Belgian format.
Quality targets: inter-annotator agreement above 95%, full locale validity, and self-QC accuracy of 98% or higher.
Native Dutch speaker covering both standard Dutch and Flemish — not two separate skill sets, but one annotator who knows the difference.
🚀 My First AI Automation Project with n8n
I recently built my first automation project — an AI-Powered Invoice Extraction & Tracking System.
The problem I wanted to solve was simple:
Imagine a business receiving dozens of invoices or cash memos every week. Someone has to open each file, read the information, and manually enter the vendor name, date, items, total amount, and category into a spreadsheet.
That’s repetitive, time-consuming, and easy to get wrong.
So I built a workflow where:
📂 An invoice is uploaded to Google Drive
→ 🤖 Gemini AI reads and extracts the invoice data
→ ⚙️ n8n processes and validates the information
→ 📊 The data is automatically added to Google Sheets
The best part?
No manual data entry. Just upload the invoice and the rest happens automatically.
While building it, I also worked with things like workflow triggers, loop control, API errors/retries, and handling multiple files.
For me, this project was less about simply “building an n8n workflow” and more about learning how AI + automation + data workflows can solve real business problems.
This is just my first step into AI automation, and I’m excited to build more practical systems like this.
If your business has a repetitive process that could be automated with AI, n8n, or data workflows, feel free to DM me. I’d be happy to discuss it.
#AIAutomation #n8n #Automation #ArtificialIntelligence #DataAnalytics #DataAnalyst #WorkflowAutomation #GenerativeAI #BusinessAutomation #AI
Smart pipeline! Have you added strict JSON schema validation after Gemini extracts the invoice fields? Found that it stops edge case hallucinations before the data ever hits Sheets.