Case Studies / InSaaS.ai
PROFESSIONAL SERVICES · DATA PRIVACY & GDPR
Automating GDPR-compliant data anonymization for market research at scale
InSaaS.ai processes massive volumes of textual data from social media, online forums, and customer sources for market research and product planning. Every dataset potentially contains personally identifiable information that must be removed before analysis – manually reviewing it all was impossible at their scale. We integrated ShareMedix as an anonymization API that automatically detects and masks PII across their entire data pipeline.
Client: InSaaS.ai – a data analytics company specializing in marketing, market research, and product development insights.
KEY RESULTS
GDPR
Full compliance with European data privacy regulations
API
Integrated directly into existing data pipelines
Parallel
Handles multiple concurrent anonymization requests at scale
Auto
Names, addresses, phone numbers, IBANs detected and masked
INDUSTRY
Professional Services / Market Research
USE CASE
PII anonymization in text data
AI APPROACH
NLP-based PII detection
DATA SOURCES
Social media, forums, customer data
The challenge
InSaaS.ai builds data analytics solutions for marketing, market research, and product development. Their work depends on analyzing large volumes of textual data – social media posts, online forum discussions, customer feedback, and internal datasets. The insights hidden in this data are valuable, but the data itself is full of personally identifiable information: names, addresses, phone numbers, IBAN numbers, and other sensitive details.
Under GDPR, this data cannot be processed or analyzed in its raw form. Every piece of PII must be detected and removed before the data enters InSaaS.ai’s analytics pipeline. Doing this manually was not an option – the volume of data was far too large, the variety of PII types too broad, and the regulatory stakes too high for a manual process.
The core problem: InSaaS.ai needed to make vast amounts of textual data GDPR-compliant before it could be analyzed – but the volume and variety of personally identifiable information made manual anonymization impossible at their scale.
What we built
We integrated ShareMedix – our NLP-powered data anonymization engine – directly into InSaaS.ai’s data processing pipeline via API. The system automatically detects and masks PII in textual data before it reaches the analytics layer.
Automatic PII detection across data types. ShareMedix identifies names, addresses, phone numbers, IBAN numbers, email addresses, and other personally identifiable information using advanced NLP techniques. The system handles data from diverse sources – social media posts written in informal language, structured customer records, and everything in between.
API-first integration. Rather than a standalone tool requiring manual operation, ShareMedix was deployed as an API that InSaaS.ai calls programmatically within their existing data pipelines. Data flows in, anonymized data flows out – no manual steps, no separate interface.
Parallel processing at scale. Market research datasets can be massive. The anonymization API handles multiple concurrent requests, processing large volumes of text without becoming a bottleneck in InSaaS.ai’s data preparation workflow.
Customizable anonymization rules. Different data sources and use cases require different handling. ShareMedix supports white lists, black lists, and configurable anonymization rules — allowing InSaaS.ai to fine-tune what gets masked and what gets preserved, adapting as regulations and data sources evolve.
The results
BEFORE
PII scattered throughout massive text datasets. Manual review impossible at scale. GDPR compliance difficult to guarantee. Data utilization limited by privacy risk.
AFTER
Automated PII detection and masking via API. GDPR compliance built into the data pipeline. Full datasets available for analysis without privacy risk. No manual intervention required.
InSaaS.ai can now process and analyze their entire data volume with confidence that GDPR compliance is maintained automatically. The anonymization step, previously a manual blocker that limited what data could be used, became a seamless, invisible part of the pipeline.
This also unlocked data that was previously too risky to use. Customer data and other sensitive sources that would have required extensive manual review can now be anonymized automatically and fed into analytics workflows, expanding the scope of insights InSaaS.ai can deliver to their clients.
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