Keep Your AI Pipeline Robust Against Real-World Data
Ertas Data Suite gives AI-powered product teams robust data pipeline infrastructure — handling messy client uploads, redacting PII, and scoring data quality before it reaches your AI models. On-premise deployment satisfies regulated-industry customers.
The Challenges You Face
Client-Uploaded Data Breaks Your RAG Pipeline
Clients upload malformed PDFs, inconsistent spreadsheets, and documents with unexpected encoding. Each one is a potential pipeline failure that surfaces as an AI product bug.
PII Leaks Into Training Data and Inference Logs
Without systematic redaction, client PII ends up in training datasets, vector stores, and inference logs. One incident erodes customer trust and triggers regulatory exposure.
Engineers Fix Data Pipelines Instead of Building Product
Data ingestion and transformation issues are the #1 source of engineering interrupts. Every hour spent debugging a malformed CSV parser is an hour not spent on AI features.
Regulated Customers Demand On-Prem Processing
Healthcare, legal, and financial clients won't adopt your product unless data processing happens on their infrastructure with audit trails. You can't currently guarantee this.
How Ertas Solves This
Ertas Data Suite serves as the pipeline infrastructure powering your product's data handling layer. Instead of building custom ingestion and transformation code for every document type your customers upload, Data Suite's 18 processing nodes handle the full spectrum — PDF, Word, PowerPoint, Excel/CSV, HTML, images, and audio — with anomaly detection and quality scoring catching issues before data reaches your AI models.
PII redaction is built into the pipeline as a dedicated node, not bolted on as an afterthought. Every document passes through configurable redaction before reaching AI models or vector stores. Planned data streaming capability will enable continuous processing — set up the pipeline to watch a data source and process new uploads automatically. On-prem deployment satisfies regulated-industry customers who require data processing on their infrastructure with full audit trails.
Key Features for AI-Powered Solution Companies
Robust Multi-Format Ingestion
8 input parsers (PDF, Word, PowerPoint, Excel/CSV, HTML, images, audio) handle the reality of client-uploaded documents. Anomaly Detector catches corrupt or malformed files before they break downstream processing.
PII Redaction as Infrastructure
PII Redactor runs as a pipeline node, not an afterthought. Every document passes through redaction before reaching AI models. Redaction decisions are logged for compliance auditing.
Data Quality Gates
Quality Scorer and Anomaly Detector nodes enforce data quality thresholds. Documents that fail quality checks are flagged rather than silently degrading AI model performance.
RAG-Ready Export
RAG Exporter outputs chunked text with metadata frontmatter or structured JSON — ready for vector database ingestion. Combined with upstream quality scoring, ensures RAG retrieval stays reliable.
Deployable On-Prem for Regulated Customers
Ship Ertas Data Suite as part of your on-prem deployment. Native desktop app with no external dependencies. Regulated-industry customers get audit trails and air-gapped operation.
Why It Works
- 80-90% of enterprise data is unstructured — the messy PDFs, emails, and documents that your AI product must handle reliably when customers upload them (IDC, Forbes).
- AI/ML teams spend 60-80% of project time on data preparation rather than model development — time your engineering team could spend on product features (Harvard Business Review).
- The global data preparation market is projected to reach $16.84 billion by 2031, reflecting the universal need for robust data pipeline infrastructure (Allied Market Research).
- 65.7% of organizations with sensitive data prefer on-premise deployment — these are exactly the regulated-industry customers who need your AI product but can't use cloud-only solutions (Flexera State of the Cloud).
- Ertas is backed by Antler, one of the world's most active early-stage venture firms, validating the market need for data pipeline infrastructure.
Example Workflow
An AI SaaS company receives client document uploads — a mix of PDFs, Word docs, and HTML pages — for a RAG-powered knowledge base product. The data pipeline runs on Data Suite: File Import → PDF Parser / Word Parser / HTML Parser (branched by file type) → Anomaly Detector → PII Redactor → Quality Scorer → RAG Chunker → RAG Exporter.
The Anomaly Detector catches 15 corrupt PDFs and 8 files with encoding issues, quarantining them for review instead of letting them silently degrade search results. The PII Redactor strips client employee names, email addresses, and phone numbers from all documents before they enter the vector store. The Quality Scorer flags 47 low-confidence extractions.
Clean, PII-redacted chunks are exported to the vector database. The pipeline runs on the client's on-prem server, satisfying their healthcare compliance requirements. The audit trail proves PII handling to the client's compliance team — a deliverable that previously required weeks of custom tooling.
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