Stop Rebuilding Data Pipelines for Every Client
Ertas Data Suite gives AI/ML service providers a reusable, on-premise data pipeline platform — so your team spends less time rebuilding data prep for every client and more time delivering AI solutions. Visual pipeline builder with PII redaction, quality scoring, and compliance logging built in.
The Challenges You Face
Engineers Spend More Time on Data Prep Than AI Development
60-80% of each engagement goes to cleaning, transforming, and validating client data before the real AI/ML work begins. Every new client means rebuilding from scratch.
Regulated Clients Require On-Prem — And You Can't Deliver It
Healthcare, legal, finance, and construction clients need data processing on their infrastructure. Cloud-based tools are legally off-limits, and building custom on-prem pipelines per client is prohibitively expensive.
No Observability Across the Pipeline
When data quality issues cause downstream model failures, there's no shared log to trace what happened. Client asks "what happened to my data?" and the answer requires days of forensic investigation across fragmented scripts.
Every Engagement Reinvents the Wheel
The pipeline you built for the last client can't be reused for the next one. Different scripts, different tools, different formats — no standardization, no templates, no institutional knowledge captured.
How Ertas Solves This
Ertas Data Suite is a reusable pipeline platform that service providers deploy on-prem at client sites. The visual node-graph builder means pipelines are visible, auditable, and transferable between engagements. 18 processing nodes handle 8 input formats — PDF, Word, PowerPoint, Excel/CSV, HTML, images, audio — covering the full spectrum of enterprise documents your clients will throw at you.
PII redaction, quality scoring, and anomaly detection are built into the pipeline as dedicated nodes, not afterthoughts bolted on at the end. Every node execution is logged with timestamp and operator ID, producing exportable audit trails that become part of your client deliverable. The result: your team spends engineering hours on AI development instead of data wrangling, and your clients get compliance documentation included in the engagement.
Key Features for AI/ML Service Providers
Visual Pipeline Builder
Drag-connect 18 processing nodes to build pipelines visually. No scripting, no YAML. Pipelines are readable by non-technical stakeholders and reusable across engagements.
Built-In PII Redaction
Flagship PII Redactor node handles email, phone, SSN, addresses, medical IDs. Runs deterministically on-prem before any downstream processing. Compliance teams can verify redaction in the audit log.
Pipeline Observability and Logging
Every node execution logged with timestamp and operator ID. Quality Scorer and Anomaly Detector nodes catch issues before they propagate. Exportable audit trails for client compliance reporting.
Multi-Format Export
Single pipeline outputs JSONL (OpenAI/Alpaca/ShareGPT), RAG chunks (markdown + YAML/JSON), or CSV. Clients get the format their downstream systems need without rebuilding the pipeline.
On-Prem Client Deployment
Native desktop app installs on client infrastructure. No Docker, no Kubernetes, no DevOps. Air-gapped operation — no internet required at runtime. Meets regulated-industry requirements by architecture.
Why It Works
- AI/ML service providers spend 60-80% of each client engagement on data preparation — time that could be spent on model development and AI solution delivery (Harvard Business Review, Anaconda State of Data Science).
- 80-90% of enterprise data is unstructured — the messy PDFs, emails, and documents that service providers must process before any AI work begins (IDC, Forbes).
- The global data preparation market is projected to reach $16.84 billion by 2031, reflecting the scale of the problem service providers face across every engagement (Allied Market Research).
- 65.7% of organizations with sensitive data prefer on-premise deployment for data processing — exactly the regulated-industry clients that service providers serve (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 standardized data pipeline tooling.
Example Workflow
An AI consultancy receives 700GB of construction PDFs from a client who needs a document classification model. The lead engineer opens Ertas Data Suite on the client's on-prem workstation. They build a pipeline: File Import → PDF Parser → Deduplicator → PII Redactor → Format Normalizer → Quality Scorer → branched output to RAG Chunker + JSONL Exporter.
The pipeline processes the full document archive with logging at every node. The PDF Parser handles mixed layouts — technical drawings, specification tables, multi-column reports. The PII Redactor catches contractor names, phone numbers, and addresses before any downstream processing. The Quality Scorer flags 340 low-confidence extractions for manual review.
Two outputs are exported: chunked markdown for RAG-powered document search and structured JSONL for fine-tuning a domain estimating model. The audit trail report is delivered to the client's compliance team. When the next construction client arrives, the same pipeline template is reused with minor configuration adjustments — no rebuilding from scratch.
Related Resources
Ship AI that runs on your users' devices.
Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.