Sovereign AI with European data control

DNA Solutions builds pipelines for invoices, contracts and scanned records, and for the mixed document flows enterprise operations generate day to day. Our team trains a classification model per document type, then extract the structured fields each type carries: dates, parties, amounts, totals and reference numbers, into your downstream systems. The pipeline is sized to the document classes your workflow processes most, so accuracy holds on the formats that matter. When a new document type appears, we add a class and retrain on the existing pipeline. Every extracted field traces back to its source document, which is what lets the output stand up under audit.

On the Canon document classification engagement, our custom SVM classifier reached 94.7% accuracy and outperformed Azure AI on 2 of 3 datasets, against an 84.2% baseline. That figure reflects one document set under one configuration, so we treat it as a reference point. Accuracy depends on the document classes you process, the quality of the scans and the training data available. We tune each model to the precision the workflow requires. Before any wider rollout, we measure accuracy on a sample of your own documents, so the figure you see matches your own formats. Where a class matters enough that errors carry cost, we route low-confidence predictions to human review and feed the corrections back into training.

The pipeline combines OCR with Tesseract for text recognition, word2vec and tf-idf for feature extraction, and an SVM classifier tuned per document type. We select established components that fit the document set, which keeps the pipeline explainable: we can trace why a given document was classified the way it was. That matters when an auditor or a domain expert questions a decision. We run the stack on your own cloud account or on-premise environment, with no proprietary license locking you in, and every stage feeds search, indexing and audit trails. When the document mix shifts, we retrain or adjust the affected stage on the existing pipeline.

Yes. The parsing pipeline is built to process invoices and structured documents at volume, extracting line items, totals and reference fields automatically into downstream systems. We tune the feature extraction to the recurring formats your operations generate, so throughput stays consistent as volume grows and the extracted fields remain traceable back to the source document for audit. We size the pipeline to your production volumes and validate it on a sample of your own invoices before any wider rollout, so the throughput you see in production matches what we measured. Where a value carries cost, low-confidence extractions route to human review before they move downstream, and those corrections feed back into the model. The pipeline absorbs your invoice volume without manual re-keying, while keeping the audit trail intact.