Trusted by Europe's leading organizations
Processing unstructured enterprise data with high-accuracy machine learning models. DNA Solutions builds document classification and extraction pipelines using OCR, word2vec, tf-idf and SVM, tuned to the precision required for audited enterprise workflows.
Trusted by Europe's leading organizations
Enterprise documents arrive as invoices, contracts and scanned records without consistent structure. DNA Solutions builds pipelines that read, classify and extract their data at volume, at the accuracy audited processes require. On the Canon engagement, our custom SVM classifier reached 94.7% accuracy and outperformed Azure AI on 2 of 3 datasets.
DNA Solutions designs technology that lands on your bottom line. European enterprises trust us with extreme data volumes and critical financial pipelines.
See client resultsDNA Solutions built and maintains a Deloitte-audited billing platform processing €300M in audited transactions every month.
By optimizing software licensing fees for a major European organization, DNA Solutions delivered over €1M in yearly cost savings.
A senior team of engineers and consultants across Europe.
T-Systems, Satellic, European Commission: our longest engagements last because we deliver.
At volume, the hard part is accuracy that survives an audit. Our pipeline is built and measured against that bar.
The pipeline parses invoices and structured documents at volume, extracting line items, totals and reference fields automatically into your downstream systems. We build it on OCR with Tesseract and tune feature extraction to the recurring formats your operations generate most. Extraction quality stays consistent as volume grows, and every extracted field traces back to the source document, so the throughput your finance and operations teams rely on holds without manual re-keying.
We train classification models per document type instead of stretching a single generic model across every case. On the Canon engagement, our custom SVM classifier outperformed Azure AI on 2 of 3 datasets, reaching 94.7% accuracy against an 84.2% baseline, using word2vec and tf-idf features. Each model is sized to the document classes and the precision threshold the workflow demands, which keeps the classification explainable and the accuracy holding on the formats that matter to your operations.
The pipeline extracts structured metadata from unstructured documents: dates, parties, amounts and document type. That output feeds search, indexing and audit trails at the precision audited workflows require. Every extracted field stays traceable back to its source document rather than re-keyed by hand, so downstream teams query reliable data and auditors can follow each value back to the page it came from, without reconstructing the trail after the fact.
For the document classes where an error carries cost, the pipeline routes low-confidence predictions to a human reviewer before the result moves downstream. We set the confidence threshold per document type, so the bulk of clean documents flow through automatically while edge cases land in a review queue. Each correction feeds back into the training data, so the model improves on the formats your operations actually see, and the accuracy holds when the same documents come up under audit.
DNA Solutions engineers document classification and extraction pipelines for high-volume enterprises. From invoice parsing to per-type classification and structured metadata extraction, each model is tuned to the precision the workflow requires.
What we buildParsing invoices and structured documents at volume, extracting line items, totals and reference fields into downstream systems. Built on OCR with Tesseract and feature extraction tuned to your recurring formats.
Classification models trained per document type rather than a single generic model. On the Canon engagement, a custom SVM classifier outperformed Azure AI on 2 of 3 datasets (94.7% versus 84.2%), using word2vec and tf-idf features.
Extracting structured metadata from unstructured documents: dates, parties, amounts and document type. Output feeds search, indexing and audit trails with the precision audited workflows require.
Document flows look nothing alike across telecom, retail and infrastructure. One classification core, tuned to each sector's classes and precision.
Classifying and extracting data from billing records, contracts and operational documents across telecom and media workflows, at the volumes operators generate.
Parsing invoices and supplier documents at volume for retail and distribution operations, where margins leave no room for manual entry errors.
Extracting structured data from toll and infrastructure documentation, feeding the operational and audit records these systems rely on.
How we classify and extract data from unstructured enterprise documents at the precision audited workflows require.
A private document classification engine that beat Azure AI Document Intelligence on two of three benchmark datasets, 94.7% versus 84.2%.
94.7% vs 84.2% on Azure AI
A single payment gateway unifying transactions across a multi-product platform, with reconciliation built in.
One gateway, every productSenior decision-makers on the data, classification and financial platforms DNA Solutions has delivered.
"We collaborated on an innovative recruiting app, and what stood out most was the supportive atmosphere and the strong autonomy given to every team member."
"DNA works with us to deliver digital systems at scale so that we can serve our customers digitally. They are both reactive to requests and proactive with ideas and proposals."
"DNA Solutions has delivered online tools that have made the client's employees and customers' lives easier. For instance, the client can now handle cases in a maximum of two days instead of five."
What clients ask about accuracy, volume and audit on document pipelines.
DNA Solutions builds pipelines for invoices, contracts and scanned records, and for the mixed document flows enterprise operations generate day to day. We train 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 instead of spreading one generic model thin across every case. When a new document type appears, we add a class and retrain rather than rebuilding the 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, and we tune each model to the precision the workflow requires rather than quoting one headline number that would not hold across every set. Before any wider rollout, we measure accuracy on a sample of your own documents, so you see the real figure for your 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. DNA Solutions selects established components that fit the document set rather than applying one generic model across every case, which keeps the pipeline explainable: we can trace why a given document was classified the way it was, instead of handing you a result no one can account for. 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 licence locking you in, and every stage feeds search, indexing and audit trails. When the document mix shifts, we retrain or adjust a stage rather than replacing the whole 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. DNA Solutions tunes 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 real 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.