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General models are impressive. They are also wrong in domain-specific contexts. We fine-tune and build custom CV and NLP systems trained on your data.
We scope around the system you actually need to operate, maintain, and operate — not a fixed vendor product or a one-size-fits-all implementation.
Real-time detection, localisation, and segmentation models trained on your annotated image and video data — optimised for your inference environment.
Layout-aware extraction from invoices, contracts, forms, and reports — handling multi-column, tabular, and handwritten content accurately.
Embedding-based retrieval that returns conceptually relevant results rather than keyword matches — across documents, products, and content.
Domain-specific classification and named entity recognition fine-tuned on your labelled data — including multi-label and hierarchical tasks.
Systems combining visual and textual inputs — for product cataloguing, medical report generation, and content moderation at scale.
Each engagement is broken into defined phases with reviewable outputs. Scope can adapt, but accountability stays visible.
Review your existing labelled data, identify gaps and annotation quality issues, and define an annotation strategy if additional data is needed.
Choose the right architecture and pre-trained base — balancing accuracy, latency, and the maintenance burden your team can sustain.
Set up annotation pipelines for your data type — bounding boxes, polygons, NER spans, or document labels — with quality control built in.
Train on your domain data with evaluation against metrics that reflect real production requirements — not just benchmark scores.
Test against held-out domain data. For CV: precision, recall, mAP. For NLP: F1, exact match, BLEU. Bias audit where relevant.
Technology choices follow your environment, operating constraints, team capability, and long-term ownership requirements.
The same technical capability can require very different controls, integrations, and operating models across industries.
Medical image analysis, radiology report NLP, clinical NER, pathology slide classification.
Document extraction from contracts and filings, fraud detection from transaction text, KYC document processing.
Product catalogue enrichment, visual search, review sentiment NER, packaging defect detection.
Contract clause extraction, case law semantic search, obligation NER, redaction pipelines.
The exact architecture and delivery plan depend on your environment. These answers describe how OSYSTIC approaches the work.
For fine-tuning a pre-trained model, a few hundred labelled examples can produce a viable baseline. The amount depends on task complexity and class diversity. We assess your data in discovery before committing to a target.
Yes. We handle PDFs, scanned documents, Excel files, and custom formats. Layout-aware models work with multi-column and tabular structures that simpler extraction tools cannot.
We use metrics appropriate to the task — mAP for detection, F1 for classification and NER, BLEU/ROUGE for generation. Critically, we agree on the eval metric and threshold before training begins, not after.
The engagement defines which model artifacts, training code, evaluation assets, and documentation are deliverables, subject to the licenses and restrictions of any third-party models or components.
Tell us what you need to detect, classify, or extract. We will come back with an honest plan based on your actual data.