Presear builds self-supervised and contrastive learning systems — foundation models, BYOL, DINO, masked autoencoders — that extract powerful representations from unlabelled data at scale.
Technical Depth
From contrastive pretraining to foundation model fine-tuning — we use the right self-supervised method for your data regime and domain.
Training encoders to produce similar representations for augmented views of the same sample and dissimilar representations for different samples — without any labels. We implement SimCLR, MoCo, and SupCon frameworks to build transferable visual and multimodal representations across image, audio, and tabular domains.
Learning without negative pairs using teacher-student architectures where the teacher is an exponential moving average of the student. BYOL and DINO produce remarkably rich features — DINO-pretrained ViTs learn semantic segmentation without segmentation labels, a property we exploit in medical and satellite imaging tasks.
Pretraining models to reconstruct masked portions of input — pixels in vision (MAE) or tokens in text (BERT) — forcing the model to build a deep generative understanding of structure. We apply masked autoencoding to images, time series, and multimodal documents to build universal feature extractors with minimal supervision.
Learning joint representations across modalities — images and text (CLIP-style), audio and video, or sensor and image data — by aligning representations of matching pairs in a shared embedding space. We build contrastive multi-modal systems that enable zero-shot transfer across new data types and retrieval across modalities.
Evaluating and adapting SSL representations through linear probing (frozen backbone + linear head) and full fine-tuning protocols. We benchmark representation quality before committing to downstream adaptation, ensuring the pretraining cost is justified by measurable linear separability gains on your target task.
Adapting pretrained vision-language foundation models (CLIP, DINOv2, SAM) to specialized domains through parameter-efficient fine-tuning with minimal labelled examples. We use adapter layers, prompt tuning, and LoRA to specialize foundation models for industrial, medical, and satellite imagery without full retraining costs.
Our Process
A five-stage process that turns raw, unlabelled data into powerful representations ready for downstream tasks. Click any step to explore.
SSL's power scales with unlabelled data volume. We design data collection strategies that maximize domain coverage without labelling cost — pulling from internal archives, public datasets, and synthetic augmentation pipelines. Data quality checks remove near-duplicates, corrupted samples, and out-of-distribution outliers before pretraining.
The choice of SSL objective, backbone architecture, and augmentation strategy determines what properties the representation learns — invariances, equivariances, semantic structure. We select and configure the right combination based on your data modality, compute budget, and downstream task requirements before committing to expensive GPU runs.
We run large-scale pretraining on multi-GPU clusters with distributed training, mixed-precision optimization, and gradient checkpointing to maximize throughput. Training is monitored through SSL-specific metrics — alignment, uniformity, collapse detection — to catch representation collapse early and adapt training dynamics accordingly.
The pretrained encoder is adapted to your labelled downstream task using only a fraction of the labels required by supervised approaches from scratch. We systematically compare linear probing, partial fine-tuning, and full fine-tuning protocols to identify the optimal trade-off between label efficiency and task accuracy for your budget.
Final models are evaluated on held-out benchmarks, stress-tested on distribution shifts and domain-edge samples, and then packaged for production deployment. We document representation properties, provide interpretability visualizations, and build inference pipelines with TorchScript or ONNX export for scalable serving.
Real-World Impact
Label-efficient AI deployments across domains where annotations are scarce, expensive, or impossible to scale.
Core Challenge
Medical imaging datasets require expert radiologist annotation, making large labelled sets prohibitively expensive. Models trained on small labelled sets overfit and fail to generalize across scanner types, patient populations, and pathology variations encountered in real clinical deployment.
Who Benefits
Hospitals, radiology AI startups, and medical device companies that have large archives of unannotated scans and a small set of expert-annotated cases — needing models that generalize across institutions and imaging protocols without collecting millions of labels.
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Manufacturing defects are rare by design — which means labelled defect datasets are tiny, and supervised models trained on them are brittle. Simultaneously, there are millions of images of normal product that can be leveraged to learn what "normal" looks like without any labels.
Who Benefits
Semiconductor fabs, electronics manufacturers, automotive parts suppliers, and FMCG producers that run high-speed production lines with vision cameras and need defect detection systems that generalize to new product variants without restarting labelling from scratch.
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Legal and compliance teams process millions of documents — contracts, filings, correspondences — but manually labelling document types, clauses, and entities for every new document category is unsustainable. Generic NLP models miss domain-specific legal language and structure.
Who Benefits
Law firms, compliance departments, banks, and regulatory bodies that hold large archives of unstructured legal and financial documents needing automated classification, extraction, and similarity search — without building exhaustive labelled training sets for each category.
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Video content platforms hold enormous archives of footage that need automated tagging, search, and content moderation — but annotating video at frame or segment level is orders of magnitude more expensive than annotating images. Per-frame supervised approaches miss temporal context entirely.
Who Benefits
Streaming platforms, broadcast archives, sports analytics companies, and surveillance operators that need scalable video understanding for retrieval, moderation, highlight detection, and action recognition — without per-clip annotation budgets.
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Purpose-built SSL libraries, distributed training infrastructure, and evaluation frameworks chosen for efficiency and reproducibility.
Frequently Asked
Answers to the questions data science leads and ML engineers ask before starting an SSL engagement with Presear Softwares.
Ask Our SSL TeamPartner with Presear Softwares to extract powerful representations from your raw data archives — without the cost and bottleneck of large-scale labelling.