From custom models and document intelligence to computer vision and edge inference, OSYSTIC builds AI around your data, constraints, integration points, and ownership requirements.
Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.
We design and train deep learning models from scratch or fine-tune pre-trained architectures on your proprietary data — structured tables, images, audio, or text. Every model is purpose-built for your domain.
Text classification, sentiment analysis, named entity recognition, document summarization, and conversational AI built on transformer architectures like BERT, GPT, and LLaMA — tailored to your industry vocabulary and data.
Object detection, segmentation, face recognition, OCR, and visual quality inspection for real-time production environments across web, mobile, and edge devices.
Regression, classification, and time-series forecasting models that detect anomalies early and make data-informed decisions — integrated into your existing dashboards.
Agent-based machine learning systems that learn optimal strategies through environment interaction — applied in recommendation engines, robotics control, dynamic pricing, and supply chain optimization.
Model quantization, pruning, and distillation for lightweight on-device inference — enabling offline-capable, low-latency AI without cloud dependency or data egress.
Ingest raw data from any source — databases, images, PDFs, audio, or live API streams. Quality assessment and schema validation included.
Data Sources Validated DatasetClean, deduplicate, normalise, and label. Annotation pipelines for images; tokenisation and embedding preparation for text.
Raw Dataset Feature StoreSelect and configure architectures, run tracked experiments, iterate on hyperparameters until performance criteria are met.
Feature Store Best CheckpointTest against held-out data with domain-specific metrics. Bias audits and explainability reports for regulated industries.
Held-out Set Approved ModelPackage as REST API, gRPC service, or embedded SDK and deliver to cloud, on-premise, or edge device.
Model Bundle Live EndpointLive dashboards track accuracy and data drift. Automated retraining pipelines keep performance high as the world changes.
Prod Traffic Stable ModelTechnology follows environment, operating constraints, team ownership, and long-term maintainability.
TensorFlow · PyTorch · Keras · JAX · Scikit-learn
Hugging Face · LangChain · OpenAI API · LlamaIndex · spaCy
OpenCV · YOLO · Detectron2 · Roboflow · MediaPipe
MLflow · Weights & Biases · Docker · Kubernetes · Ray
AWS SageMaker · Google Vertex AI · Azure ML · GCP BigQuery ML
ONNX · TensorFlow Lite · CoreML · TensorRT · OpenVINO
Medical imaging analysis, clinical NLP, patient risk stratification, drug discovery pipelines
Fraud detection, credit scoring, algorithmic trading signals, document processing automation
Visual defect inspection, predictive maintenance, yield optimisation, demand forecasting
Product recommendation engines, demand forecasting, visual search, returns prediction
Deliverables, source access, model artifacts, third-party licensing, and handover terms are defined in the engagement agreement. Where model weights or other artifacts can be transferred under their applicable licenses, the delivery scope states that explicitly.
Absolutely. We design models to fit into your infrastructure rather than asking you to change it. Whether your backend runs on cloud, on-premise, or a mix, we expose the model through a standard interface that connects cleanly with your existing systems.
Data privacy is a core part of every engagement. We agree on data handling terms before any files are shared, and training can be run entirely within your own environment so your data never needs to leave your control.
Every project starts with a discovery session where we understand your data, goals, and constraints. From there we align on a scope and work in defined phases — each one delivering something tangible you can evaluate before we move forward.
Tell us about the problem, the data available, and the environment the system needs to run in. We will help turn that into a practical technical plan.