From raw signals to
rich representations and real decisions.
ARGenesis uses Deep Learning including convolutional networks, sequence models and transformers to extract meaning from high‑dimensional data: text, images, time series and complex event streams, all within governed, production‑ready architectures.<
Vision · NLP · Time‑series · Multimodal
Industrialised for regulated industries
Transformer pipeline snapshot
1. Ingest
Documents, images, event streams
Latency < 2s
99%
2. Embed
Domain‑tuned representations
Latency < 3s
98%
3. Reason
Transformer‑based decision layer
Latency < 4s
97%
4. Act
Downstream API / workflow trigger
Latency < 5s
96%
Example business output
Automated extraction and interpretation of unstructured content (reports, notes, emails) reduces manual review time by 60–80% while improving consistency and traceability.
What is Deep Learning & Transformation?
Neural networks that learn powerful representations from complex data.
Deep Learning uses layered neural networks to learn structure directly from data images, language, audio, time‑series and more. Transformers extend this by modelling context and relationships at scale, making them ideal for document understanding, sequence modelling and multimodal intelligence.
Where we help
- Document and email understanding.
- Claims and medical report extraction.
- Image, scan and signal interpretation.
- Complex event and sequence modelling.
How we work
- Problem framing and feasibility checks.
- Architecture selection (CNN, RNN, transformer, hybrid).
- Domain‑specific fine‑tuning and evaluation.
- Deployment with latency, cost and governance constraints.
ARGenesis capabilities
Deep learning architectures tuned for real operational environments.
We combine applied research with production engineering, ensuring deep learning models are not just accurate in notebooks but robust, governable and maintainable in enterprise settings.
Computer vision
• Convolutional neural networks (CNNs).
• Image, document and scan analysis.
• Quality, damage and anomaly detection.
Sequence & time‑series models
• RNNs, LSTMs and temporal models.
• Event stream and sensor analytics.
• Trajectory and trend forecasting.
Transformers & attention
• Domain‑tuned transformer models.
• Long‑document and context modelling.
• Multimodal fusion (text + tables + images).
Representation learning
• Embeddings for customers, policies and assets.
• Similarity search and clustering.
• Feature extraction for downstream ML.
Evaluation & robustness
• Stress‑testing and sensitivity analysis.
• Fairness, bias and stability checks.
• Human‑in‑the‑loop review where needed.
Deployment & optimisation
• Latency and cost‑aware serving.
• Model compression and distillation.
• Monitoring, drift and lifecycle management.
Cross‑industry use cases
Extracting meaning from unstructured and high‑dimensional data.
Insurance
• Claims document and image understanding.
• Automated injury and damage coding.
• Unstructured reserving and pricing insights.
• Voice and text analytics for complaints.
Healthcare
• Clinical note and report interpretation.
• Scan, ECG and signal analysis.
• Patient risk and pathway support.
Retail & digital
• Review and feedback analytics.
• Search and recommendation enrichment.
• Multimodal product understanding.
Education & public sector
• Free‑text survey and feedback analysis.
• Document processing and summarisation.
• Case and evidence triage support.
Part of the ARGenesis technology framework
Deep learning is the bridge between raw inputs and intelligent action.
We position Deep Learning alongside Classic AI rules, Machine Learning, GenAI, Agentic and Gentic AI, underpinned by strong data engineering forming one coherent AI decision stack.
1. Classic AI & Rule Engines
3. Deep Learning & Transformation
5. Gentic AI (Evolutionary)
7. Applied AI (Industry)
Next steps
Considering Deep Learning or transformers for a project?
We can help you assess where Deep Learning truly adds value, what data you need and how to build solutions that are efficient, explainable and maintainable.