AI that solves real business problems — not demo problems.
We build neural networks for NLP, computer vision, and time-series forecasting. We integrate LLMs into your internal tools and workflows. Everything deployed to production with monitoring, not just proved in a notebook.
What we build
- NLP models for document classification, entity extraction, and sentiment analysis using Hugging Face Transformers
- Computer vision systems for quality control, object detection, and document OCR with PyTorch
- Time-series forecasting with LSTM, Temporal Fusion Transformer, and N-BEATS architectures
- LLM integrations: RAG pipelines with LangChain, OpenAI API, or open-source models via Ollama
- Fine-tuning pre-trained models on your domain-specific data for classification and generation tasks
- Model compression and quantisation (INT8/FP16) for efficient inference in production
- GPU compute management on AWS SageMaker, GCP Vertex AI, or Azure ML with spot instance strategies
- Responsible AI documentation: model cards, bias audits, and explainability reports
How we work
Define the problem and success criteria
We clarify exactly what the model needs to output, how accuracy will be measured, and what the business impact of different error rates is. This prevents building impressive things that do not help.
Assess feasibility with a spike
Before committing to a full build, we run a one-week feasibility spike: can the available data support this model? What is the expected performance ceiling? This saves weeks of wasted effort.
Build and experiment
We train models using PyTorch or TensorFlow, tracking all experiments in MLflow or Weights & Biases. We start with the simplest architecture that could work, then add complexity only when needed.
Optimise for production
We optimise inference latency and memory footprint using ONNX, TensorRT, or quantisation. We package models with FastAPI + Docker for consistent, testable deployments.
Monitor and maintain
We set up prediction monitoring, input distribution tracking, and automated retraining triggers. For LLM applications, we add evaluation pipelines to catch quality degradation as prompts or models change.
Frequently asked questions
Do we need a GPU cluster to use deep learning?+
Not necessarily. Many production deep learning workloads run efficiently on CPU with optimised models (ONNX, quantised INT8). For training, we use cloud GPU instances on-demand — you pay per hour, not for idle capacity.
Should we use an LLM API or host our own model?+
It depends on your latency requirements, data sensitivity, and cost model. OpenAI API is fastest to start. Self-hosted open-source models (Llama, Mistral via Ollama) are better for sensitive data or high-volume applications. We help you make this decision with a cost-benefit analysis.
How do you handle the hallucination problem with LLMs?+
We implement RAG (Retrieval Augmented Generation) architectures to ground LLM outputs in your actual data. We also add structured output validation, citation requirements, and human-in-the-loop checkpoints for high-stakes outputs.