Custom Model Development

Off-the-shelf models rarely fit real business problems. Alchemilla Ventures designs, trains, and deploys custom machine learning and deep learning models tailored to your data, your constraints, and your production environment — from first experiment to monitored, live inference. We work across the major frameworks — PyTorch, TensorFlow, JAX/Flax, Keras 3, and the HuggingFace ecosystem — choosing the right tool for each workload rather than forcing a one-size-fits-all stack.

Why a Custom Model?

Pre-trained APIs and generic models break down when your data, latency budget, or accuracy requirements are unique. A purpose-built model — trained on your proprietary data and optimised for your deployment target — delivers higher accuracy, lower inference cost, and full ownership of the IP. We bridge the gap between experimental notebooks and production-grade systems, so models don’t just work in a demo, they survive contact with real traffic.

What We Build

• Custom Deep Learning Models: Transformer-based architectures, CNNs, RNNs, and GANs designed for your specific data and problem domain — be it healthcare, fintech, or manufacturing.
• Transfer Learning Pipelines: Fine-tune pre-trained models (ResNet, BERT, EfficientNet, and HuggingFace model zoos) on your proprietary datasets to cut training time and cost.
• Custom LLM Fine-Tuning & Training: Adapt open-weight large language models (LLaMA, Mistral, Gemma, Qwen) to your domain with full fine-tuning, parameter-efficient methods (LoRA/QLoRA, PEFT), instruction tuning, and RLHF/DPO — plus pre-training of smaller domain-specific models from scratch where it pays off.
• Model Optimization & Quantization: Reduce inference latency and model size with quantization, pruning, and distillation — ideal for edge and mobile deployments in bandwidth-constrained markets.
• Real-Time Inference APIs: High-throughput REST or gRPC endpoints using TorchServe, TensorFlow Serving, or NVIDIA Triton, ready to integrate into your existing systems.
• On-Device & Edge Deployment: Compress and convert models with TensorFlow Lite, TorchScript, and ONNX for deployment on IoT, embedded, and mobile hardware — no network required.
• MLOps & Model Monitoring: Automated retraining pipelines, drift detection, and performance dashboards so your models stay accurate as your data evolves.
• Custom CUDA Kernels: When off-the-shelf layers don’t suffice, we write custom CUDA extensions in C++/CUDA for maximum GPU utilisation.

Frameworks We Work With

• PyTorch — Dynamic computation graphs and an intuitive Pythonic API make it ideal for research, rapid prototyping, and cutting-edge architectures. Paired with PyTorch Lightning for structured training and TorchServe for serving.
• TensorFlow — A mature, end-to-end platform with Keras for fast development, TensorFlow Serving for production, and TensorFlow Lite for on-device inference — a reliable choice for mission-critical workloads.
• JAX / Flax — Composable, high-performance numerical computing with XLA compilation, ideal for large-scale training and research that pushes hardware to its limits.
• Keras 3 — A multi-backend, high-level API for rapid prototyping that runs seamlessly on PyTorch, TensorFlow, or JAX.
• HuggingFace (Transformers, PEFT, TRL) — The de facto ecosystem for LLMs and modern transformer models, from fine-tuning to deployment.
• Classical ML (scikit-learn, XGBoost, LightGBM) — When tabular data and interpretability matter more than deep learning, we reach for the right traditional tool.

We are fluent across these and recommend the framework that best fits your accuracy, latency, and infrastructure requirements rather than forcing a one-size-fits-all stack.

Industries We Serve

• Healthcare & Life Sciences — Medical imaging diagnostics for hospitals
• Finance & Banking — Fraud detection and risk modelling for fintech
• Manufacturing — Predictive maintenance for the automotive sector
• Agriculture — Crop yield prediction and drone-based monitoring
• E-commerce — Personalised recommendation engines for online retail

Our Development Process

1. Problem Framing: Collaborative workshops to define the ML problem, KPIs, and data strategy.
2. Data Preparation & EDA: Cleaning, augmentation, and feature engineering pipelines, with deep dives to surface patterns and biases.
3. Model Development: Iterative training and experimentation, tracked with TensorBoard and Weights & Biases for full reproducibility.
4. Validation & Testing: Rigorous cross-validation, A/B testing, fairness checks, and adversarial robustness assessments.
5. Deployment & Handover: Containerised serving on AWS, Azure, GCP, or on-premise infrastructure, with documentation and knowledge transfer to your team.

Technology Stack

• PyTorch & PyTorch Lightning — Research-friendly deep learning framework
• TensorFlow, Keras 3 & TFX — End-to-end ML platform and pipelines
• JAX / Flax — High-performance, XLA-compiled training at scale
• HuggingFace Transformers, PEFT & TRL — Pre-trained models and LLM fine-tuning (LoRA/QLoRA, RLHF/DPO)
• scikit-learn, XGBoost & LightGBM — Classical ML for tabular problems
• DeepSpeed & Accelerate — Distributed and memory-efficient large-model training
• ONNX, TorchScript & TensorRT — Model export and optimization
• TorchServe, TensorFlow Serving, vLLM & NVIDIA Triton — Enterprise-grade model serving
• TensorFlow Lite & ONNX Runtime — Edge and on-device inference
• Docker & Kubernetes — Containerised serving at scale
• Weights & Biases, Prometheus & Grafana — Experiment tracking and model monitoring

Whether you are a startup validating an idea or an established enterprise scaling AI across the organisation, our model development expertise helps you unlock the full potential of deep learning. Contact us to discuss your project.