Or write to hello@algorithmic.network
Our philosophy
We start with local execution and private environments, and integrate hosted models when they serve the work better. Either way you get software that is measured on your real tasks, easy to operate and clear about its limits.
What we build
From focused assistants to custom model development, fine-tuning and complete products, built around your people, data and deployment needs.
Focused applications that run intelligence on your devices. We evaluate the models, hardware and workflow around the task you want to improve.
Search and assistance over your own documents and business information, with source permissions, references and deliberate data boundaries.
Clear interfaces and integrations that make AI a useful part of everyday work, with people in control of important actions.
When local is not enough, we integrate hosted models with minimal data sharing, clear provider terms and the same review steps.
Methodology
Four stages, each ending with something you can see and test before the next begins.
Define the use case, information requirements and outcome before choosing a model.
Design the workflow, data boundaries and interface around the people using the system.
Develop or fine-tune models where the task calls for it, and evaluate the product on separate test examples, including cases where it should fail safely.
Prepare the deployment, documentation and ongoing review of quality and operating costs.
Technology
We choose AI tools for the task, from custom model training and fine-tuning to orchestration and local inference, with a practical stack for the application around them.
Hugging Face
TypeSafe AI
Ollama
PyTorch
fastai
TensorFlow
Keras
scikit-learn
Weights & Biases
ONNX
MLflow
Anthropic
Questions
Common questions about how we plan, build and support AI products.
Ask us directlyWe define the use case and data flow, then assess local execution or a private environment. The choice depends on the task, the hardware, access requirements and the quality the system needs to achieve.
Yes. We develop custom models and fine-tune existing ones around an agreed task and dataset. The work includes data preparation, training or adaptation, evaluation on separate test examples and deployment planning. We compare results with a baseline so you can judge the benefit.
Yes. When a hosted model is the better fit for quality, scale or cost, we integrate it carefully: we limit what is shared, mask sensitive details where possible and choose providers with clear data terms.
Yes. We examine the existing workflow, available interfaces and access permissions, and aim for a focused integration with clear ownership and useful behaviour.
After discovery we agree on scope, deliverables and evaluation criteria. A focused prototype can test the important assumptions before a larger development commitment.
Documentation, handover and support are agreed as part of the engagement. The operating plan covers maintenance, model behaviour, access controls and costs.
Tell us about the workflow you want to improve and the information that needs to stay private. We can begin with a focused conversation.
