AI and machine learning technologies for practical business use
AI is useful when it solves a specific problem reliably, not when it is added for its own sake. We work with hosted language models, retrieval frameworks and classic machine learning libraries, and we choose the simplest approach that meets the requirement.
AI & Machine Learning we work with
OpenAI
Hosted GPT models for text, vision, speech and embeddings via API.
Learn moreAnthropic Claude
Claude language models for reasoning, writing, coding and long documents.
Learn moreGoogle Gemini
Multimodal Google models for text, images, audio, video and code.
Learn moreLangChain
Framework for composing language-model apps, agents and tool calling.
Learn moreLlamaIndex
Data framework for connecting language models to your documents.
Learn morePyTorch
Flexible deep learning framework for training and deploying neural networks.
Learn moreTensorFlow
Machine learning platform for production models, including mobile and web.
Learn moreHugging Face
Hub and libraries for open models, datasets and fine-tuning.
Learn morescikit-learn
Python library for classic machine learning on structured data.
Learn moreThere are two broad families of tools here. Large language models from providers such as OpenAI, Anthropic and Google handle text, documents, conversation and reasoning, and frameworks like LangChain and LlamaIndex connect them to your own data through retrieval and tool calling. Classic machine learning, using scikit-learn, PyTorch or TensorFlow, is better for structured prediction tasks such as forecasting, scoring, classification and anomaly detection, where you have historical data and need measurable accuracy.
We start by defining what a good answer looks like and how it will be measured, then test more than one model against real examples before committing. Hosted models are fastest to deliver; open models from Hugging Face make sense when data must stay on your own infrastructure or running costs need to be predictable. Often a plain rules engine or a small statistical model is the better answer, and we will say so.
AI & Machine Learning questions
Will our data be used to train the AI provider's models?
The major providers offer business API terms under which customer data is not used for training by default. We review the terms for your chosen provider and can use self-hosted open models where stricter control is required.
How do you stop AI features giving wrong answers?
We ground responses in your own data through retrieval, constrain outputs with structured formats, add validation and human review where the stakes are high, and test against a set of real examples before and after launch.
Do we need machine learning or just a language model?
Language models suit unstructured text and conversation; machine learning suits numeric prediction from historical data. Many projects need neither, and a clear set of rules does the job.
Which language model provider do you use?
We work with OpenAI, Anthropic Claude and Google Gemini, and choose based on the task, cost and where your data needs to be processed. We usually keep the provider behind a thin layer in the code so it can be switched later.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) finds the relevant passages from your own documents and gives them to the model with the question, so answers are based on your content and can cite their sources. Frameworks such as LangChain and LlamaIndex help build this.
Can we use open-weight models on our own infrastructure?
Yes. Models available through Hugging Face can run on your own cloud or servers, which keeps data in your environment. They need more infrastructure work and are sometimes less capable than hosted models, so we compare both for your use case.
How do you measure whether an AI feature works?
We build a test set of real examples with expected results and score the feature against it before launch and after every change to prompts or models. This turns accuracy into something you can see and track.
How do you keep AI running costs under control?
We pick the smallest model that does the job well, cache repeated requests, limit input size and set usage alerts. Costs are estimated during scoping so there are no surprises when usage grows.
How do you handle personal data in AI features?
We send the model only the data it needs, remove or mask personal details where possible and use provider settings and agreements that suit your obligations. Where laws such as the GDPR or the EU AI Act apply, we build with them in mind and work with your advisers.
When do you use PyTorch or TensorFlow instead of an API?
When the problem is a specific prediction or classification on your own data, such as forecasting demand or recognising defects in images, a trained model is often cheaper and more reliable than a general language model.
Other categories
Frontend
Fast, accessible interfaces for websites, dashboards and web applications.
Backend
Reliable server-side code, APIs and business logic that scale with you.
Mobile
Native and cross-platform apps for iOS and Android.
Databases
The right data store for each job, from relational to vector search.
Cloud & DevOps
Hosting, automation and deployment pipelines that keep releases routine.
Platforms & CMS
eCommerce, content management, CRM, ERP and payments, integrated properly.
Have a project in mind? Let’s talk.
Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.