AI Consulting & Engineering

Build, deploy and maintain AI systems with confidence.

From machine learning models to generative AI applications, Jumping Rivers helps organisations turn AI ideas into dependable software, combining data science, engineering and validation expertise to keep it reliable in production.

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Our Technology Partners

Our AI Services

AI consulting

Before you invest, we review your use cases, data and governance and give you a prioritised plan for where AI fits.

Building AI systems

Machine learning models and LLM tools, built in R and Python and tested and documented from day one.

Validation and assurance

Every system is tested against agreed acceptance criteria, with the evidence your quality team and regulators expect.

Refactoring and productionising

Proofs of concept become tested, version-controlled software your team can maintain.

Training your team

Our courses give your analysts and developers the skills to build with AI and to question its output.

Monitoring and support

After launch, monitoring shows your models still meet their criteria as data and LLMs change.

Trusted By

What Can We Help You Build?

Document assistants

Tools that summarise, extract and draft from the documents your teams work with every day, such as reports, protocols and correspondence, with a person reviewing the output before it is used.

Retrieval-augmented generation (RAG)

Question-and-answer applications grounded in your own data and documents, which show the sources behind every answer so users can check them.

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Predictive models

Machine learning models for forecasting, classification and anomaly detection, built in R or Python and tested so you know how they behave before anyone relies on them.

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AI in your existing workflows

Models and LLM features built into the tools you already use, including Shiny apps, Quarto reports, data pipelines and Posit Connect, so AI supports your current processes rather than sitting beside them.

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Building AI You Can Trust

Most organisations already have AI in use. The question has moved from "can we use AI?" to "how do we show it works?", and from "which tools should we buy?" to "is our team ready to use them?" We answer both by treating AI like any other system that informs a decision. For organisations earlier on, we also support wider digital transformation, from modernising legacy tools and data to building the skills to adopt AI well.

Industries We Support

Pharma and life sciences

Risk-based validation for AI used in statistical programming, data review and document work, with evidence that fits your GxP processes and GAMP 5 (second edition) principles.

Government and public sector

Explainable models your own team can run, documented in a way that supports the Algorithmic Transparency Recording Standard and avoids vendor lock-in.

Health and research

Analysis and training for institutes and public health bodies that need rigorous, reproducible work. We have built data science capability across more than 100 researchers at the Francis Crick Institute and support Public Health Scotland's move to modern, open tooling.

Why Jumping Rivers?

Data scientists and engineers in one team. We build in R and Python and deploy on Posit, AWS, Azure and Google Cloud.
Validation is already part of our work. Our Litmus tooling grew out of R package validation for pharma.
Training is part of every engagement. Your team can maintain and extend what we build together.
Certified and easy to procure. ISO 9001, ISO 27001 and Cyber Essentials Plus. Posit Premier Partner. Available to UK public sector buyers through G-Cloud and DOS7.

ISO 27001 certified, information security

Take a Look for Yourself

Electrics Company: Applying Machine Learning Tools to Highlight Anomalous Data

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Hertfordshire County Council: Posit Deployment

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Build Your Team's AI Skills

Our trainers are practising data scientists, so courses are built around the problems your team will face. Every course can run privately, on-site or online, with examples from your own domain.

View course catalogue

AI in Production 2027

Want to see how others run AI in production? Our annual conference brings together people building, deploying and maintaining AI systems in industry, government and research, with a focus on what happens after the prototype.

Visit the conference site

10 and 11 June 2027Workshops on Thursday, talks and lightning talks on Friday
The Catalyst, NewcastleNewcastle Helix, home of the Jumping Rivers office
Call for speakers openSubmit a talk by 29 January 2027

Watch Our AI Webinars

Free sessions from our monthly webinar series, recorded and ready to watch.

A First Look at Positron and Posit Assistant

From Our Blog

What is an MCP server?

15 Oct 2026, Ishbel Carlyle & Russ Hyde

Model Context Protocol (MCP) is quietly becoming one of the most useful ideas in AI tooling. Here, we describe what MCP is, why it matters, and how to build an MCP server yourself.

Retrieval-Augmented Generation: Setting up a Knowledge Store in R

8 Jan 2026, Myles Mitchell

Learn about retrieval-augmented generation (RAG) workflows for large language models. This blog provides a hands-on introduction, including example code for creating a working knowledge store using open source packages in R.

Vetiver: MLOps for Python

27 Feb 2025, Myles Mitchell

Part 4 of our series of blogs on vetiver for MLOps. Having previously explained how to set up an MLOps workflow in R, we now turn to Python. This blog will introduce the vetiver package for Python and outline the key MLOps steps including model versioning, deployment and monitoring.

Let's Talk About Your AI Project

Whether you have a model to build, a proof of concept to productionise or a system to validate, we'll help you find the right place to start.