AI & machine learning development
A GOOD FIT WHEN
For businesses with a specific document, information or prediction task. We need access to suitable examples, permission to use them and someone who understands what a good result looks like. A feasibility review can establish what is worth building.
The final scope, deliverables and acceptance criteria are agreed in writing before the build begins.
FROM QUESTION TO HANDOVER
Understand the current process, the information available and the consequences of an incorrect result. Check whether search, rules or an existing tool can do the job.
Connect an existing AI model or develop an ML approach suited to the task. Start with a limited workflow that can be tested before wider use.
Compare with the current process or a simpler method. Check accuracy, missing or unsupported answers, response time and running costs. Investigate differences across relevant groups or data segments.
Agree access controls, human review, failure handling and provider terms. Plan how results will be checked as the data, documents or models change.
TWO DIFFERENT KINDS OF PROJECT
An assistant might search approved documents, show the source passages and prepare a draft for review. The work is in retrieval, permissions, the interface, evaluation and safe integration. Training a new model is often unnecessary.
A forecasting tool needs historical observations, a useful baseline and evaluation on data it has not trained on. The work may include data preparation, feature selection, training or adaptation, and monitoring after deployment.
SCOPE & OWNERSHIP
A feasibility review may show that the available data cannot support the proposed system. Assistants can produce incorrect answers, and predictions have uncertainty. Sensitive information and provider terms are reviewed before integration. High-stakes automated decisions, foundation-model training and large research programmes need specialist scope and are outside the standard offer.
AI services usually have model or API charges as well as hosting costs. Document collections need maintenance, and provider updates can change results. ML systems need checks for changes in data and accuracy, with retraining when warranted. Agree who reviews performance, handles failures and approves updates.
Our pricing approachUSEFUL QUESTIONS
AI integration connects an existing model to your documents, tools and user interface. A document assistant, for example, retrieves permitted source passages and helps prepare an answer. The work includes permissions, retrieval, evaluation and integration. ML development builds or adapts a model for a task such as forecasting demand or classifying records. It involves preparing data, choosing an approach, training or adaptation, and evaluation on examples kept separate from training.
The task determines what is needed. A document assistant needs reliable source material and permission to use it. A forecasting model needs enough consistent historical observations to learn and test relevant patterns. Data preparation may be the first project. Some ideas are not viable with the information available.
The degree of automation depends on measured reliability and the consequences of a mistake. A system may prepare a draft, suggest an action or complete a limited task. Human review, restricted permissions and a fallback are part of the design where needed.
No. Page / Find uses fixed, synthetic text to demonstrate how an answer can show its source. It makes no AI requests. A live service would require document permissions, retrieval evaluation and ongoing checks.
Service notes / 03
A short introduction to the work.
Text-led animation. No audio.
All key points appear on screen and in the transcript. Diagrams and data are illustrative.
Start with a useful task, suitable data and a clear way to judge results.
Three questions guide the project: what should change, what data can be used, and how will success be judged?
A USEFUL OUTCOME COMES FIRST
Example: search approved documents and draft answers with source links.
Approved documents feed a draft answer with source links, followed by human review.
INTEGRATING EXISTING AI MODELS
Train or adapt a model for a defined task, such as demand forecasting.
A clearly labelled illustrative chart separates observed history from a forecast. It is not client data or a claim of predictive accuracy.
DEVELOPING A SCOPED DATA SYSTEM
Check data quality, privacy, errors and human review before rollout.
A checklist covers data quality, privacy, error checks and human review.
EVIDENCE BEFORE AUTOMATION
Measure quality and cost. Monitor changes. Maintain the system.
Quality, cost and changes are reviewed so the system can be adapted and maintained.
BRING A TASK AND THE DATA BEHIND IT
Next / Your project
A new direction, a stubborn problem, an idea that deserves to exist. Tell us what you have in mind.