Frequently asked questions about Artificial Intelligence

It depends on the type of work. A predictive modelling project with clean, structured data typically falls between £18,000 and £45,000. That covers the data audit, model development, validation, and deployment as a production API. Computer vision projects run higher because of the image labelling and GPU training costs; expect £30,000 to £70,000 for a system that handles real-time inspection on a production line.

We always agree a fixed-price ceiling before work starts. If our estimate turns out to be wrong, we absorb the overrun, not you.

A standard predictive model takes six to ten weeks from data audit to production deployment. The first two weeks are discovery: understanding your data, cleaning it, and agreeing on success metrics. The remaining weeks are iterative development in fortnightly sprints.

Computer vision projects usually run eight to fourteen weeks, depending on how many training images we need to collect and label. NLP projects fall somewhere in between. We give you a detailed timeline during the proposal stage, and we have hit our deadlines on 93% of projects over the past three years.

Not always. For tabular predictive models (demand forecasting, lead scoring, anomaly detection), a few thousand rows of clean historical data is often enough to build a useful first model. We have built effective churn-prediction systems on as few as 4,000 customer records.

Image classification models need at least 500 labelled images per category. If you have fewer, we use data augmentation techniques (rotation, cropping, colour jitter) to stretch smaller sets. We can also help you set up a labelling workflow using open-source tools so your team can build the dataset over time.

Yes. We deploy models as REST APIs that slot into your existing infrastructure. We have delivered integrations with SAP, Salesforce, Oracle, Microsoft Dynamics, and several bespoke ERP and warehouse management systems. Our engineers handle the API layer, authentication, and data pipeline so your internal team does not need to become machine learning experts.

If your systems are on-premise and cannot connect to the cloud, we can deploy models on local servers or edge devices. We have done this for manufacturing clients with strict data-sovereignty requirements.

Most of our clients are in manufacturing, logistics, retail, financial services, and legal. We have also completed projects for healthcare providers (clinical document classification) and property management companies (predictive maintenance for building systems). The common thread is businesses with enough historical data to train a model and a clear operational problem that data can help solve.

Every project includes three months of post-deployment support at no extra charge. During that period we monitor model performance, fix bugs, and retrain the model once if your data distribution shifts.

After the initial three months, we offer monthly monitoring contracts starting at £800 per month. The contract covers automated performance alerts, quarterly retraining, and up to four hours of engineering support per month. Most of our manufacturing clients opt for this because production data drifts seasonally.

You do. Once the project is complete and paid for, all custom model weights, training scripts, deployment configurations, and documentation are yours. We retain no proprietary claim. You can take the code to another provider, modify it internally, or open-source it if you choose.

The only exception is our internal tooling libraries, which we license to you for use within the project but do not transfer ownership of. These libraries handle routine tasks like data validation and logging; they are not the core intellectual property of your model.

We sign non-disclosure agreements before seeing any client data. For projects involving personal data covered by UK GDPR, we act as a data processor under a formal Data Processing Agreement. All data is encrypted in transit (TLS 1.3) and at rest (AES-256).

For healthcare clients, we work within NHS Digital's Data Security and Protection Toolkit requirements. For financial services, we follow FCA guidelines on algorithmic decision-making, including model explainability and audit trails. We can provide detailed compliance documentation during the scoping phase.