Frequently asked questions about our AI software

We build a broad range of AI software products including predictive analytics dashboards, computer vision inspection systems, natural language processing pipelines, recommendation engines, and custom deep learning models. Every solution is tailored to the client's specific industry, data landscape, and business objectives rather than being a one-size-fits-all product.

Timelines vary depending on scope and data readiness. A focused proof-of-concept can be delivered in as little as four to six weeks. For production-grade systems that include data engineering, model training, integration with existing platforms, and user-interface development, you should plan for three to nine months. We always provide a detailed project plan with milestones before work begins so there are no surprises.

Absolutely. Although our headquarters are in Scotland, we regularly collaborate with organisations across Europe, North America, and the Asia-Pacific region. Our team is experienced in remote delivery, using video calls, shared repositories, and cloud-based development environments to keep projects on track regardless of geography. We are also happy to arrange periodic on-site visits when the project warrants it.

Our portfolio spans healthcare, financial services, retail and e-commerce, manufacturing, logistics, energy, and the public sector. Because our approach starts with understanding the business problem rather than forcing a technology, we adapt quickly to new domains. If your industry is not listed here, we are still likely to be a good fit — just get in touch and we will discuss your specific needs.

Having existing data is helpful but not strictly required. During the discovery phase we audit your current data assets, identify gaps, and recommend strategies for collection, augmentation, or the use of synthetic data. In some cases we can leverage publicly available datasets or transfer learning techniques to get a minimum viable model running while you build out your proprietary data pipeline in parallel.

We offer three engagement models: fixed-price for well-defined scopes, time-and-materials for exploratory or evolving projects, and retainer agreements for ongoing support and continuous improvement. Every engagement starts with a free scoping call where we understand your requirements and provide a transparent estimate. There are no hidden fees, and we break costs down by phase so you can make informed decisions at each stage.

Our team is proficient in Python, PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, OpenCV, and Apache Spark, among other tools. For deployment we work with Docker, Kubernetes, AWS, Azure, and Google Cloud. We choose the stack that best fits each project rather than imposing a single toolchain. If your organisation already has technology preferences or constraints, we are happy to work within them.

Security is embedded into every stage of our workflow. We sign non-disclosure agreements before any data is shared, use encrypted storage and transfer protocols, and restrict access on a need-to-know basis. Our development environments are isolated, and we follow UK GDPR guidelines rigorously. For highly sensitive projects we can work entirely within your own infrastructure so data never leaves your control.

Yes, integration is a core part of our service. We build RESTful APIs, event-driven microservices, and batch processing pipelines that plug into ERP platforms, CRM systems, data warehouses, and custom internal tools. During the discovery phase we map your technology landscape to design an integration architecture that minimises disruption and maximises the value of your existing investments.

Deployment is not the finish line — it is the beginning of continuous improvement. We offer post-launch support packages that include model performance monitoring, automated retraining pipelines, bug fixes, and feature enhancements. Our MLOps engineers set up dashboards that track key metrics like prediction accuracy, latency, and data drift, alerting your team before issues affect business outcomes.