From initial model development to full production deployment — each service bay is a defined engineering capability with clear intake requirements, build methods and delivery outputs.
Each service station maps a business requirement category to a defined engineering method, a set of deliverables and a practical outcome. Select the bay that matches your current requirement.
Full-stack engineering of bespoke AI-powered applications from specification through to production deployment. We architect, build, test and deploy complete intelligent software systems tailored to your operational context, data environment and integration requirements. This service covers the full application lifecycle — not just the model layer.
End-to-end model development covering data preparation, architecture selection, training pipeline construction, hyperparameter optimisation and performance validation. We build models to agreed accuracy and performance specifications — not to a theoretical maximum that may not match your production requirement.
End-to-end predictive analytics systems that convert operational data into actionable business intelligence — demand forecasting, risk scoring, customer lifetime value modelling, churn prediction and operational capacity planning. We build on your live data and integrate output with your existing reporting and decision tooling.
Machine learning-driven automation of document processing, classification, routing and operational decision workflows. We replace manual review and data entry tasks with configurable, auditable AI pipelines that maintain human-in-the-loop controls where required by your compliance or operational framework.
Convolutional, transformer and recurrent neural network architectures for computer vision, natural language processing, time-series forecasting and complex pattern recognition tasks. We design, train and deploy architectures matched to the specific problem — including fine-tuning of foundation models where appropriate and cost-efficient.
Taking trained models from development environments into production infrastructure — your on-premise systems, private cloud or public cloud provider. We design the serving architecture, build the API layer, configure monitoring, set up alerting and complete integration testing before handover.
AI projects do not require complete data or a fully formed specification to begin. We conduct a structured feasibility review at the start of every engagement to assess what is available and what is needed. The following gives you a practical guide to what makes a strong starting position.
Project timelines depend on data readiness, system complexity and the required integration depth. A focused model development engagement — data already prepared, clear specification — typically runs six to twelve weeks. A full AI application build covering data pipeline, model development, API layer and deployment is more typically four to six months. We provide a detailed timeline estimate in our scoping proposal after the initial review.
Messy or incomplete data is extremely common and is not automatically a blocker. Our data bench preparation stage covers data quality assessment, cleaning, structuring and pipeline design. We will give you an honest assessment of whether the data is sufficient for the intended application and what data collection or remediation work might improve the outcome.
Yes. We build to integrate with your existing infrastructure rather than requiring you to adopt a specific platform. We work across major cloud providers (AWS, Azure, GCP), common data warehouse and BI tools, ERP and CRM systems, and custom internal applications. We design the integration architecture to fit your environment, not the other way around.
All deployments include an initial monitoring and hypercare period. We provide a full technical documentation package covering system architecture, operational procedures, retraining schedules and troubleshooting guidance. We offer ongoing maintenance and retraining engagement for clients who want continued ZORVAYA involvement post-handover, or a full knowledge transfer for clients who prefer to manage the system internally.
Yes. We operate under formal Data Processing Agreements on all client engagements. We are familiar with UK GDPR requirements and have experience working with data in regulated sectors including financial services and legal. Data handling, storage, access controls and processing are designed to meet the requirements of your specific regulatory context.
We define performance targets collaboratively during scoping, based on what the available data and problem structure can realistically support. Where a target is achievable based on our feasibility assessment, we commit to it as a delivery condition. Where data or problem complexity makes a specific target uncertain, we will document that clearly rather than make commitments we cannot be confident in keeping.
Submit a work order with your requirement, available data and project context. Our engineering team will review and respond with a structured scoping proposal within two business days.
