We design, build and deploy AI-powered applications and machine learning systems that solve real business problems — on time, to specification, production-ready.
Every engagement follows a structured build process — from requirement intake to deployed, validated, production-grade AI output.
We do not ship ML systems that have not been validated against real performance targets. Our inspection standard covers accuracy, fairness, robustness, explainability and production behaviour — applied consistently across every engagement before handover.
A selected record of AI systems built, validated and handed over to clients across the United Kingdom and internationally.
End-to-end predictive analytics system built for a UK multi-site retailer. The platform ingests point-of-sale, inventory and external demand signals to produce 12-week demand forecasts at SKU and location level, reducing overstock and stockout incidents across the estate.
Anomaly detection model deployed at transaction level for a UK fintech platform, operating at under 80ms latency with configurable risk thresholds.
NLP extraction pipeline for a legal services firm, processing contracts, correspondence and filings with structured entity and clause output via REST API.
Computer vision inspection platform detecting surface defects and dimensional non-conformances on a manufacturing line, replacing manual visual inspection.
The majority of failed AI initiatives do not fail because the underlying technology was inadequate. They fail because the delivery framework was wrong — models built in isolation from production requirements, data pipelines not hardened for operational load, and validation processes that measured training accuracy rather than real-world business impact.
At ZORVAYA LTD, we apply a production-first engineering discipline from the first scoping conversation. Every system we build is designed for the operational context in which it will run — not for a controlled demonstration environment. We define success metrics against your business outcomes before we write a line of training code.
This means slower early stages, more questions, more pushback on scope when necessary. It also means systems that work in production, that are maintainable by your team, and that continue to deliver value after the initial deployment.
A model that performs at 97% accuracy in a notebook and fails in production is not a successful AI project. We build for the second metric, not the first.
Six dedicated service areas, each mapped to a specific category of AI and machine learning requirement. Every bay includes a defined intake, build method and delivery standard.
Full-stack AI application engineering — from specification to deployed, tested, production system. We architect solutions around your data environment, business logic and integration requirements.
End-to-end model development covering data preparation, architecture selection, training, hyperparameter optimisation and validation. We build models to your performance specification.
Demand forecasting, risk scoring, lifetime value modelling and operational prediction systems built on your live data, integrated with your reporting and decision tools.
ML-driven automation of document processing, classification, routing and decision workflows — replacing manual review with configurable, auditable, scalable intelligent pipelines.
Convolutional, transformer and recurrent architectures for computer vision, natural language processing and sequence prediction tasks requiring complex pattern recognition at scale.
Integrating trained models into your existing infrastructure — ERP, CRM, data warehouses, internal tools — with robust API design, monitoring, alerting and operational documentation.
Every ZORVAYA engagement follows a defined seven-stage production sequence. No shortcuts. No black-box development.
Requirement intake, problem definition, data asset review and feasibility assessment. We establish what success looks like before committing to a build path.
Data audit, quality assessment, pipeline architecture and feature engineering design. We do not start model development on unprepared data.
Algorithm selection, architecture design, baseline experiments and approach sign-off. We document the selection rationale and present alternatives considered.
Model development, training runs, iteration cycles and performance optimisation against agreed targets. Tracked in a managed experiment environment with full reproducibility.
Model validation, bias testing, edge case analysis and production readiness assessment against the inspection standard defined at project start.
Production deployment to your infrastructure or cloud environment, integration testing, monitoring configuration and load validation before go-live.
Post-deployment monitoring, model drift tracking, retraining cycles and feature iteration support. The handover is a beginning, not an end.
Production metrics, client category results and quality indicators from completed work records.
"The demand forecasting system delivered exactly what was specified. Inventory waste down significantly in the first quarter of operation, and the model has required no major intervention since deployment."
"ZORVAYA built our fraud detection system to a precision spec we thought would require a larger team. The delivery was clean, documented and has operated reliably at transaction volume since day one."
"The document processing pipeline handles our contract review workload with consistent extraction quality. The accuracy thresholds were calibrated exactly as we needed — configurable and auditable."
Submit a work order and our engineering team will review your requirement, assess feasibility and respond with a structured scoping proposal — typically within two business days.
After submission: review in 2 business days · Scoping call within 5 days · No obligation to proceed
