Enterprise AI architecture, readiness, and delivery for regulated organisations, from assessment through implementation.
Enterprise AI Services
Design, integrate, and operate digital infrastructure that connects your business securely and at scale.
Infrastructure Services
Always-on managed operations across SOC, cloud, DevOps, MLOps, and application platforms.
Cloud migration, modernisation, and transformation programmes aligned to business outcomes.
Cloud Services
Security architecture, managed SOC, vCISO, compliance, and incident response for regulated sectors.
Specialist talent and team outsourcing across digital, cloud, data, AI, and security disciplines.
Talent & Outsourcing
Industries
Resources
Production AI doesn't run itself. We keep it running.
Operationalise machine learning, ensuring models are deployed, monitored and maintained at scale. We bridge the gap between data science and production.
We build and manage infrastructure for deploying ML models into production. From containerised serving to serverless inference, we ensure models are accessible, scalable and performant.
We monitor model performance, detect drift and manage the full lifecycle from training to retirement. This ensures models remain accurate, compliant and valuable over time.
Accuracy degrades as real-world data drifts from training data, often unnoticed until business metrics suffer.
Data scientists can build strong models but have neither the time nor the remit to operate them reliably in production.
Governance, documentation, and explainability requirements for production AI systems are new territory for most teams.
Inference infrastructure is often oversized and underutilised, with no one tracking cost against actual usage.
When a production model starts producing bad outputs, there's no monitoring to catch it or runbook to respond.
Deployment is the beginning, not the end.
Models drift as real world data diverges from training data, and governance obligations continue long after go live. We own the full lifecycle of your production AI, monitoring performance, retraining on schedule, and keeping governance evidence current.
Containerised or serverless inference infrastructure, sized and scaled to real usage patterns.
Automated monitoring and scheduled retraining pipelines that keep models accurate over time.
Versioning, explainability, and documentation aligned to the EU AI Act and sector specific requirements.
Performance, incidents, governance status, cost actions, and recommendations.
Accuracy, latency, throughput, and data quality against baselines; drift and concept drift detection.
Scheduling, validation, and deployment of model updates, with pipeline design available during onboarding.
Audit trails, versioning, explainability, and alignment with the EU AI Act and sector specific AI governance.
GPU utilisation, inference efficiency, model compression where appropriate, and cloud commitment strategy.
How organisations across different sectors use managed MLOps to keep production AI accurate and governed.
A consumer lender's credit risk model was quietly losing accuracy as applicant behaviour shifted, with no monitoring in place to catch it.
An online retailer's data science team had a strong recommendation model that had never made it past a notebook into production.
A health insurer needed documented governance and audit trails for its production risk models ahead of incoming EU AI Act obligations.
A logistics provider's route-optimisation models were running on oversized, underutilised GPU infrastructure.
A consumer lender's credit risk model had been in production for over a year with no monitoring, and accuracy had quietly degraded as applicant behaviour shifted from its original training data.
An online retailer's data science team had built a strong product recommendation model, but had neither the infrastructure nor the operational remit to get it safely into production.
A health insurer's production risk models had no formal versioning, explainability documentation, or audit trail, leaving the organisation exposed as EU AI Act obligations approached.
A logistics provider's route-optimisation models were running on GPU infrastructure sized for peak load year-round, driving significant unnecessary cost.
Organisations partnering with SynaptekX for managed MLOps can achieve:
Drift caught and corrected through scheduled retraining, instead of degrading unnoticed.
Versioning, explainability, and documentation aligned to the EU AI Act and sector requirements.
GPU and cloud spend brought in line with real usage, without sacrificing performance.
A repeatable deployment pipeline that gets validated models live in weeks, not months.
Production operations handled for you, so your data science team can focus on new models.
Monitoring and runbooks that catch and contain problems before they reach the business.
Five reasons organisations trust SynaptekX with their production AI.
Engineers who understand both model behaviour and production infrastructure, not just one side.
Genuine, current understanding of EU AI Act obligations and sector specific AI governance requirements.
We own performance, retraining, and governance long after a model first goes live.
GPU and cloud efficiency built into how we operate, not treated as a separate cost review.
We take over existing models and pipelines without requiring a disruptive rebuild.
No. We operate the production infrastructure and lifecycle around models your data science team builds, freeing them to focus on model development rather than production operations.
We monitor prediction accuracy, data distribution, and concept drift continuously against agreed baselines, with alerting when metrics move outside acceptable thresholds.
Yes, this is one of our most common starting points. We audit the existing setup, close monitoring and governance gaps first, then improve from there.
Risk classification of your models, documented versioning and audit trails, explainability where required, and ongoing evidence that governance obligations are being met.
Through automated pipelines that trigger on schedule or on drift detection, with validation gates before any retrained model reaches production.
Explore the rest of our managed operations capability.
24/7 monitoring, detection, and DORA/NIS2 aligned incident response through our Security Operations Centre.
Reliability, FinOps, and security posture management across AWS, Azure, and GCP estates.
CI/CD pipeline management, infrastructure as code, and release automation, run for you.
Security scanning, compliance automation, and secure configuration embedded into your pipelines.
Application performance monitoring, release management, and day to day operational ownership.
Full lifecycle ownership for AI systems already in production.
Get in touch, and discover how our AI-driven expertise can propel your next phase of digital growth
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