Launching AI is only the beginning. We keep your models sharp, your teams confident, and your applications performing — with the skills, talent, and managed support to sustain your AI investment long-term.
Full audit of your deployed models: accuracy drift, inference cost, latency, and bias. We optimise and fine-tune to cut costs by 30–50% while improving real-world performance.
Tailored workshops and certification programmes from executive AI literacy to hands-on engineering training. Change management and enablement so your team owns the AI, not just uses it.
Staff augmentation with senior ML engineers, AI architects, and data scientists. Embedded in your workflow, aligned to your processes, available for short sprints or long-term engagements.
24/7 monitoring, proactive issue resolution, and SLA-backed managed services. Continuous performance optimisation and model refresh cycles so your AI never goes stale.
Models degrade silently. Data distributions shift, user behaviour changes, and yesterday's high-performing model becomes today's liability — often without obvious warning signs until something breaks in production.
Our assessment process gives you complete visibility into how every model in your portfolio is actually performing, then applies targeted fine-tuning and optimisation to bring it back to — or beyond — its original standard.
Typical reduction in inference costs after optimisation
Typical time to deliver a full model health report
We work with models you built or bought, regardless of platform
Accuracy, precision, recall, latency, throughput, and cost per inference. Benchmarked against deployment-time baselines.
Data drift and concept drift analysis. Quantify how much the real-world data has diverged from training data.
LoRA, QLORA, prompt tuning, distillation, quantisation. Smaller, faster, cheaper models without sacrificing the accuracy your users expect.
Dashboards, alerts, and retraining triggers so drift never catches you off guard again.
The most durable AI advantage comes from internal capability. We help you build it — through structured upskilling and by embedding senior AI talent directly in your team.
From "what is AI?" for the boardroom to production ML engineering for your developers — we design and deliver training that sticks.
Senior AI talent embedded in your workflow — without the 6-month hiring process. We match the right mix of skills to your specific programme.
AI systems require ongoing care: data pipelines break, models drift, APIs deprecate, traffic spikes. Our managed service means you have a dedicated team watching, responding, and optimising around the clock.
Uptime, performance metrics, cost usage, and anomaly detection across all your AI services.
Defined response and resolution times. Escalation paths. No ambiguity when something goes wrong.
Scheduled model reviews, dependency updates, and performance tuning — before issues become incidents.
General MSPs handle infrastructure. We specialise entirely in AI — model performance, data quality, LLM behaviour, and ML pipeline reliability. We understand what "a model is underperforming" means and what to do about it, which a general IT team typically doesn't.
We typically have a team proposal to you within 48 hours and the first engineers onboarded within 1–2 weeks. We move quickly because we maintain a bench of available senior AI talent rather than recruiting reactively.
Both. We offer fully remote workshops, on-site immersives, and blended programmes. Most clients prefer a hybrid — on-site for the initial kickoff and hands-on labs, remote for follow-up sessions and ongoing coaching.
Under our AMS agreement, degradation is caught by monitoring before it becomes a business problem. You get a notification, a root cause analysis, and a remediation plan — most issues are resolved without you needing to do anything.
Tell us what you've built and what you need to keep it sharp. We'll design a support or enablement package that fits.
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