AIOps-as-a-Service for Production AI
From monitoring and retraining to governance and cost optimisation, we operate the AI systems your business depends on.
What we do
We operate the day-to-day lifecycle of production AI on your infrastructure. From monitoring and retraining to governance, incident response, and continuous optimisation, we make sure your AI keeps delivering measurable business value.
Selected AIOps-as-a-Service work
Explore the operational capabilities that keep production AI reliable, compliant, and continuously improving. From monitoring and retraining to governance and cost optimisation.
BUILDING A HIGH-TRAFFIC TRAVEL BOOKING PLATFORM FOR A LEADING UK PUBLISHER
Building a custom CMS-based product with frontend and backend development, infrastructure setup, and optimisation..
DELIVERING A REAL-TIME DRUG SHORTAGE PLATFORM FOR GLOBAL PHARMA MARKETS
Developing frontend and backend systems with integrations and scalable architecture to ensure accurate, real-time data across multiple markets.
What you get
A dedicated team, proven operating processes, and a single control layer for your AI systems. You’ll gain continuous monitoring, retraining workflows, governance support, cost visibility, and ongoing improvements without building an in-house AIOps function.
Product design
UX and UI design that helps shape intuitive user journeys, validate concepts early, and create interfaces that are ready for development across web and mobile products.
Mobile development
Native or cross-platform mobile experiences designed to work seamlessly across devices while supporting product growth, usability, and consistent performance.
Solution architecture
A clear technical foundation that defines how the product is structured, how systems interact, and how the solution can scale securely and reliably over time.
DevOps and infrastructure
Cloud infrastructure, CI/CD pipelines, environments, and deployment practices that support fast releases, reliable operations, and long-term product stability.
Frontend and backend development
Robust product engineering across client-side and server-side layers, ensuring performance, maintainability, and smooth delivery of business-critical functionality.
Quality assurance and release management
Structured testing, quality control, and release processes that reduce risk, support reliable delivery, and ensure the product is ready for production use.
How AIOps-as-a-Service Works
01
Understand Product and Business Needs
We start by understanding the product vision, business goals, user needs, and technical context. This helps define what needs to be built, what constraints matter, and what the product needs to succeed in production.
02
Define Scope, Architecture, and Delivery Approach
We shape the product structure, align on priorities, and establish the right technical and delivery foundations — from architecture and environments to development workflow and release planning.
03
Design and Build the Product
Our teams move from validated concepts into implementation, covering product design, frontend and backend engineering, mobile development, and infrastructure setup as needed.
04
Test, Refine, and Prepare for Release
Through QA, testing, and cross-team collaboration, we identify issues early, refine the product, and ensure quality, performance, and readiness before launch.
05
Release and Continuously Improve
We support deployment, monitor performance, and continue improving the product through iterations, technical enhancements, and ongoing delivery aligned with business needs.
From ISO to AWS, we got it
Quality & reliability
We pride ourselves on delivering top-tier quality and reliability, backed by our AWS Select Tier partnership and recognition by Clutch as one of the top 15 companies in our field. Our commitment is reinforced through ISO-certified standards in quality, security, and privacy – ensuring our clients receive services that are consistently secure, compliant, and dependable.
When to Invest in AIOps-as-a-Service
AIOps-as-a-Service becomes valuable once AI moves beyond experimentation into business-critical operations. Whether you’re launching your first production model or scaling multiple AI products, it helps reduce operational risk while keeping performance, costs, and compliance under control.
Launching a New Digital Product
When a new idea needs to move from concept to production with the right mix of design, architecture, development, testing, and release planning.
Scaling Product Complexity
When business growth introduces more users, integrations, workflows, and operational demands that require stronger technical foundations.
Modernising an Existing Product
When legacy systems, technical debt, or outdated architecture limit performance, usability, scalability, or release speed.
Improving Delivery Reliability
When quality issues, unstable releases, or fragmented ownership slow teams down and create risk around production readiness.
Building Across Web and Mobile
When a product needs to work consistently across platforms and requires coordinated frontend, backend, mobile, and infrastructure delivery.
Needing One Delivery Partner Across the Lifecycle
When it is more efficient to work with one team that can cover design, architecture, engineering, QA, DevOps, and release management end to end.
Partner with us
Looking to build or scale your product with AIOps-as-a-Service?
FAQ
What is included in AIOps-as-a-Service services?
The service typically includes production monitoring, alerting, model retraining, governance workflows, incident response, performance optimisation, cost reporting, and ongoing operational support tailored to your AI environment.
Can AIOps-as-a-Service work for both new and existing products?
Yes. It can support newly launched AI solutions from day one or take over the operation and optimisation of AI systems already running in production, regardless of whether they were built by Q or another team.
How is AIOps-as-a-Service different from traditional DevOps?
While DevOps focuses on application infrastructure and deployment, AIOps-as-a-Service manages the ongoing operation of AI systems—including model performance, data quality, retraining, governance, and AI-specific monitoring—to keep production AI reliable over time.