AI Managed Services
Drive growth with AI managed services that monitor, optimize, and improve system performance as your AI capabilities scale.
What are AI managed services?
AI managed services provide ongoing monitoring, optimization, and governance support for AI systems after they go live. This helps enterprises keep AI reliable, compliant, and efficient as systems scale.
10Pearls supports AI across its full lifecycle, helping enterprises design, build, integrate, operate, and optimize AI systems.
We take responsibility for AI performance and the environment it runs in, spanning models, agents, data, and infrastructure as the
AI ecosystem evolves.
Proof in numbers
20+
years delivering
enterprise technology
1,400+
engineers and
specialists globally
700+
products shipped
for clients
200+
enterprise clients across
three continents
Enterprise AI challenges that can limit business value
Model drift
AI models can become less accurate over time, leading to unreliable outputs, weaker decisions, and declining business value.
Uncontrolled AI spend
Without ongoing oversight, AI usage and infrastructure costs can grow quickly, making AI investments harder to manage.
Audit & compliance gaps
Missing governance and audit evidence can increase regulatory risk, and expose the organization to compliance issues.
Limited operational capacity
Internal teams lack the capacity to manage AI systems at scale, slowing adoption and limiting long-term value.
Benefits of AI managed services
Prevent disruption before it starts
Catch AI failures before they impact customers, revenue, or critical operations by protecting business continuity and avoiding the cost of unplanned downtime.
Resolve AI incidents faster
Minimize the financial impact of AI incidents by shortening resolution times, reducing lost productivity, and getting customer-facing systems back to full performance faster.
Protect AI quality & performance
Keep AI delivering accurate, consistent results as business needs to evolve to reduce costly errors, rework, and declining customer experiences that can erode ROI.
Control AI cost & usage
Identify where AI spend is creating value and where it is creating waste to reduce unnecessary token, compute, and infrastructure costs while improving ROI.
Keep AI audit-ready
Stay prepared for audits and compliance reviews without costly last-minute work, reducing compliance overhead and the risk of fines, delays, or lost business.
Scale capacity ahead of demand
Align AI infrastructure investment with actual business demand, avoiding unnecessary capacity costs while ensuring performance never becomes a bottleneck to growth.
Why private equity teams choose 10Pearls
AI monitoring & observability
Protect customer experience by tracking agent health performance and outcomes in real time, helping teams identify and address issues before they impact customers.
Incident response & support
Minimize downtime and customer impact with rapid diagnosis and resolution of AI incidents against defined SLA, keeping critical models and agents operational.
Performance & drift management
Maintain consistent AI accuracy and reliability by continuously monitoring model, data, and concept drift and taking corrective action before performance declines.
Prompt, model & agent optimization
Improve AI accuracy, and cost efficiency through ongoing tuning of prompts, models, and agent rules based on real-world performance and changing business needs.
AI cost & capacity management
Control AI spending and optimize infrastructure utilization by tracking usage, forecasting capacity needs, and identifying opportunities to improve resource efficiency.
Governance operations
Strengthen accountability by operationalizing access policies, and responsible AI practices across models and agents, making governance part of everyday operations.
Compliance evidence & reporting
Stay audit-ready and reduce compliance overhead by maintaining structured evidence, including logs, decision traces, & policy records, mapped to applicable requirements.
Change management & re-testing
Ship AI improvements faster and with less risk through structured review and testing of every model, prompt, or tool update before it reaches production.
Why enterprises choose us for AI managed services
Reduce AI delivery fragmentation
Our team connects AI engineering, data, applications, cloud, security, governance, and operations to reduce handoffs and keep ownership aligned throughout the AI lifecycle.
Defined operational ownership
We take ownership of defined AI operations, with clear responsibilities, SLAs, and reporting that keep performance on track and accountability clear.
Performance beyond uptime
Our teams continuously monitor model performance, drift, service health, and business impact to ensure AI delivers consistent, measurable value over time.
Operational AI governance
We turn governance requirements into daily production practices through a structured operating framework and runtime controls that keep AI compliant and accountable.
Manage AI complexity at scale
Manage your enterprise AI ecosystem across models, agents, frameworks, applications, integrations, data platforms, cloud environments, and governance requirements.
What we manage in production
AI agents & workflows
Keeping multi-step, autonomous processes running as designed
Models, prompts & tools
Version control and performance tuning across your AI stack
Runtime identity & policy controls
Enforcing who and what can access your AI systems and how
Usage, quotas & cost controls
Managing consumption limits and spend across teams and use cases
Observability & decision evidence
Logging and tracing model decisions for accountability and audit
Platforms & ecosystem
Scale AI without scaling risk
Reduce operational exposure, optimize AI spend, and maintain reliable performance so enterprise AI can support growth instead of becoming a source of complexity.
How we operate & improve AI
Onboard and transition
Assess your current AI estate and take over operations with minimal disruption.
Stabilize production AI
Closing monitoring gaps and establishing baseline performance and cost metrics.
Monitor & respond
Monitor and document incident response process keeping your systems dependable.
Optimize performance & cost
Tuning models, prompts, and infrastructure against real usage data, not assumptions.
Improve and scale
Adjusting capacity, governance, and support to keep pace as your AI footprint grows.
Choosing the right AI operating model
Fully managed
Reduce the operational burden of AI with our team managing your AI environment, so your internal teams can focus on higher-value priorities.
Co-managed
Extend your AI capabilities through a structured operating partnership where our team and your share clearly defined responsibilities and accountability.
Staff augmentation
Extend AI operational coverage with targeted expertise where we own defined responsibilities within your AI operating model.
The team behind your AI operations
Service Manager
Service governance, SLAs, and accountability
Service Architect
Service architecture & continuous improvement
AI & Platform Engineers
AI reliability, evaluation, and optimization
Support Engineers
Monitoring, production support, and incident Response
How we measure managed AI performance
Measure AI performance across the operational, financial, and business metrics that matter to leadership. See how we help enterprises align AI performance with measurable business outcomes.
Operational health
We continuously monitor uptime, latency, and system reliability against agreed service levels, providing complete visibility into the health and availability of your AI systems.
AI quality & drift
Model and agent outputs are continuously evaluated for accuracy, relevance, and consistency, enabling early detection and remediation of quality issues.
Cost and usage control
Spend against budget, cost per transaction, and usage trends across your AI estate, so leadership sees exactly what AI is costing and what it’s returning.
Incident resolution speed
We monitor incident detection, response, and resolution against SLA targets to reduce downtime and maintain confidence in your AI systems.
Capacity readiness
Infrastructure capacity and operational readiness are continuously assessed to ensure your AI environment scales seamlessly as demand grows.
Compliance & audit readiness
We keep continuous documentation aligned with applicable regulatory standards, ensuring every audit is supported by current, audit-ready records.
Business KPI progress
How AI operations translate into the outcomes your business actually cares about, from cost savings to customer experience gains, so AI stays tied to results, not just activity.
Responsible AI and human oversight
AI needs to operate in ways your teams, customers, and regulators can trust. 10Pearls builds responsible AI controls into production workflows to keep decisions explainable, risks visible, and high-impact actions accountable.
Make AI decisions explainable
Maintain decision evidence
and traceability so teams
can understand, validate,
and defend how AI outputs
are produced.
Monitor bias and model drift
Continuously assess AI behavior for emerging bias, performance degradation, and changes that could affect business or customer outcomes.
Keep humans in control
Route high-impact decisions and sensitive actions through human review, with defined approval thresholds and escalation paths.
Align with leading AI standards
Design AI operations around recognized frameworks and requirements, including NIST AI RMF, ISO/IEC 42001, and EU AI Act readiness.
Industries we support
Case studies
case study - AI Enablement
Establishing an Executive AI Roadmap for Enterprise Scale
Unifying enterprise leaders on AI strategy with ROI-driven use cases and a clear, scalable adoption roadmap.
case study - AI Enablement
Building Executive Alignment for Responsible AI Adoption
Aligning public healthcare leaders on responsible AI adoption with prioritized use cases and measurable KPIs.
case study - Media & ENTERTAINMENT
Accelerating Content Delivery with AI
Scaling multilingual, real-time video production with AI to cut costs, speed delivery, and expand audience engagement.
case study - TECHNOLOGY
Modernizing Inmate Communications with Secure Cloud AI
Rebuilding a correctional communications platform on AWS to improve call quality, automate compliance, and secure access.
case study - artificial intelligence
Improving Patient Care with Conversational AI
Automating routine pre- and post-operative engagement to ease clinician workload, improve responsiveness, and enhance patient experience.
Insights
AI/ML
The AI Opportunity in Enterprise Data
Enterprises have more data than ever, but traditional predictive approaches may not be suitable to capture the context within it....
AI/ML
AI Pilot Failure: Why Enterprise AI Pilots Don’t Scale
AI pilot failure often stems from execution gaps. Discover why enterprise AI pilots struggle to scale, and what it takes...
Healthtech
Interoperability in Healthcare: Levels, Barriers & AI Readiness
Healthcare AI needs reliable, connected data. Learn how interoperability helps organizations improve data quality, simplify integration, and build the foundation...
Advanced technology
Digital Transformation in the Energy Industry – A Practical Roadmap
What does it take to move digital transformation in energy beyond pilots? Explore the key barriers and a practical roadmap...
Fintech
Top 10 Fintech App Development Companies in 2026
Looking for a reliable fintech app development partner? Explore the top fintech app development companies in 2026 and compare their...
Company news
Orange Tree Foundation Partners with 10Pearls
10Pearls and Orange Tree Foundation join forces to unify admissions, donor, student affairs, and finance on one platform, helping OTF...
AI/ML
Understanding the uses of AI in energy sector
This blog explores how AI is transforming the energy sector, the opportunities it offers in various energy domains, and what...
AI/ML
AI in Banking Use Cases: What Works and What Stalls
A guide to AI in banking use cases, from fraud detection to document processing, the risks that stall them, and...
AI/ML
Integrating AI with Legacy Systems: Enterprise Guide
Four in five enterprises are struggling to connect AI to the systems they already run. Four proven strategies for bridging...
AI/ML
AI-powered DevOps in healthcare
AI-powered DevOps in healthcare accelerates release cycles with smarter code review, self-healing test automation, and proactive monitoring, without risking compliance.
Keep AI delivering value at scale
Get the operational expertise you need to monitor, optimize, and improve AI performance continuously.
FAQs about AI managed services
What does 10Pearls manage in a live AI estate?
We manage AI agents and their tasks, set up models and instructions, manage who can use the systems, track costs, and monitor how well your AI systems are working.
How do you detect and respond to AI performance drift?
We monitor changes in our model, data, and concept drift continuously against defined thresholds. When we notice a problem, our engineers look into what caused it, update or improve the model, and test the solution before it goes live.
How do you manage AI cost and model usage?
We monitor how much you’re using tokens and spending on your AI systems. We help you set budgets and limits when necessary, and we alert you to any unusual charges before they become unexpected costs.
How are incidents handled for AI agents and live AI workflows?
Incidents follow set rules for who is on call, how they are sorted out, and how quickly they need to be handled. Our support team figures out the main problem, whether it’s about a model, connections, or the system itself, and works to fix it while keeping you informed.
How are governance and compliance maintained after go-live?
We enforce governance policies and access controls in daily operations and maintain documentation and evidence aligned to standards like SOC 2, ISO 27001, HIPAA, GDPR, and the NIST AI RMF.
Can 10Pearls support AI systems built by another provider?
Yes. Our onboarding and transition process looks at the current AI setup, gathers knowledge and access, and creates a starting point for operations, no matter who created the original system.
How do changes to models, prompts, or tools get managed?
Every change is carefully checked and tested before it goes live, so updates make things better without causing new problems.
Can managed services include application, integration, or infrastructure support?
Yes. AI systems rarely operate in isolation, and we can extend coverage to the broader 10Pearls services around applications, integrations, and infrastructure that your AI depends on.
How do you measure AI Managed Services performance?
Performance is checked based on targets set by Service Level Agreements (SLAs). These targets include how well operations are running, how fast incidents are resolved, the quality of AI, changes in AI performance, costs and usage, readiness for capacity, compliance status, and progress towards business goals (KPIs).