Keep Your AI Running and Improving
A deployed AI system is an operational asset — not a finished project. Our Managed Services engagement handles ongoing monitoring, model optimization, use case scaling, and adoption improvement so your team captures compound returns from AI without building an in-house AI operations team.
99.5%
Target System Uptime Across All Managed Deployments
6
Industry Verticals Under Active Management
Monthly
Performance Reviews with Optimization Cycles
AWS AI Competency
part of 60 global launch partners
The Challenge
Why Production AI Systems Degrade
Launching an AI system is the beginning, not the end. Without dedicated operations, production AI loses accuracy, user trust, and business value within months.
Model Drift
AI models trained on historical data lose accuracy as business conditions, regulations, and customer behavior change. Without continuous monitoring, a system that was 95% accurate at launch can drop below acceptable thresholds within months — and nobody notices until damage is done.
Adoption Stall
Initial user excitement fades when edge cases surface, workarounds persist, and nobody is actively improving the experience. Teams revert to manual processes within weeks of launch if the system does not evolve with their feedback and operational reality.
Scaling Without a Playbook
One successful pilot proves the technology works. Scaling that pilot to additional departments, regions, or use cases introduces data source variations, new compliance requirements, and integration complexity that the original deployment never addressed.
Where We Fit
Enterprise AI Framework
Managed Services is Phase 5 — the ongoing operational layer where successful pilots scale across the organization and AI becomes a continuously improving business asset.
Service Tiers
Choose Your Operations Model
Every enterprise has different operational maturity and scaling ambitions. We offer three engagement tiers that match your requirements.
TIER 1
Monitor & Maintain
Keep your production AI system healthy with proactive monitoring, incident response, and regular performance reporting.
✓
System health monitoring and alerting
✓
Incident triage and resolution
✓
Monthly performance reports
✓
Model drift detection
Best for: enterprises with internal teams that need monitoring coverage and a safety net.
TIER 2
Optimize & Scale
Active optimization of your AI system with model retraining, new use case deployment, and adoption improvement programs.
✓
Everything in Monitor & Maintain
✓
Quarterly model retraining and tuning
✓
New use case expansion (up to 2/quarter)
✓
User adoption tracking and improvement
✓
Dedicated account manager
Best for: enterprises ready to scale AI across departments and maximize ROI from existing deployments.
TIER 3
Full AI Operations
Your outsourced AI operations team. We manage the entire AI lifecycle across all deployed modules — from monitoring through continuous improvement to new capability launches.
✓
Everything in Optimize & Scale
✓
Embedded AI operations team
✓
Continuous model retraining pipeline
✓
Unlimited use case expansion
✓
Executive quarterly business reviews
✓
Priority SLA with 4-hour response
Best for: enterprises that want compound AI returns without building an internal AI operations team.
What We Manage
Managed Services Capabilities
Every managed engagement covers the operational, technical, and strategic layers required to keep production AI systems delivering measurable business value.
Performance Monitoring & Alerting
Real-time system health dashboards, latency tracking, error rate monitoring, and automated alerting. When something degrades, our team investigates and resolves — before your users notice.
Model Optimization & Retraining
Scheduled model evaluation against accuracy benchmarks, guardrail effectiveness audits, and retraining cycles using fresh operational data. Keeps your AI system aligned with current business conditions and regulatory requirements.
Use Case Scaling
Expand successful pilots to new departments, regions, or business processes. Each scaling engagement includes data source assessment, integration mapping, compliance review, and user training for the new context.
Adoption & Change Management
Track actual usage metrics — not just logins, but task completion rates, manual override frequency, and time-to-value. Identify adoption blockers and deploy targeted training, workflow adjustments, and UX improvements.
Compliance & Governance Updates
Regulatory landscapes evolve — especially in Indonesian financial services and healthcare. We monitor OJK directives, data protection changes, and industry-specific guidance, then update guardrails and audit configurations proactively.
Infrastructure & Cost Optimization
Right-size compute resources, optimize token usage, review API call patterns, and manage cloud costs. Monthly cost-versus-value reporting ensures your AI investment stays efficient as usage scales.
Service Commitments
Built-In Operational Standards
Every managed services engagement is backed by measurable commitments. These baselines apply across all tiers, with enhanced SLAs for Tier 2 and Tier 3.
99.5%
Target System Uptime
<4hr
Critical Incident Response (Tier 3)
Monthly
Performance & Optimization Reviews
90-Day
Model Accuracy Revalidation Cycle
Industry Expertise
Managed Operations Across Six Verticals
Our operations team understands the compliance, data, and workflow requirements specific to each industry — not just the AI technology underneath.
Banking & Financial Services
FMCG & Distribution
Healthcare
Logistics & Manufacturing
Human Resources
System Monitoring
How It Works
Your Managed Services Engagement in Four Steps
A structured onboarding process that transitions your production AI system into an ongoing managed operations model with clear accountability at every stage.
Step 01
Operations Audit
Comprehensive review of your deployed AI system — architecture, performance baselines, current usage patterns, and operational gaps. Establishes the starting point for managed operations.
1 week
Step 02
SLA & Scope Definition
Define service tier, response targets, reporting cadence, and scaling priorities. Align on the specific KPIs your organization will track to measure AI operations success.
3–5 days
Step 03
Monitoring Setup
Deploy monitoring infrastructure, alerting rules, performance dashboards, and automated health checks. Connect to your existing incident management and communication tools.
1–2 weeks
Step 04
Ongoing Operations
Continuous monitoring, monthly optimization reviews, quarterly model revalidation, and use case expansion planning. Your AI system improves every cycle — not just stays alive.
Ongoing
Our Consulting Services
The Full Engagement Lifecycle
Start building with us
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