MANAGED SERVICES

Managed AI

A turnkey service to run and improve AI infrastructure and AI solutions in production.

Our service model Let's talk

FRAMEWORK

AI,
WHAT
A GIFT

Companion Economy: Where emotional connection becomes the most valuable currency

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73%


of AI use is outside work*

 

 

46%


active AI users use it at least occationally for decision making or comparison (Global)**

 

 

80%


larger shopping cart value when AI is used in decision making (UK)**

 

 

83%


believe the use of AI will result in a wide range of benefits (Global)**

 

Source: *NBER Study Sep 2025, **KPMG

 

Position early to scale reach and 
deepen the customer relationship layer

With ChatGPT and Google’s AI Mode becoming everyday tools, people are no longer relying only on brand websites or apps. Instead, they’re shopping, searching, and making decisions through AI-driven conversations that skip the usual steps. This is both threat and opportunity for companies.

To retain loyalty and customer engagement, companies need to figure out how they tap onto this new kind of customer behavior with their own AI-companions, in their own channels.

In the Companion Economy, growth isn’t about adding more features or squeezing people through rigid purchase and loyalty program funnels. It’s about being close, helpful, and trustworthy where needed and when needed — offering solutions that are personal, timely, predictive, and reliable.

The next big shift isn’t about who has the most advanced technology. It’s about who own the client relationship built on trust.

From Tools to Companions:
The Rise of the Agentic Age


AI agents are no longer tools — they're trusted companions.


Emotional utility is overtaking transactional value.


Your brand is either a companion – or background noise.

Match with the market speed on moving AI from PoC to production

The enterprise AI market is experiencing a critical transition. Organizations are discovering that moving from PoC to production AI requires specialized operational capabilities they lack internally:

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Technical expertise

Production AI demands expertise in model management, data pipelines, full stack development, infrastructure engineering, security, and governance.

Monitoring, evaluation & quality improvement

AI solutions need more than just technical monitoring. A database going down is obvious; a model producing subtly wrong outputs is harder to detect and potentially more damaging.

Cost & performance management

Costs can become unpredictable and new, more suitable models emerge. In production AI, the need for fit-for-purpose models is evident, and model selection should be evaluated regularly.

Managed AI Service Model

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Managed AI Service Model

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The production AI challenge

Building a proof of concept is one thing. Running AI reliably in production is entirely different. Once AI moves beyond the pilot phase, you face operational challenges that require specialized skills, infrastructure, and around-the-clock attention:

CHOICE I

The reliability question

Will you monitor and retrain AI models continuously — or let performance quietly degrade? Models trained on historical data gradually become less accurate as real-world patterns shift. Without continuous monitoring and retraining, your AI quietly becomes less reliable.

CHOICE II

The cost governance decision

Do you have cost governance built to support scaled deployments across business units? Lack of predictability blocks investment.
Token usage fluctuates, retrieval patterns change, and infrastructure scales up unexpectedly. What started as a manageable monthly cost can quickly spiral without proper governance.

CHOICE III

The risk ownership dilemma

Are you confident your AI-driven innovations won’t slow down at the compliance checkpoint? Production AI needs built-in guardrails for GDPR, data privacy, and auditability. Production AI must protect sensitive data, maintain audit trails, and comply with regulations like GDPR and EU AI Act. One misconfigured endpoint or inadequately anonymized dataset creates serious risk.

CHOICE IV

The operational readiness moment

Running AI at scale demands new processes, alerts, and support. When your AI system fails at night or starts producing incorrect results, who gets the alert and when are you ready to receive one? Who knows how to troubleshoot it? What's the rollback procedure? Most organizations don't have the capacity to build and maintain specialized AI operations teams.

Steps in taking AI from PoC to production

1.
Assess the production readiness

2.
Prepare the foundations

3.
Implement the production-ready solutions

4.
Run and continuously improve the solutions

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Managed AI Solutions

We ensure your AI models and AI solutions are deployed safely, reliably, and secure, with continuous monitoring and improvement. Once live, our team handles daily AI operations: managing the AI platform, keeping data fresh, monitoring quality and costs and responding to incidents.

Full-stack AI operations

We manage the entire AI stack – from infrastructure and data pipelines to model serving and application integration. Whether you're running traditional ML models, large language models, or both, we handle the operational complexity.

Proactive monitoring & incident management

Real-time tracking of model accuracy, latency, data drift, and infrastructure health. When issues arise, our team responds rapidly with root cause analysis and preventive measures to avoid recurrence.

Model training & evaluation

Continuous optimization of model performance through fine-tuning, scheduled retraining, prompt engineering, and parameter tuning – all linked to evaluations to ensure stable performance. We incorporate fresh data to maintain accuracy and relevance over time.

Performance & cost optimization

Ongoing adjustments to maximize speed, throughput, and cost efficiency. We keep infrastructure, token usage, and optimization costs transparent with clear models that avoid surprises.

Security & sovereign AI

Data governance, access controls, audit logging, and compliance monitoring built into operations. We help you maintain GDPR compliance and support on sovereign AI.

Vendor-neutral & flexible

Work in your chosen cloud environment (Azure, AWS, Google Cloud) with your preferred models and tools. Or leverage our sovereign LLM infrastructure for full data control. We adapt to your architecture or utilize our frameworks.


Managed AI Solutions

We ensure your AI models and AI solutions are deployed safely, reliably, and secure, with continuous monitoring and improvement. Once live, our team handles daily AI operations: managing the AI platform, keeping data fresh, monitoring quality and costs and responding to incidents.

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What you get

Full-stack AI operations
We manage the entire AI stack – from infrastructure and data pipelines to model serving and application integration. Whether you're running traditional ML models, large language models, or both, we handle the operational complexity.

Proactive monitoring & incident management
Real-time tracking of model accuracy, latency, data drift, and infrastructure health. When issues arise, our team responds rapidly with root cause analysis and preventive measures to avoid recurrence.

Model fine-tuning & retraining
Continuous optimization of model performance through scheduled retraining, prompt engineering, and parameter tuning. We incorporate fresh data to maintain accuracy and relevance over time.

Performance & cost optimization
Ongoing adjustments to maximize speed, throughput, and cost efficiency. We keep infrastructure, token usage, and optimization costs transparent with clear models that avoid surprises.

Security & sovereign AI
Data governance, access controls, audit logging, and compliance monitoring built into operations. We help you maintain GDPR compliance and support on sovereign AI.

Vendor-neutral & flexible
Work in your chosen cloud environment (Azure, AWS, Google Cloud) with your preferred models and tools. Or leverage our sovereign LLM infrastructure for full data control. We adapt to your architecture or utilize our frameworks.

 

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SIILI-Media-AI-powered_software_development-5_4

The Perceived Customer Value of ChatGPT was valued at 83€ per month.

More than Spotify, Netflix and Facebook combined.

Source

Key tools & technologies

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Our insights

What is Managed AI?

What is Managed AI?

→
AI from promising prototype to production reality

AI from promising prototype to production reality

→
The strategic reset your AMS relationship needs

The strategic reset your AMS relationship needs

→

Managed AI FAQ: From AI proof of concept to reliable production

Have questions about running AI in production? Here are the key things to know about Managed AI, AI operations, model monitoring, cost governance, and how Siili helps organizations turn promising AI solutions into reliable business value.

It covers the operational layer between a successful proof of concept and a reliable, business-critical AI system.

Where MLOps and LLMOps describe the tooling and practices internal teams use, Managed AI describes an operating model where a partner takes shared accountability for the system as a whole — across cloud infrastructure, data platforms, model serving, application integration, monitoring, incident response, security, cost governance, and continuous optimization.

Siili's Managed AI service supports traditional machine learning models, large language model applications, AI agents, and AI-powered digital services. The goal is to move teams from "the AI works in a demo" to "the AI works reliably in our business every day."

Because production AI is a different problem than prototype development. Most organizations don't realize how different until they try to scale.

Recent research is consistent: only about 5% of AI pilots make it into production use, and analysis from MIT, RAND, and Gartner places the broader AI project failure rate between 50% and 80%, depending on the segment. The root causes are rarely the AI itself. They're data foundations that weren't designed for continuous quality, infrastructure that wasn't built for the cost and latency profile of inference workloads, governance that wasn't planned for GDPR and the EU AI Act, and unclear ownership of the system once it goes live.

A successful pilot proves the use case works. Production requires answering different questions: Who gets the alert at night when latency spikes? What's the rollback procedure when a model starts hallucinating? How do you keep token costs predictable as adoption grows across business units? Who has accountability for compliance evidence when the auditor arrives?

Siili helps organizations close this gap by assessing production readiness, preparing the foundations, implementing production-ready solutions, and operating and improving them continuously.

Siili's Managed AI is a full-stack service. We take responsibility for keeping your AI infrastructure and AI solutions running reliably, securely, and cost-effectively in production — and for improving them as your business evolves.

In practice, this covers:

  • Infrastructure and platform operations — Cloud and AI platform management, autoscaling, GPU and serverless inference, multi-cloud support
  • Data operations — pipeline reliability, freshness, quality monitoring, retrieval (RAG) and vector database upkeep
  • Model and prompt operations — evaluation, fine-tuning, retraining, prompt versioning and optimization
  • Reliability operations — proactive monitoring, incident response, root cause analysis, rollback procedures
  • Security and compliance — access controls, audit logging, GDPR, EU AI Act readiness, sovereign AI options
  • Cost governance — fit-for-purpose model selection, FinOps and token economics, transparent cost reporting

The service is vendor-neutral and works across Azure, AWS, and Google Cloud. For organizations with sovereignty requirements, Siili also offers sovereign AI and infrastructure options with selected partners, running open-source and fine-tuned models on Nordic, EU-jurisdiction GPU infrastructure with no dependency on US hyperscalers.

By treating AI as a system that degrades quietly, not a piece of software that either runs or doesn't.

A database going down is obvious. A model that has started producing subtly wrong outputs — same uptime, same latency, same dashboards — is much harder to detect and often more damaging. Siili's Managed AI combines technical monitoring with model-specific evaluation:

  • For traditional ML: data drift detection, segment performance tracking, retraining triggers
  • For LLM applications: output quality and groundedness scoring, hallucination detection, prompt regression testing, response evaluation with LLM-as-judge and human review
  • For AI agents: trace-level observability across multi-step reasoning, tool call correctness, loop and cost guardrails
  • For the underlying platform: latency, throughput, GPU utilization, infrastructure health

When something goes wrong, we handle root cause analysis, incident response, rollback, prompt or parameter tuning, retraining, and preventive improvements — under agreed service levels.

By making AI spend visible at the workflow level — not just the monthly invoice — and by applying AI FinOps discipline to the levers that actually move the bill.

AI costs don't behave like traditional infrastructure costs. They're usage-based, non-deterministic, and can vary by 30x or more between an unoptimized and a well-optimized deployment of the same use case. Where the cost typically hides:

  • Output tokens, which often cost 3–5x more than input tokens, especially when models are allowed to be verbose
  • System prompts that grow over time and get re-sent on every call
  • Premium models used for simple tasks that smaller, cheaper models would handle equally well
  • Repeated calls with no caching, where the same question gets billed at full price every time
  • Agentic loops that multiply token consumption 5–30x per task compared to a single chatbot interaction

Siili's Managed AI applies model routing, prompt and semantic caching, prompt compression, fit-for-purpose model selection (including small language models and open-source options where appropriate), context-window discipline, and continuous cost-per-outcome tracking by team and workflow. A recent comparable example. The right answer depends on the workload, which is why we evaluate it continuously rather than once.

Keep AI strategy, use case ownership, and domain knowledge in-house. Partner for the specialized operational capabilities needed to run AI reliably in production.

Building a mature internal AI operations capability typically takes 18–24 months under optimal conditions. It requires hiring specialists across cloud infrastructure, data engineering, AIOps, AgentOps, MLOps, LLMOps, security, governance, and continuous service management — in a market where those people are scarce and expensive. For most organizations, AI operations are not the differentiator. The use cases, data, and customer experience are.

A Managed AI partner like Siili gives you a faster, more flexible path. We take responsibility for the operational heavy lifting — infrastructure, monitoring, incident response, model evaluation, cost optimization, compliance evidence — while your team retains control of use cases, priorities, business outcomes, and the proprietary knowledge that no partner can replicate.

The most common engagement model is hybrid: your team owns the vision and the roadmap, Siili operates, monitors, secures, and continuously improves the AI solutions that deliver against it. We can also start narrow — covering only the parts of the stack you don't want to staff — and expand as the AI footprint grows.

A typical engagement has three phases:

  1. Production-readiness assessment. We map the current state of your AI initiatives — infrastructure, data, models, security, costs, ownership — and identify the gaps that would stop you from running this reliably at scale. Output is a concrete remediation plan and an SLA proposal.
  2. Foundations and transition. We close the readiness gaps, implement monitoring and incident-response processes, set up cost tracking, and take operational responsibility for the agreed scope. Existing teams stay involved; this is a shared-accountability model, not a hand-off.
  3. Run and improve. Day-to-day operations, monitoring, incident response, evaluations, model and prompt optimization, cost governance, and compliance reviews — all under defined service levels. Quarterly reviews align AI performance to evolving business objectives.

Scope is modular. You can start with a single critical AI solution and expand, or begin with co-managed operations alongside your internal team. We don't require you to lift-and-shift everything to start seeing value.

New opportunities to embed companion services into the daily flow of consumers living with intelligent assistants

 

We'll Help you turn the new era of consumer behavior into a business opportunity

Energy

Tap onto wider ecosystem of energy consumption

Banks

Become a Prosperity Companion for financial wellness

Telcos

Re-think digital storefront and expand servicing for both B2C and B2B

Retail

Manage life's operations and erase friction

Insurance

Transform from risk mitigation to wellness catalyst

Health

Companions for for patients and practitioners

New opportunities to embed services into the daily flow of intelligent assistants

We'll Help you turn the new era of consumer behavior into a business opportunity

Energy

Own the value chain of devices, end-to-end

Banks

Become a Prosperity Companion for financial wellness

Telcos

Re-think the digital storefront and integrate servicing features B2X

Retail

Manage life's operations and erase friction

Insurance

Transform from risk mitigation to wellness catalyst

Health

Harness self-reflection and wellness for predictive healthcare

We'll help you turn the new era of consumer behavior into a competitive advantage

Let's explore your role and opportunities in Companion Economy. For companies that owned the digitalization era, vertical leaders with optimized funnels and mature servicing models. The opportunity is equally transformative to Telco, hospitality banking, retail, FMCG to health and wellness.

A bank becomes a prosperity coach. An airline becomes a travel companion.

Companies we work with

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Ready to future-proof your digital operations?

Let’s turn continuous improvement into your competitive edge. Whether you’re looking to enhance performance, reduce risk, or unlock new value with AI-powered operations, we’re here to help. 

Toni Petäjämaa

Toni Petäjämaa

Director, Managed Services

+358 40 576 5315 toni.petajamaa@siili.com