AI systems don't fail loudly. A model version changes, a prompt that worked last quarter stops working, knowledge goes stale, token costs creep up — and nobody notices until customers do.
Codersarts AI Managed Services take operational accountability for your AI: we monitor it, maintain it, improve it, secure it and report on it every month.
Any platform. Any model provider. Built by us or by anyone else.
Get a Managed AI Plan → | Book a Free AI System Review →
Platform-agnostic: OpenAI, Claude, Gemini, open-source, Copilot Studio, Bedrock, Vertex, n8n, custom code
Written SLAs for uptime, response time and AI quality — not just "support"
Flexible billing: flat monthly per-workload plans, outcome-based pricing, or dedicated AI ops pods
No lock-in: you keep full ownership of code, prompts, data and accounts
What Are AI Managed Services?
AI managed services (also called managed AI services) is an ongoing engagement in which a provider takes operational accountability for an organization's production AI — monitoring, maintenance, model and prompt updates, drift detection, evaluations, cost control, security and governance reporting — against agreed service levels, billed monthly.
You own the goal. We own the operation.
AI managed services are different from AI development. Development delivers a system once; managed services keep that system accurate, safe and cost-efficient every month after it goes live.
Need the system built first? See Codersarts AI development services or build.codersarts.com for fixed-price builds.
Why Production AI Needs Active Management
Quality drifts silently — data patterns shift, knowledge goes stale and answers degrade without throwing errors.
Vendors ship constantly — model versions, APIs and SDKs change, breaking prompts, tools and integrations.
Costs grow with usage — token spend, inference and infrastructure costs rise faster than value if nobody watches them.
Agents take real actions — they call APIs, write to systems and spend money, so permissions, guardrails and audit trails need ongoing review.
Regulation now requires it — frameworks like the EU AI Act, NIST AI RMF and ISO/IEC 42001 expect continuous monitoring, not one-time approval.
Ownership disappears after launch — the builder moves on and the system is left running with no one accountable.
Three Levels of AI Managed Services
Level | What we manage | Typical buyer | Billing |
1. Managed AI Systems | The technology — agents, chatbots, RAG, LLM apps, ML models, automations | CTOs, AI and engineering leads | Flat monthly per workload or tier |
2. Managed AI Business Functions | The work — support, documents, outbound, content, back-office, run with AI + human review | COOs, ops, support and sales leaders | Per outcome (resolution, document, meeting) or base + usage |
3. Managed AI Program & Risk | The organization's AI estate — governance, security, cost, evals, adoption | CIOs, CISOs, compliance, AI CoE | Monthly program retainer |
Start at any level. Most clients start with one system and expand to functions and program management as AI use grows.
Level 1 — Managed AI Systems
Service | What we manage | Learn more |
Managed AI Agents (AgentOps) | Operate, monitor and govern production AI agents — single agents and multi-agent systems. Monitoring, tool-call failure tracking, permission reviews, prompt and instruction tuning, escalation handling and change control. | |
Managed AI Chatbots & Assistants | Keep customer-facing and internal assistants accurate, on-brand and connected to current knowledge across web, WhatsApp, Slack, Teams and in-app channels. | |
Managed AI Voice Agents | Run inbound and outbound voice agents — call quality review, script and intent tuning, latency, telephony integrations and per-minute cost tracking. | |
Managed RAG & Knowledge Systems | Refresh and re-index knowledge sources, tune chunking and retrieval, track answer accuracy and citation quality, and manage vector databases. | |
Managed LLMOps | Prompt registry and versioning, model upgrades and provider migrations, regression checks before every change, latency and uptime, model routing and fallbacks. | |
Managed MLOps & ML Models | Data, model and concept drift monitoring, scheduled and drift-triggered retraining, model registry, staged rollouts and rollback for predictive, recommendation, forecasting and classification models. | |
Managed Computer Vision & Document AI Models | Monitor and retrain OCR, extraction, classification, detection and inspection models as documents, layouts, cameras and conditions change. | |
Managed AI Automations & Workflows | Operate n8n, Make, Zapier, Power Automate and custom AI workflows — failure alerts, retries, credential and API updates, and workflow optimization. | |
Managed MCP Servers & AI Integrations | Run and secure MCP servers, tool connectors and AI integrations with CRMs, ERPs, helpdesks and databases — versioning, auth, rate limits and access audits. | |
Managed AI Features in SaaS Products | For software companies: we operate the AI features inside your product — copilots, AI search, summarization, generation — so your engineering team keeps shipping roadmap work. |
Building a new agent, RAG system or MCP server? Start with Codersarts AI engineering services. Need a one-off fix? Browse Codersarts Tasks or Codersarts Support.
Level 2 — Managed AI Business Functions
AI plus a human review layer, run by Codersarts and billed against outcomes.
Service | What we run | Learn more |
Managed AI Customer Support Desk | AI agents resolve routine chat, email and ticket volume; trained reviewers handle escalations and QA. Priced per resolution or per ticket. | |
Managed Document Processing (IDP) | Invoices, forms, contracts, claims, KYC and records extracted by AI, with human-in-the-loop validation for low-confidence fields and exceptions. Priced per document or per page. Powered by DocProcessing360. | |
Managed AI Sales Development (AI SDR) | Prospect research, personalized multi-channel outreach, reply handling and meeting booking run by AI with human oversight. Priced monthly or per qualified meeting. | |
Managed Lead Qualification & Appointment Booking | Inbound lead response, qualification, scheduling and intake across web, chat and voice — around the clock. | |
Managed AI Content Operations | AI-assisted content production with editorial review — blogs, product content, SEO and AI-search (GEO) content, documentation and social. | |
Managed Data Labeling & Annotation | Ongoing labeling, review and quality assurance for text, image, video, audio and code data that your models depend on. For AI labs, see AI Training Data. | |
Managed AI Back-Office Operations | Data entry, reconciliation, catalog and listing management, order and returns processing, and research tasks run with AI and human QA. | |
Managed AI IT & HR Helpdesk | Internal copilots for password resets, policy questions, onboarding and SOP answers in Slack and Teams, with escalation to your team. | |
Managed AI Technical Screening | AI-assisted candidate screening and technical assessments with engineer review, for hiring teams. Live expert interviews via Codersarts Experts. |
Level 3 — Managed AI Program & Risk
Service | What we deliver | Learn more |
Managed AI Monitoring & Evaluations | Continuous eval suites per workflow, LLM-as-judge and human scoring, regression testing, quality dashboards and alerting. | |
Managed AI Governance & Compliance | AI system inventory, risk classification, documentation, human-oversight records, post-market monitoring and audit evidence mapped to the EU AI Act, NIST AI RMF and ISO/IEC 42001 — delivered monthly. | |
Managed AI Security & Continuous Red Teaming | Prompt-injection and jailbreak testing, data-leakage checks, agent permission audits, guardrail tuning and AI incident response — run continuously, not once a year. | |
Managed AI Cost Optimization (AI FinOps) | Token and inference spend tracking, cost per run and per outcome, caching, model routing and right-sizing, with monthly savings reports. | |
Shadow AI Discovery & AI Inventory | Find the AI tools, agents and API keys in use across your organization, assess risk and bring them under policy. | |
Managed AI Adoption & Copilot Enablement | Rollout support, usage analytics, prompt libraries and team training for Microsoft 365 Copilot, ChatGPT Enterprise, Claude and Gemini workspaces. Team training via Codersarts Labs. | |
Model Upgrade & Provider Migration Management | Tested migrations when models are deprecated or a better model ships — eval comparison, prompt adaptation, staged rollout and rollback plans. | |
AI Center of Excellence as a Service | A fractional AI operations lead plus engineers who run your AI roadmap, standards, vendor reviews and quarterly business reviews. |
Need a one-time review first? See Codersarts Technology Audit.
Managed AI Infrastructure
Inference hosting & GPU operations — managed hosting for open-source and fine-tuned models, autoscaling and capacity planning.
Vector database operations — Pinecone, Weaviate, Qdrant, Milvus and pgvector: indexing, scaling, backups.
LLM gateway & routing — fallbacks, rate limits, key management and multi-provider routing.
Observability stack — setup and operation of Langfuse, LangSmith, Arize Phoenix, Helicone or Braintrust.
Backup, disaster recovery & environment management — staging, production and rollback environments for AI systems.
What's Included Every Month
Area | What we do |
Operate | Run AI systems and workflows day to day, handle exceptions and escalations |
Monitor | Uptime, latency, error rates, tool-call failures, answer quality, data and model drift |
Maintain | Prompt and instruction updates, knowledge refresh, bug fixes, API, SDK and dependency changes |
Improve | Eval-driven tuning, retraining and fine-tuning, retrieval improvements, new intents and use cases |
Optimize | Token, inference and infrastructure cost; caching; model routing; right-sizing |
Secure | Access and permission reviews, guardrails, red-team checks, PII handling, patching |
Govern | Change log, audit trail, model and prompt version history, compliance evidence |
Report | Monthly operations report: SLA compliance, quality, usage, outcomes, cost and recommendations |
Written SLAs — Not Just "Support"
Every plan comes with service levels in writing:
SLA | What it covers |
Availability | Uptime target for the AI service and its integrations |
Incident response | Response and resolution targets by priority (P1–P4) |
AI quality | Accuracy, resolution or extraction thresholds measured on an agreed eval set |
Drift triggers | Thresholds that trigger investigation, retraining or prompt changes |
Change control | Regression checks and tested rollback before any production change |
Cost guardrails | Budget alerts and per-run / per-outcome cost tracking |
Reporting cadence | Monthly operations report and quarterly business review |
Plans & Pricing Models
Per-Workload Plans
| Starter | Growth | Business | Enterprise |
Best for | One AI system | Systems that must keep improving | Business-critical AI | Multiple systems, regulated environments |
Price | From $499 / workload / month | From $1,499 / month | From $3,499 / month | Custom |
Monitoring & alerting | ✓ | ✓ | ✓ | ✓ |
Fixes, API & dependency updates | ✓ | ✓ | ✓ | ✓ |
Monthly report | ✓ | ✓ | ✓ | ✓ |
Monthly tuning & knowledge refresh | — | ✓ | ✓ | ✓ |
Eval suite & regression checks | — | ✓ | ✓ | ✓ |
Cost optimization reports | — | ✓ | ✓ | ✓ |
Security & permission reviews | — | — | ✓ | ✓ |
Governance & compliance evidence | — | — | ✓ | ✓ |
Named AI operations engineer | — | — | ✓ | ✓ |
P1 response | 1 business day | 8 hours | 4 hours | 1 hour, 24/7 |
Quarterly business review | — | — | ✓ | ✓ |
Outcome-Based Pricing
For managed business functions, pay for results: per resolved ticket, per processed document, per qualified meeting or per completed task — with an optional base fee for guaranteed capacity.
Dedicated AI Ops Pod
A dedicated team of AI operations engineers and reviewers for organizations running many AI systems, billed monthly. Prefer engineers inside your own team? See Hire Developers.
Free 14-Day Pilot Review
Start with a no-cost review of one AI system: we instrument it, measure quality and cost, and share a written findings report before you commit.
Month-to-month plans. No setup fee on Starter and Growth. Cancel with 30 days' notice.
Get Pricing for Your AI System →
How It Works
1. AI System Review
We audit architecture, prompts, data sources, integrations, costs, security and current quality, and set a measured baseline.
2. SLA & Scope Agreement
We agree what's in scope, the eval set, quality thresholds, response times, reporting and pricing model.
3. Onboarding & Instrumentation
We set up monitoring, alerting, eval pipelines and access, and write runbooks and documentation for every in-scope system.
4. Run, Improve & Report
We operate the system, ship improvements through change control, and report monthly against the SLAs.
5. Quarterly Review & Roadmap
We review outcomes and cost with your team and recommend the next systems, functions or use cases to automate.
AI System Takeover — Built by Someone Else? We'll Run It
We take over AI systems built by other agencies, freelancers, in-house teams, low-code tools and AI app builders (Bolt, Lovable, Cursor, Replit, Base44 and similar).
Audit — document what exists, what's broken and what's risky.
Stabilize — fix critical issues, secure keys and access, add monitoring.
Run — move the system onto a managed plan with SLAs.
No rebuild required unless the audit shows it's cheaper than fixing.
Request an AI System Takeover → | Rescue a Broken AI App →
White-Label AI Managed Services for Agencies & MSPs
Agencies, consultancies, IT MSPs and software resellers can offer AI managed services under their own brand. Codersarts delivers monitoring, maintenance, tuning and reporting behind the scenes while you own the client relationship and pricing.
White-label reports and communications
Multi-client operations under one partner agreement
Partner pricing and recurring margin
Overflow and after-hours AI operations coverage
Platforms & Technologies We Manage
Category | Platforms |
Model providers | OpenAI, Anthropic Claude, Google Gemini, Mistral, Meta Llama, DeepSeek, Qwen and other open-source models |
Cloud AI platforms | Amazon Bedrock & SageMaker, Azure AI Foundry & Azure OpenAI, Google Vertex AI |
Agent platforms | Microsoft Copilot Studio, Bedrock Agents, OpenAI Assistants & custom GPTs, LangGraph, CrewAI, AutoGen, custom agents |
Frameworks | LangChain, LlamaIndex, Haystack, DSPy |
Automation | n8n, Make, Zapier, Power Automate |
Vector databases | Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector |
Observability & evals | Langfuse, LangSmith, Arize Phoenix, Helicone, Braintrust, Promptfoo, DeepEval |
Voice | Twilio, Vapi, Retell, ElevenLabs, Deepgram |
Business systems | Salesforce, HubSpot, Zendesk, Intercom, Freshdesk, ServiceNow, Slack, Microsoft Teams, Google Workspace |
Industries
Industry | Managed AI use cases |
Healthcare & wellness | Patient intake, scheduling, document processing, with PHI-aware handling |
Financial services & insurance | KYC, claims, document extraction, compliance monitoring |
E-commerce & retail | Support, catalog, returns, product content |
SaaS & technology | In-product AI features, support, developer documentation |
Legal & professional services | Contract review, research, knowledge assistants |
Real estate & construction | Lead qualification, document processing, project knowledge bases |
Education & edtech | Tutoring assistants, content operations, student support |
Logistics & manufacturing | Document workflows, inspection models, forecasting |
See AI solutions by industry →
Who This Is For
Startups that shipped an AI product and need it reliable without hiring an AI ops team.
SMBs running chatbots, automations or document workflows that no one maintains in-house.
Mid-market companies scaling from one AI pilot to several production systems.
Enterprises that need governance, security, cost control and SLAs across an AI estate.
SaaS companies that want their engineers on roadmap work, not AI firefighting.
Agencies & MSPs that want to resell AI operations under their own brand.
Why Codersarts
| Codersarts | In-house AI ops team | Platform-specific providers | Large consultancies | Freelancers |
Works across any model, cloud and builder | ✓ | Depends on hires | One platform only | ✓ | Varies |
Takes over systems built by others | ✓ | — | Limited | Rarely for small systems | Varies |
Written AI-quality SLAs | ✓ | Internal only | Some | ✓ | Rarely |
Systems + business functions + governance in one partner | ✓ | Multiple teams | Systems only | ✓ | — |
Starts at one workload, month to month | ✓ | Full salaries | Some | Large minimums | ✓ |
Engineering depth to fix and rebuild, not just monitor | ✓ | ✓ | Limited | ✓ | Varies |
You keep full IP and account ownership | ✓ | ✓ | Varies | ✓ | Varies |
How to Choose an AI Managed Services Provider — 10 Questions to Ask
Will you commit to uptime, response time and AI-quality SLAs in writing?
How do you measure quality — what eval set, how often, who scores it?
What thresholds trigger retraining, prompt changes or escalation?
How are changes tested and rolled back before they reach production?
Can you manage systems on any model provider, or only one platform?
Will you take over a system you didn't build?
How do you track and reduce AI cost per run or per outcome?
How do you handle security reviews, agent permissions and prompt-injection risk?
What compliance evidence do you produce, and how often?
Who owns the code, prompts, data and accounts if we leave?
Codersarts answers all ten in writing before you sign.
Frequently Asked Questions
What are AI managed services?
AI managed services are an ongoing engagement where a provider takes operational responsibility for your production AI — monitoring, maintenance, updates, drift detection, evaluations, cost control, security and governance — under agreed service levels, billed monthly.
Are "managed AI services" and "AI managed services" the same?
Yes. Both describe a provider running your AI after launch under contract, rather than building it once and handing it over.
How is this different from MLOps, LLMOps or AgentOps?
MLOps, LLMOps and AgentOps are the disciplines of running models, LLM applications and agents in production. AI managed services deliver those disciplines as a service, with a provider accountable for the outcome.
How is this different from AI development or AI support?
Development delivers a system once. Support fixes a specific issue. Managed services keep the system running, accurate and cost-efficient every month under SLAs.
How much do AI managed services cost?
Per-workload plans start from $499 per month. Business-critical and multi-system plans are priced by scope, response times and compliance needs. Managed business functions can be priced per outcome — per resolution, per document or per meeting.
What does an AI managed services SLA cover?
Availability, incident response by priority, AI-quality thresholds on an agreed eval set, drift triggers, change control and rollback, cost guardrails and reporting cadence.
Can you manage an AI system you didn't build?
Yes. We audit, stabilize and document it first, then run it under a managed plan.
Which platforms and models do you support?
All major model providers, cloud AI platforms, agent frameworks, automation tools and vector databases, including OpenAI, Claude, Gemini, open-source models, Copilot Studio, Bedrock, Vertex AI, LangChain and n8n.
Do we keep ownership of our AI system and data?
Yes. You own your code, prompts, models, data and accounts. We operate within the access you grant and hand everything back if you leave.
Can you help with EU AI Act or other compliance requirements?
Yes. Managed governance includes AI inventory, risk classification, documentation, monitoring records and audit evidence mapped to the EU AI Act, NIST AI RMF and ISO/IEC 42001. We support your compliance team; we don't replace legal advice.
Do you offer outcome-based pricing?
Yes, for managed business functions such as customer support, document processing, lead qualification and outbound.
How quickly can you start?
Most engagements begin with a review in the first week, with onboarding and handover in the following one to two weeks.
What's not included?
Large new features and new AI systems are scoped separately as development work. Third-party costs — model API usage, cloud hosting and SaaS licenses — are billed by those providers to you unless agreed otherwise.
Can we cancel?
Yes. Plans are month to month with 30 days' notice.
Related Codersarts Services
Service | When to use it |
Enterprise AI development — RAG engineering, multi-agent orchestration, LLM fine-tuning, MLOps | |
Human coding data, evaluations and RLHF ratings for AI labs | |
Fixed-price software, MVPs, modules and components | |
Fix broken apps built with Bolt, Lovable, Cursor, Replit or Base44 | |
One-time code, architecture and AI system audits | |
Production, application, cloud, DevOps and AI support | |
Fixed-scope fixes, setups and AI/ML tasks | |
Dedicated, part-time or full-time developers in your team | |
Expert calls, technical interviews and product reviews | |
AI-powered intelligent document processing platform | |
AI and engineering training for teams |
Keep Your AI Working — Long After Launch
Tell us what AI you're running and we'll show you how to keep it reliable, accurate, secure and cost-efficient.
Talk to Codersarts → | Book a Free AI System Review →