AI Managed Services
Denebly operates and maintains AI systems after launch: monitoring models, catching drift, retraining them, fixing incidents and reporting performance every month. We run systems we built and systems another team built, so your AI stays accurate, current and under control long after go-live.
After launch we stay on: monitoring your systems, improving answers, updating models and adding new workflows as your business changes, for one predictable monthly fee instead of surprise bills.
What Are AI Managed Services?
AI managed services take operational responsibility for AI in production: watching it, keeping it accurate, fixing it when it breaks, improving it as usage grows, and reporting on it every month.
AI degrades after launch. Data drifts, products and policies change, providers update the models underneath, and usage costs climb. A system nobody watches gives worse answers every month while looking fine.
Building ends at launch. Managing starts there. The build team ships the system; the managed service keeps it accurate and under control for as long as it runs.
AI Managed Services Included
Eight services keep a live system accurate, watched and improving. Three start on day one of every engagement.
Model Monitoring
Accuracy, latency, error rate and usage watched against the onboarding baseline.
WatchDrift Detection and Retraining
Data drift and concept drift measured on a schedule. Models retrain before answers slip.
DriftPrompt and Answer-Quality Improvement
Prompts, guardrails and answer patterns tuned from real conversations, tested before release.
QualityKnowledge-Base Maintenance
Documents, help content and product data refreshed, so retrieval answers from current facts.
KnowledgeCost Watch
Usage and token spend tracked per system. Waste flagged in the monthly report.
CostIncident Response and Fixes
Alerts triaged by a named engineer. Fixes ship, or the change rolls back to the last good version.
ResponseModel and Provider Updates
Provider version changes tested against your evaluation set before they reach your users.
UpdatesMonthly Performance Reporting
One report every month: what we watched, what changed, what it cost, what comes next.
ReportTypes of AI Systems We Manage
AI managed services cover six kinds of live systems under one operating rhythm.
Customer Chatbots and AI Agents
Support assistants watched for answer quality and handoff failures, built under our AI chatbot development and AI agent development work, or taken over from another team.
RAG Knowledge Assistants
Assistants over your documents, watched for stale sources and retrieval misses. See our RAG development work.
Machine Learning Models
Forecasting, scoring and recommendation models watched for drift as seasons and markets move.
Workflow Automations
Automations watched for failed runs and breaks when connected tools change. Built under our AI automation services.
Computer Vision Systems
Inspection, scanning and counting systems watched for accuracy drops as lighting, products and cameras change.
Systems Built by Another Team
Any live system above, whoever built it. Takeover starts with the audit in our process, not a rebuild pitch.
Managed Service vs In-House Team vs Break-Fix Support
A live AI system needs watching, whoever does it. The three models differ in who watches, what it costs and what happens when something breaks.
Break-fix suits a minor internal tool. A customer-facing system needs watching, and an in-house hire pays off only at a scale most growing businesses have not reached.
Business Benefits of AI Managed Services
Six benefits follow when a live system is watched and improved on a rhythm.
Answers Stay Accurate
Drift is caught by monitoring and fixed by retraining, before customers see a wrong answer.
Problems Found Before Customers Report Them
Alerts fire on accuracy, latency and error signals, so the first report comes from us, not a complaint.
Running Costs Stay Visible and Controlled
Usage and token spend tracked per system every month, with waste flagged and right-sized.
Your Team Ships New Work
Your people build what is next instead of firefighting the last system shipped.
Every Change Tested Before Users See It
Model updates, prompt changes and retraining promotions pass an evaluation set before release.
One Predictable Monthly Fee
After launch we stay on for one predictable monthly fee instead of surprise bills, scoped in writing.
AI Managed Services Use Cases by Industry
AI managed services focus first on the systems that earn their keep daily. Those are the ones that drift first.
Retail and Ecommerce
Support chatbots whose answers must match current products, prices and policies. See our retail and ecommerce AI work.
SaaS
In-product assistants and support agents watched as features and docs change. See our SaaS AI work.
Legal and Home Services
Knowledge assistants over documents that change often: contracts, manuals, service guides.
Real Estate and Ecommerce Forecasting
Demand and scoring models that drift with seasons and markets. See our real estate AI work.
Dental Clinics and Home Services
Intake and booking automations watched for failed runs and missed follow-ups.
Daily Operations
Document and vision systems in daily use, watched for accuracy drops as inputs change.
Our AI Development Process: Four Steps From Idea to Live System
From first conversation to a live system, with a written plan at every stage.
Discovery & Roadmap
We audit the live system first: models, data, prompts, integrations and how it is watched today. You get a short, honest plan in writing: what we watch, what we fix first, and the results we measure.
Data & Design
We set baselines and instrumentation from real traffic: the metrics, thresholds, alerts and dashboards, designed around your real workflows, not around a demo.
Build & Integrate
We take over the running: monitoring, drift checks, retraining and fixes, the links to your CRM, store, helpdesk or booking tools watched, security built in, real outputs tested with your team.
Launch & Improve
A phased launch so daily operations never stop, then retraining, guardrails and new workflows driven by real usage.
Have a Project in Mind?
Tell us the problem. We will tell you honestly if AI can fix it, what it takes and what it costs.
Monitoring Stack and Tools
AI managed services read six signal groups to tell how a live system is doing, before its users have to.
Live answers sampled and scored against the onboarding evaluation set, so quality is a number, not a feeling.
Response times, failed calls and timeouts watched per system, with alerts off baseline.
Data drift and concept drift measured with statistical tests: Population Stability Index (PSI), the Kolmogorov-Smirnov test and Jensen-Shannon divergence.
A fixed set of real questions runs against the system after every change. Changes ship only when the set passes.
Standard operations tooling, such as Prometheus, Evidently, Arize, WhyLabs, LangSmith, Langfuse, Grafana and PagerDuty, worked inside your stack.
Request volume, token spend and cost per conversation tracked per system, so surprises surface in the report, not the invoice.
The method is baselines, thresholds, evaluation sets and a person who reads the signals.
Incident Response, Escalation and Human Oversight
When something breaks, the path is fixed in advance: automated alert, on-call engineer, senior review for production changes.
The Alert Fires
Monitoring crosses a threshold on accuracy, errors, latency or cost, and the alert reaches the on-call engineer with the failing signal.
A Named Engineer Triages
A person, not a ticket queue, checks whether users are affected and how badly. Severity sets the order of work.
Fix or Roll Back
The engineer fixes the cause, or rolls back to the last good version first. Users are not the test bed.
A Human Approves Production Changes
No retraining promotion, prompt change or model update reaches production on automation alone. A person approves it first.
Root Cause Goes in the Report
What broke, why, what changed and what prevents a repeat: written into the monthly performance report.
Coverage and response terms are agreed in writing per engagement, matched to how critical the system is, not published as headline numbers.
Security and Data Protection
Operating a system means holding its keys. In AI managed services, these controls come with the run, from day one.
Access, approvals and audit trails are the operating layer of governance. For the full program behind them, see our AI governance and security services.
How AI Managed Services Cost Is Scoped
The monthly fee follows scope, and scope follows six factors measured on your systems.
Every engagement is scoped and priced in writing before we start. No hourly surprises and no open-ended retainers for build work. A short AI readiness audit fixes the scope first, when your needs are unclear.
Onboarding and Operating Timeline
Onboarding runs on the same written-timeline discipline as our builds. Most focused builds reach production in 2 to 4 weeks. Larger work is phased, and every phase has a written timeline agreed before we start. A takeover runs audit, baselines, then the operating rhythm.
After onboarding, the rhythm is monthly: monitoring and fixes through the month, the report at month end, and agreed improvements from real usage in the next cycle.
Why Businesses Choose Denebly to Run Their AI
Denebly is a Dallas, Texas AI team with 8 years of experience, 100+ projects and 15+ services, including 10+ development projects. The team that builds AI also runs it, and we manage systems other teams built, starting from an audit, not a rebuild pitch.
- Projects delivered
- 100+ across AI, software and automation
- Experience
- 8 years building production systems
- Development projects
- 10+ full product builds
- Services
- 15+ AI and software services
- Based in
- Dallas, Texas, USA
- Ownership
- You own the systems, the data pipelines and the documentation, in full
Management connects with the systems handed over from our AI automation services and AI integration services. Take management back in-house later and the documentation is already yours: after launch we stay on, monitoring, improving and updating, for as long as you want us on.
Frequently Asked Questions About AI Managed Services
What are AI managed services?
AI managed services take operational responsibility for AI in production: watching it, keeping it accurate, fixing it when it breaks, improving it as usage grows, and reporting on it every month. Denebly runs systems we built and systems another team built.
What do AI managed services include?
AI managed services include model monitoring, drift detection and retraining, prompt and answer-quality improvement, knowledge-base maintenance, cost watch, incident response and fixes, model and provider updates, and monthly performance reporting.
What is model drift, and how do you detect it?
Model drift is the slow drop in accuracy after launch, as data, products and behaviour change away from what the system was built on. We detect it by measuring live inputs and outputs against the onboarding baseline, using statistical tests such as the Population Stability Index (PSI), the Kolmogorov-Smirnov test and Jensen-Shannon divergence, and by scoring sampled answers against a fixed evaluation set.
Can you manage AI systems another company built?
Yes. Takeover starts with a system audit: models, data, prompts, integrations and how the system is watched today. Most focused builds reach production in 2 to 4 weeks; larger work is phased, with every phase on a written timeline agreed before we start.
How do you control AI running costs?
We track usage and token spend per system, flag waste and runaway calls, right-size models to the work, and cache repeat answers where safe. The engagement itself is scoped and priced in writing before we start. No hourly surprises, no open-ended retainers for build work.
What does the monthly performance report include?
The report covers accuracy and answer quality, latency and error rates, drift findings and retraining done, incidents and root causes, usage and running costs, and next month’s agreed improvements. It is the full record of what we watched, what changed and why.
What happens when an AI system fails?
An alert fires with the failing signal attached, a named engineer triages it, and we fix it or roll back to the last good version. No change reaches production on automation alone: a person approves retraining promotions and prompt changes. The root cause goes into the monthly report.
What SLA do you offer?
Coverage and response terms are agreed in writing per engagement, matched to how critical the system is. A customer-facing system gets stronger terms than an internal tool. We write in terms we can keep, not headline numbers.
Will our business data stay private?
Yes. We build with access controls from day one, offer private deployment where your data never leaves your environment, and never train public models on your business data.
Tell Us What You Want to Build
Share a few details about your project and we will come back with honest next steps: what is possible, what it takes, and a written scope and price if you want to move ahead. No pressure and no obligation.
- ✓Reply within one business day
- ✓Scoped and priced in writing before we build
- ✓You own everything we build, in full
Prefer to talk first? Call or WhatsApp +1 (214) 896-2906 or email hello@denebly.com
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