For AI already live, not AI still on a whiteboard
Managed AI Services
A model that works in a demo is a different asset from one running unattended against real customers, because nobody notices drift until a decision goes wrong. This practice takes ownership of live models after they ship, watching cost, output quality and behaviour change, and acting before a customer does. It is built for teams who shipped an AI feature and then discovered nobody owned what happened next.
What a managed model actually needs
Ownership, monitoring and the incident path a live model needs after launch.
80 secSample frame · video pending01Before you hand a model over
A model needs ownership before it needs more features
Shipping a model is not the same as running one, and the gap between the two is where cost overruns and silent accuracy loss actually happen. Four checks settle whether a model needs a named operator now or can wait.
Is anyone watching the model daily?
A model with nobody checking its output drift or cost curve is already a liability, whether or not it has failed yet.
Do you know this month's inference spend?
Inference cost creeps upward as usage grows, and few teams notice until a bill arrives that nobody can explain.
What happens when the model is wrong?
An incident path for a bad output has to exist before the first customer hits one, not after.
Is retraining tied to a real trigger?
Retraining on a schedule ignores drift; retraining on measured signal catches it while it is still cheap to fix.
| Unmanaged | Managed | |
|---|---|---|
| Ownership | Diffuse across teams | One named operator |
| Drift detection | Noticed after complaints | Measured on a schedule |
| Cost visibility | Discovered on the invoice | Tracked per model, per day |
| Incident response | Improvised | A rehearsed path |
| Right for | A single low-stakes prototype | Anything customers depend on |
02Agreed before the handover
The ledger that keeps operation honest
Every managed AI engagement fixes what moves into our operation, what we watch and change, what comes back to you, and what authority never leaves your hands regardless of engagement shape.
- Your side
You bring
- The model already running in production
- Whatever logs or metrics already exist
- The incident history so far, if any
- Our side
We build
- Monitoring instrumented for cost, drift and accuracy
- Alert thresholds tied to measured behaviour
- An incident path with a named owner
- Your side
You receive
- A dashboard your team can read daily
- A tested alert and rollback path
- A record of what the model actually costs
Never leaves you
- All decisions about the model's future
- Full ownership of the underlying data
- Final say on any retraining or rollback
Where the line sitsWe instrument and watch. Every decision about what the model should do next stays with your team, because operation is not authorship.
03From handover to steady state
Four stages, each closing on a working system
Operating a model well is a repeatable process, not an ad hoc habit, so each stage of the handover closes with something you can check yourself.
Instrument the live model
Cost, latency, accuracy and drift are measured against your production traffic before anything about the model changes.
A monitored baselineSet the retraining and alert triggers
Alerts fire on measured drift, not on a calendar, so retraining happens when the model actually needs it.
Thresholds with teethBuild the incident path
A bad output has an owner, a rollback and a communication step decided before the first real incident.
A rehearsed responseHand over the operating dashboard
Cost, drift and incident history move to a dashboard your team reads daily, so the model stays visible after we step back.
A team that can run it
04What actually drives the number
Four drivers behind the operating fee
Running a model well is priced against what it actually needs watched, not a flat retainer, because these drivers change the effort by multiples.
Model count and traffic volume
Watching one model on modest traffic costs less than watching a fleet under real customer load.
How tightly drift must be caught
Regulated or high-stakes decisions need tighter thresholds and faster response than a low-stakes recommendation feature.
Retraining frequency and data pipeline
A model that needs frequent retraining carries more pipeline work than one that stays accurate for months.
Incident severity your business tolerates
A wrong product recommendation costs less to get wrong than a wrong compliance decision, and depth of operation follows that line.
05Evidence and proof
Models that stayed accurate after everyone stopped watching them by hand.
Managed AI proof is measured in incidents avoided and drift caught early, not in demo accuracy. Each record stays a placeholder until the named client signs off in writing.
Explore case studiesA fintech's fraud model finally had a name attached to its uptime.
Vela Ledger's fraud-scoring model ran without an assigned owner until drift went unnoticed for weeks. Managed monitoring gave the model a daily accuracy check and an alert path.
- Drift detection time
- Same-day
- Unowned incidents since handover
- Zero
An industrial defect-detection model stopped drifting in silence.
Nexa Foundry's vision model flagged defects accurately at launch, then slowly lost precision as the production line changed. Retraining triggers caught the decline before scrap rates rose.
- Precision recovered
- Within one cycle
- Scrap-rate impact
- Contained
We knew our cost per inference and our drift trend every morning, not just when something broke.
Sample · pending client approval
06Read this first
When managed AI is the wrong spend
Selling ongoing operation to a team running one experimental model would cost more than it earns in trust, so the exits are marked before the pitch. When the engagement is justified, monitoring runs on your existing cloud and model stack, not a new platform we impose.
You have one model still in testing
Prove the model works before paying to operate it at production discipline. A pilot does not need a named operator yet.
- AI Consulting
The model changes weekly
Fast-moving experimentation needs iteration speed more than operating discipline. Revisit management once the model settles into a stable version.
- MLOps services
Your team should build this capability itself
If your engineers can own monitoring and just need the pipeline discipline, MLOps sets up the tooling without adding a permanent operator.
| Shape | Best when | You direct | We carry |
|---|---|---|---|
| Scoped operation | One or two models need daily watching | Priorities and thresholds | Monitoring and incident response |
| Delivery pod | Models multiply across products | Roadmap and risk tolerance | Ongoing operation, releases, retraining |
| Team extension | Your leads want to own operation | Standards and architecture | Named engineers embedded in your operation |
07Straight answers
Asked before a model gets handed over.
01How is managed AI different from MLOps?
MLOps builds the pipelines, versioning and deployment tooling a model lifecycle needs. Managed AI is the ongoing operation of a model already live, using that tooling to watch cost, drift and quality daily rather than building the infrastructure itself.
02Do you take over models another team built?
Yes, starting with an assessment of the model's current accuracy, drift history and cost before any monitoring changes. Most handovers begin with instrumentation, not a rebuild.
03What happens if a model starts producing bad output?
The incident path set up during handover assigns an owner, a rollback option and a communication step. A bad output gets caught and corrected on a known timeline, not discovered by a customer first.
04Can managed AI cover multiple models across products?
Yes, and most engagements do, because a fleet of models is where an unowned drift problem is most expensive. Pricing scales with model count and how tightly each one must be watched.
05What tools do you use to monitor models?
Monitoring is chosen against your existing model stack and cloud provider rather than a fixed platform we impose. Open-source and cloud-native observability tools are the common starting point, wired to your alerting.
06How quickly can a model be brought under management?
Instrumentation for a single model typically starts within the first engagement cycle, once baseline accuracy and cost are measured. Fleets of models take longer, scoped against how many need daily watching.
07Who owns the model's intellectual property once you manage it?
You do, entirely and without exception. Managed AI covers operation, not ownership, and every decision about the model's future stays with your team throughout the engagement.