How Vervali Load Tests Shopify Stores Before Black Friday
How Vervali load tests Shopify and Shopify Plus stores before Black Friday: the Shopify rules, what gets tested, when to start and what it costs.
AI Model Monitoring
and Retraining Services
Vervali monitors models already running in production: accuracy against a held-out set you still control, drift in the inputs and in the relationship between inputs and outcomes, alerts that a named owner will actually see, and retraining when that is warranted rather than reflexive. Delivery is from India with a 9am to 1pm Eastern overlap.
150+ clients
450+ projects
275+ engineers
15+ years
ISO/IEC 17025:2017Accredited testing laboratory
CMMI Maturity Level 3The process is written down and repeats
ISO 9001:2015Quality management
ISO/IEC 27001Information security
Accuracy, Drift and Retraining for Models Already Live
What is watched once a model is in production, and who sees it.
Accuracy That Still Means Something
The live score against a held-out set you still control, not the training set. A high sandbox score that collapsed on live traffic is the usual miss.
Calibration
Whether a 0.9 score is actually right nine times in ten, reported as a gap. We do not invent an accuracy figure we cannot evidence.
Drift in Inputs and Outcomes
Inputs that no longer match training, or a changed link between inputs and outcomes. Method is on data drift testing.
Fairness After Launch
Fairness that held at launch and failed later sits in the same watch, reported by group and subgroup as traffic changes.
Alerts a Named Owner Sees
Which inputs and outputs to watch, what threshold fires and who is on the call. An alert nobody owns is a dashboard, not monitoring.
A Watch List Written With Your ML Owner
We agree the list with your ML owner before the first week, so every alert has a threshold, an owner and a next step.
Need drift caught before the model quietly fails in production?
Free Model ReviewWhen Retraining Is Warranted, Not Reflexive
Retraining because a calendar said so is how teams spend a week on a model that had not moved.
A Threshold You Named
Retraining starts when a threshold you named is crossed, on data you still control, not on a calendar date.
The Same Held-Out Checks
A retrained model faces the same held-out checks as the first validation. The first validation is on AI and machine learning testing.
Rollback When the New Version Is Worse
A retrained model that loses on the held-out set does not ship. Versioning and rollback are in the quote.
AWS and Azure are the platforms Vervali builds on. Building the model is on AI powered business solutions.
Not sure retraining is the next step? A QA lead can look at one model.
Book a CallWhat We Need to Start, and What You Get Back
The model or an endpoint, a held-out set you still control, the intended use and the decision the model supports.
Endpoint or Scored File
Most production models are watched as a black box. Training code is not a prerequisite, and we say in the design if a check needs weights.
Thresholds You Named
What should fire and what should not. A single global number hides a slice, which is why we watch by group and by segment.
Evidence a Board Can Read
Findings come with the evidence a model review board will ask for. We do not copy production customer records into a test environment.
Proof From Delivery
Three Vervali AI builds, each saying what was validated and what changed. They were not monitoring-only jobs.
Financial services · KYC deepfake detection
Manual review was the bottleneck on identity. A detection model for know-your-customer cut that load: 70% less manual review at 75% detection accuracy. That pack has to keep holding after launch.
70% less manual review 75% detection accuracyAI calling engine · lead qualification
An AI calling engine, built and validated by Vervali. After: 35% more qualified leads and a 28% shorter sales cycle. Live traffic is where that check continues.
35% more qualified leads 28% shorter sales cycleOperations · real-time SOP image detection
A real-time image detection system for standard operating procedure compliance, built and validated by Vervali. Floor conditions move, and monitoring is how you see that.
How an Engagement Starts
From one production model to a watch with owners and thresholds.
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01
Share the Model
Tell us the model or endpoint, the intended use, the decision it supports and the held-out set you still control.
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02
Agree the Watch
We write the inputs, outputs, thresholds and owners with your ML owner before the first week.
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03
Start With a Model Review
A free review of one production model, for one hour, is the first look. Most engagements land at $25 to $49 an hour blended, and ongoing monitoring is quoted separately.
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04
Watch, Alert and Retrain
Drift is watched, alerts reach a named owner and retraining happens when it is warranted, 9am to 1pm Eastern with delivery from India.
Frequently Asked Questions
Here the model is already live: accuracy against a held-out set you still control, drift in inputs and outcomes, alerts a named owner will see, and retraining when it is warranted. The first validation is on AI and machine learning testing, and findings come with evidence a review board can read.
Yes. Most vendor models are watched as a black box: an endpoint or a scored file, a held-out set you control, the intended use and the decision the model supports. Training code is not usually required. We report how the model behaves on your live traffic, not how the vendor scored it.
No. Supervised classifiers and rankers, unsupervised systems, and generative and large language model systems are in scope for production monitoring. The checks change with the type, and if a type is outside what we can evidence we say so before we quote. A free model review is the first look.
We keep an evaluation set from your own traffic, stored so a prompt or model change cannot silently alter output. Hallucination, grounding, safety and refusal continue after launch, and retrieval is watched separately from generation. The first evaluation pack is on AI and machine learning testing.
We test group and subgroup performance on live traffic, with more than one fairness measure, because fairness that held at training can fail later. Method detail is on AI bias and explainability testing. A free model review shows which groups to watch first.
Validation is a point in time, and drift is the world moving after it: inputs that no longer match training, or a changed link between inputs and outcomes. We say which inputs and outputs to monitor, what threshold should fire and who owns the alert, then retrain only when warranted.
Judge a vendor on whether they watch held-out data you control, whether drift and retraining sit with accuracy, whether findings are evidence a review board can read, and whether they are independent of the team that trained the model. Vervali is an ISO/IEC 17025 accredited testing laboratory.
Most engagements land at $25 to $49 an hour blended. Work is scoped to the model, the data and the watch you need, then quoted before we start. Ongoing monitoring is billed separately from a one-off review, and a free review of one production model shows the surface first.
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