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.
Data Drift Testing for
Production AI Models
Vervali detects input and prediction drift before accuracy falls, with thresholds, alerting and a retraining trigger. Every alert has a named owner, every threshold is written with your ML owner, and retraining happens when it is warranted rather than on a calendar. 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
Input and Prediction Drift, Caught Before Accuracy Falls
What is watched on a model that is already live, and who sees it.
Input and Feature Drift
Whether inputs have stopped looking like training: moved distributions, missing values that were rare at launch and fields that quietly changed type.
Prediction and Concept Drift
Whether the link between inputs and outcomes has changed. The model still scores and the world has moved, which is the silent fail.
A Held-Out Baseline You Control
Output is watched against a held-out set you still own, not only a live score that can hide the shift.
Thresholds You Named
What should fire and what should not. A single global number hides a slice, so we write the list with your ML owner.
Alerts a Named Owner Sees
An alert nobody owns is a dashboard. Every alert has a threshold, an owner and a next step before the first week.
A Retraining Trigger, Not a Calendar
Retraining happens after a threshold you named, on data you still control, with the same held-out checks as the first validation.
Need input and prediction drift caught before accuracy falls?
Free Model ReviewHow the Watch Is Built
AWS and Azure are the platforms Vervali builds and operates on.
A Baseline You Still Control
Training is not the proof. A held-out set you still own is, and if the sandbox score and the live score disagree, the live score wins.
Thresholds Written First
We write what should fire with your ML owner before the first week, and name the features and segments to watch.
Rollback When a New Version Is Worse
A retrained model that loses on the held-out set does not ship. Versioning and rollback are in the quote.
We do not copy production customer records into a test environment without an agreed mask or synthetic set.
Did a production model already drift without anyone noticing? A QA lead can look.
Book a CallThe First Validation, and the Ongoing Loop
Drift is one part of keeping a model honest. These are the neighbouring packs.
First Validation
Accuracy, calibration, bias, explainability and LLM output tested before launch, with evidence a board can read. See AI and machine learning testing.
Fairness After Launch
Fairness that held at launch and failed later, reported by group and subgroup as traffic changes. See AI bias and explainability testing.
Monitoring and Retraining
The ongoing loop on accuracy, alerts and retraining once the model is live, with a named owner. See AI model monitoring and retraining.
Building the Model
New document, forecasting and decision-support models, built and validated from day one. See AI powered business solutions.
Proof From Delivery, Models That Had to Keep Holding
Three Vervali AI builds, each saying what was validated and what changed. They were not drift-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, which is the drift question.
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 drift is how you see that.
How an Engagement Starts
From one live model to a watch with owners and thresholds.
-
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.
-
02
Agree the Thresholds
We write the features, segments, thresholds and owners with your ML owner before the first week.
-
03
Start With a Model Review
A free review of one model, for one hour, is the first look. Most engagements land at $25 to $49 an hour blended, and ongoing drift monitoring is quoted separately.
-
04
Watch and Retrain
Drift is watched, alerts reach a named owner and retraining happens when warranted, 9am to 1pm Eastern with delivery from India.
Frequently Asked Questions
Detecting input and prediction drift on a live model before accuracy falls, with thresholds, alerting and a retraining trigger. The first validation is on AI and machine learning testing, and the ongoing loop is on AI model monitoring and retraining.
Data drift is the inputs no longer looking like training. Concept drift is the link between inputs and outcomes changing, so the model still scores but is wrong more often. We watch both, and compare output with a held-out set you control rather than a live score alone.
We compare live input and output distributions with the training baseline and a held-out set, on the features and segments you name. Each threshold has an owner and a next step. A dashboard of charts with no owner is not a drift watch, so every alert reaches a named person.
When a threshold you named is crossed, not because a calendar said so. Retraining on a schedule can spend a week on a model that had not moved. A retrained model faces the same held-out checks as the first validation, and rolls back if it is worse.
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 traffic, not the vendor's.
Yes. We keep an evaluation set from your own traffic, stored so a prompt or model change cannot silently alter output, and check hallucination, grounding and refusal after launch. Retrieval is watched separately from generation. See AI and machine learning testing.
Yes. Fairness that held at launch can fail later as traffic changes, so group and subgroup performance is part of the watch. Method detail is on AI bias and explainability testing. A free model review shows which groups to watch first.
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 drift monitoring is billed separately from a one-off review, and a free review of one model shows the surface first.
Email Us
Call Us
