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Research report
AI Does Not Remove the Bottleneck
Find your real workflow constraints before buying AI tools.
AurvikAI · Machine Learning
Machine learning that stays accurate after launch.
Reliable forecasting, prediction, and classification systems trained on your data and continuously checked as the world changes.
We build machine learning around the decision you want to improve, not a technical score. From demand forecasting and customer retention to fraud detection and document sorting, every solution includes the data flows, monitoring, and update process needed to keep delivering value.
89%
Precision maintained twelve months after launch
40M+
Predictions delivered every month
Daily
Performance monitoring for every live model
Clients we've worked with
A model can look accurate today and quietly become wrong tomorrow.
Customer behaviour changes. Suppliers change. New products arrive. Source systems are updated. When the real world changes, model accuracy can slowly fall even while every dashboard looks normal. We build the data processes, automatic checks, and update triggers that catch problems before they affect important decisions.
Many machine learning projects fail because the data used after launch differs from the data used during development. We keep both consistent and make ownership clear from the start.
Explore our machine learning capabilities80%
Of delivery effort focused on dependable data and processes
Quarterly to daily
Improvement in retraining frequency after automation
From one dependable prediction to a complete self-improving system.
We choose the simplest approach that can solve your problem reliably, then add automation only when the value justifies it.
6x
Faster model improvement using automated training
AurvikAI client data
Machine learning that improves real decisions
Live systems delivering measurable value, not experiments that remain in a notebook.
Plan with greater confidence
Create forecasts for each product, location, or channel using seasonal patterns, promotions, and outside signals. Every result includes a realistic range so planners can see uncertainty instead of relying on one exact number.
ForecastingSeasonalityConfidence Range
31%
reduction in forecast error compared with the baseline
The foundation that protects performance over time
Every stage is recorded, monitored, tested, and repeatable.
Give the model consistent, trustworthy information during development and daily use.
Shared data definitionsConsistency
Use the same inputs during training and production so the model sees what it was prepared for.
Automatic data checksQuality
Catch missing, unexpected, or changed information before it reaches the model.
Clear review processGround Truth
Create consistent labels using written standards and measured reviewer agreement.
Full source historyTraceability
Track every dataset, calculation, and training run from beginning to end.
Models can fail quietly while every dashboard stays green.
Every model we launch includes daily checks for changing data and predictions, performance tracking against real outcomes, and confidence limits that send uncertain cases to a person. New versions are tested safely before release, every release can be reversed, and performance is checked across important groups so one weak area cannot hide inside a healthy average.
A model that slowly loses accuracy can cause more damage than one that stops working completely. Silent decline is the first risk we design against.
100%
Of production models actively monitored for changes
Under 24 hours
To detect a meaningful drop in accuracy
Practical lessons for production machine learning
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Selected work
AI systems running in production, described rather than named.
An industrial hygiene consultancyTurning lab data into review-ready reportsLab PDFs read automatically, exposure limits checked against the right jurisdiction, and report prose generated with the regulation cited.Under 1 hrto produce a report that used to take six to tenRead the case
A US remote-monitoring healthtechSupporting rehabilitation after dischargeRemote therapeutic monitoring built so the billing qualification cannot be faked, with an AI care plan no patient sees unapproved.15 mina day for a physician to review a full patient panelRead the case
A US healthcare practiceMaking surgery clearance easier to track.Referrals, pre-authorisations and clearances tracked in one place, with a rules engine that decides when a patient is ready.4 wksfrom kickoff to a tool the practice runs on dailyRead the caseWhat we build with
Modelling
- Python
- SQLAlchemy
- Evaluation harnesses
Data
- PostgreSQL
- ClickHouse / Snowflake
- AWS S3
Serving
- FastAPI (Python)
- Docker
- AWS
Questions we get asked
How do we know a model is better than what we do now?
By measuring both on the same data. Whatever you use today — a rule, a spreadsheet, an experienced person's judgement — is the baseline, and a model that cannot beat it does not go live. Setting that comparison up first is the step most often skipped, and it is the only thing that makes the result arguable.
How much data do we need?
It depends far more on how varied the problem is than on raw volume. A narrow, well-defined prediction can work on a few thousand examples; something with many edge cases needs to have seen them. The useful test is whether your history contains the situations you want predicted, including the rare ones that matter.
Can you explain why the model made a decision?
For most problems yes, and where you need it we choose methods that support it. Regulated decisions affecting people usually require an explanation you can defend, which rules out some approaches regardless of accuracy. We settle that requirement before choosing the model, because retrofitting explainability is rarely possible.
What happens when the model degrades?
It will, because the world moves under it. We monitor whether incoming data still resembles what the model learned from and whether accuracy is holding, then retrain on that signal. A model with no monitoring is a system quietly getting worse while everyone assumes it still works.
Do we need our own model or will an API do?
An API for anything general — language, vision, transcription. Your own model where the value is in patterns specific to your business that no general provider has seen. We start with the API because it settles in days whether the problem is tractable, and build only where that proves insufficient.
Who owns the model and the training data?
You do, both. The trained artefact, the training set, the evaluation set and the code live in your accounts from the start. The evaluation set is the asset people undervalue: it outlasts any particular model and is what lets you move to a better one later.
Partners in delivery
AI Cloud PartnerPartner Network
Ready to use machine learning for a real decision?
Start with the decision you want to improve. We will tell you honestly whether machine learning is needed, what data would make it work, and how to keep the model accurate after launch.