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Research report
AI Does Not Remove the Bottleneck
Find your real workflow constraints before buying AI tools.
AurvikAI · Generative AI
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.
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.
Demand forecasting
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.
Data and inputs
Give the model consistent, trustworthy information during development and daily use.
You getShared data definitions, automatic data checks, clear review process, full source history
Training and evaluation
Build models that can be compared, repeated, and explained.
You getComplete experiment records, automated optimisation, group-level testing, approved model history
Launch and monitoring
Find declining performance before it changes a business decision.
You getChange detection, outcome tracking, silent testing, controlled retraining
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
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 creator-tools startupValidating the product before funding the buildA rapid concept workshop that checked every platform assumption against the real API docs — and corrected several of them.10 daysfrom first workshop to a fundable, build-ready packageRead the caseWhat we build with
Models
- Anthropic Claude
- GPT (OpenAI)
Grounding
- pgvector
- Pinecone / Chroma
- OpenAI Embeddings
Application
- Next.js
- FastAPI (Python)
- PostgreSQL
Questions we get asked
Where does generative AI help?
Where the work is reading, drafting or summarising, and a person still checks the result. Extracting fields from documents, drafting a first version of something formulaic, answering questions from material you already hold. It struggles where the task needs a guaranteed correct answer with no reviewer, which is why we look for the review step before we look for the model.
How do you stop it making things up?
By giving it less room to. Answers are grounded in your own material and checked against it before anyone sees them, so the model explains retrieved evidence rather than recalling from training. Where a claim cannot be traced to a source, the system says so. A confident wrong answer is worse than an admission of not knowing.
Will our data be used to train someone's model?
Not under the enterprise agreements we build on. Anthropic and OpenAI both offer terms where prompts and outputs are excluded from training, and for regulated work we sign the vendor agreements that make that contractual. If your data cannot leave your own infrastructure at all, that constrains which models are available and we scope it that way from the start.
What does it cost to run once it is live?
Inference is a running cost that scales with use, unlike a licence. We log every call with its tokens and estimated cost from the first week, route routine work to smaller models, and cache what repeats. The number to watch is cost per completed task, because a cheaper model that needs three attempts costs you more.
Should we build this or buy a tool?
Buy where your problem is the same as everyone else's — transcription, translation, general chat. Build where the value is in your own data, your own workflow or your own rules, because that is exactly what a general tool cannot reach. Most of what we are asked to build is the second kind, and we will say so when it is the first.
How do you know whether it is working?
An evaluation set built from real examples of your work, with the right answers agreed before anything is built. Every change runs against it, so an improvement in one area that breaks another is visible immediately. Without that, judging a generative system is a matter of opinion and the opinions change weekly.
Practical lessons for production machine learning
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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.