Data pipelines and warehousing
Pull data out of the tools you already use, clean it, and land it somewhere a person or a model can actually query.
Data science & AI consulting
Your data already knows.
We build the thing that acts on it.
Cog Cloud Consulting takes messy business data and turns it into models, dashboards and AI assistants that run every day — not slide decks that sit in a folder.
Python · SQL · AWS · Azure
Remote across India
Fixed-scope pilots
who is likely to leave?
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Each dot is a customer. The shaded background is the model's prediction for anyone landing there — tap the chart to add a customer and watch it rethink.
Try it
This is what a model feels like from the inside
Two toys, running entirely in your browser. Real projects use your data and far more signal — but the shape of the thing is the same.
churn_risk · scorer
logistic model
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move a slider
Why we start with a toy
A model is only useful once someone trusts it enough to act on it. That trust does not come from an accuracy score in a report — it comes from watching the thing respond to inputs you recognise.
So the first thing we build in any engagement is something you can push on. Change a number, see the prediction move, argue with it. If it disagrees with your instincts we find out why in week two rather than month six.
The scorer on the left is deliberately simple: four inputs, one logistic model. A production version for your business would draw on transaction history, product usage, seasonality and support records — and would run against every customer, every night.
Explainable by default
Benchmarked vs your current process
Yours to keep
What we build
Six things we get asked for most
Most work starts with one of these and grows. You can hire us for a single piece or the whole chain.
Analytics and dashboards
The numbers your team argues about, in one place, refreshed on schedule, with definitions everyone agrees on.
Machine learning models
Churn, demand, credit risk, lead scoring, maintenance windows. Trained on your history, scored against a baseline.
Assistants and document AI
Chat over your own documents, contract and invoice extraction, drafting tools — with retrieval, guardrails and evaluation.
Image and video models
Quality inspection, counting, OCR on scans and forms. Useful when the data arrives as pictures rather than rows.
MLOps and deployment
Getting a notebook into production: APIs, scheduling, monitoring, retraining, and an alert when accuracy drifts.
How we work
Small, sequenced, and reversible
Every engagement runs in the same order, so you can stop after any stage and still keep something that works.
01
Scope
We look at your data and the decision you want to improve, then write down what success would measure.
1–2 weeks02
Prototype
A working version on real data, benchmarked against how you do it today. If it does not beat the baseline, we say so.
2–4 weeks03
Productionise
Pipelines, deployment, access control and documentation, in your cloud account under your ownership.
4–8 weeks04
Operate
Monitoring, retraining and changes as your data shifts — or a handover so your own team runs it.
OngoingEngagements
Three ways to start
Data audit
- Review of your data sources and quality
- Shortlist of use cases ranked by effort and payoff
- Architecture recommendation
- Written report and walkthrough
Pilot project
- One use case, taken from data to working model
- Measured against your current process
- Deployed in your cloud account
- Code and documentation handed over
Embedded team
- Data and ML engineering capacity on call
- Model monitoring and retraining
- Roadmap and reviews with your team
- Training for your in-house engineers
Tools we work in
PythonSQLPyTorchscikit-learn
SparkdbtAirflowAWS
AzureSnowflakePower BIDocker
LangChainVector databases
Writing
How we think about this work
Notes written for the person who has to approve the budget — what these projects cost, where they fail, and how to tell whether one is worth starting.
Machine learning
What a churn model actually costs — and when it pays for itself
The arithmetic to run before you commission anything, and how to benchmark a model against the instinct it is meant to replace.
16 Aug 2026 · 9 min read Generative AIChat over your own documents: what actually breaks in production
Retrieval quality, hallucination guardrails, and the evaluation set you should build before you write a line of prompt.
Coming soon FoundationsYour dashboard disagrees with your finance team. Here is why.
Metric definitions, late-arriving data and timezone drift — the three causes behind almost every number nobody trusts.
Coming soon
Read all writing
New pieces roughly every fortnight.
Start here
Tell us the decision you want to get better at
Send a short note about your data and what you are trying to predict, automate or understand. We will reply with whether we can help, and what the first step would cost.
- Phone
- +91 95738 22723
- Based in
- Hyderabad, India — working remotely with clients anywhere
- Response
- Within one working day