01
Demand forecasting
Product-level forecasts that help teams plan inventory, reduce stockouts and spot changing demand before it becomes expensive.
CUSTOM PREDICTION SYSTEMS FOR DTC, E-COMMERCE & SAAS
Predict churn and demand before they cost you.
I’m Abdul Qadeer, an ML engineer. I build custom prediction models for founders and operations teams — churn, demand forecasting, lifetime value and practical automation — without requiring an in-house data-science team.
Live deployed buildsExplainable outputsAPI-ready delivery
MODEL / PRODUCTION
PredictWhat is likely to happen next
ActTurn output into a useful workflow
Built for use, not demos. Clear interfaces, honest limitations and deployment-ready handoff.
A LOW-RISK FIRST STEP
Bring the question. I’ll help test whether your data can answer it.
In a free 30-minute review, we’ll clarify the business decision, the data you already have, and the smallest useful pilot. If the signal is weak or the project is not ready, I’ll say so.
Book the review →
No proposal required. Start with the decision you need to improve.
01 / SERVICES
Models that answer a business question.
No AI theatre. We start with the decision you need to improve, then build the smallest reliable system that moves it.
02
Churn, LTV & propensity
Prioritized customer signals for retention and growth, with explainable outputs your marketing or success team can actually use.
03
Computer vision
Detection, segmentation and OCR pipelines for real operational problems — packaged as a practical app or API, not a loose notebook.
04
Risk & fraud scoring
Decision-support models that rank transactions, accounts or applications for review — with thresholds and explanations your team can inspect.
05
AI workflow automation
n8n, Make and API workflows that connect models to alerts, dashboards, CRMs and the tools your team already uses.
02 / SELECTED WORK
Proof, not promises.
Three systems across commerce, climate resilience and road safety. Select a project to explore the build.
FORECASTING / XGBOOST
A clearer answer to “what should we stock next?”
Pulse turns historical sales into product-level demand forecasts, then serves the results through a deployed API and decision-focused dashboard.
ChallengeInventory decisions happen before demand is known.
ApproachTurn sales history into product-level forecasts.
DeliveredA deployed API and a decision-focused dashboard.
APIDEPLOYED OUTPUT
WebBUSINESS DASHBOARD
Scope note: this is a live demonstration system; a client build would be retrained and validated on that business’s own data.
View live project ↗Math-trained. Self-taught in modern ML. Focused on making systems useful in the real world.
I lead Qadeer Automations, an independent AI and automation practice. My work spans model development, APIs, dashboards and operational workflows — so the output does not stop at a high notebook score.
I communicate clearly with technical and non-technical teams, document limitations honestly, and build in phases so clients can validate value before investing in more complexity.
Core toolkit
03 / PROCESS
A straight line from problem to production.
Each phase ends with something concrete you can inspect before committing to more complexity.
Diagnose
Clarify the decision, available data, success metric and constraints.
OUTPUT · DATA & SCOPE REVIEWPrototype
Build a focused baseline and test whether the signal is real.
OUTPUT · VALIDATED BASELINEProductize
Package the model as an API, app or workflow people can use.
OUTPUT · WORKING SYSTEMHandoff
Test, document and deploy with honest scope and next steps.
OUTPUT · TESTS & DOCUMENTATION04 / FAQ
Before we talk, here are the straight answers.
Prediction projects work best when the business decision is clear and the limits are visible.
What data do I need?
Usually, historical records tied to the outcome you care about: orders and inventory for demand, or customer activity and status for churn. In the first review, we check whether the coverage, labels and time range are useful before discussing a build.
Can you guarantee a specific accuracy?
No responsible model builder should promise accuracy before seeing the data. I establish a baseline, choose metrics that match the business risk, validate on held-out data, and make the model’s limitations clear.
How long does a project take?
It depends on data readiness, integrations and the form of delivery. A focused prototype comes before a larger build, so you can judge whether the signal is useful without committing to unnecessary scope.
How is pricing decided?
After the data and delivery needs are understood, I scope the smallest useful phase with clear deliverables. You receive the scope before work begins; infrastructure or third-party costs are kept visible.
What do I receive at handoff?
The agreed working system — such as an API, dashboard or automation — plus testing notes, documentation, deployment guidance and known limitations.
Have data, but not yet a decision system?
Tell me what you are trying to predict, who will use the result, and what a useful outcome looks like. I’ll reply with a practical next step.
No polished brief needed — a business question and a short description of your data are enough to start.