MACHINE LEARNING DEVELOPMENT — TRAINED ON YOUR DATA
The model is about a tenth of the work. The other nine tenths is your data.
We build machine learning where a general-purpose model genuinely can’t do the job — forecasting from your own history, classifying your own images, ranking your own catalog. And we start by checking whether you have the data to make it work, because that is the answer more often than the algorithm is.
✓ Baseline first, model second✓ Accuracy measured on data it never saw✓ Deployed and monitored, not handed over✓ You own the model and the weights
Send a sample export. We’ll tell you whether there’s a model in it before anyone signs anything.
In short: machine learning means training a model on your own historical data so it can make a specific prediction about a new case — which customer will churn, which invoice is anomalous, what this photograph contains, what this person will buy next. It is the right tool when the answer depends on patterns particular to your business, and the wrong tool when a general model with a good prompt already gets there.
When to train something, and when not to
A large share of what used to require a trained model can now be done with an API call and a well-written prompt. Summarising, extracting fields from documents, classifying free text into obvious categories, drafting — all of that is cheaper, faster to build and easier to change as a prompt. We will tell you when that’s your answer, and it frequently is.
Training earns its cost in four situations. When the pattern is specific to you and appears nowhere in public data — your customers, your equipment, your fraud. When you need a number rather than a sentence, with a confidence interval attached. When the volume is high enough that per-call API pricing becomes the dominant cost. And when the answer has to be explainable in a way that survives a regulator or an auditor.
If none of those apply, we’d rather build you the cheaper thing. Our opportunity audit exists partly to catch this before anyone spends a training budget.
We won’t quote a target accuracy from a conversation. We’ll quote it after looking at a sample, and here is what we’re looking for.
Enough examples of the thing you care about
Not total rows — examples of the outcome. Fifty thousand customers with eleven churners is eleven examples, and no amount of modelling fixes that. Rare-event problems are solvable but need a different approach, and knowing which one you have changes the whole plan.
Labels that mean the same thing throughout
If “cancelled” meant something different before the 2024 system migration, the model learns the migration rather than the behaviour. Finding this is unglamorous work and it is where most of the early weeks go.
Only information you’d actually have at decision time
The classic failure: a churn model that turns out to use the cancellation-reason field. It scores brilliantly in testing and is useless in production, because in production that field is empty. We hunt for this deliberately — a model that looks too good usually is.
A decision that will actually change
A prediction nobody acts on has no value however accurate it is. Before we build, we agree what happens differently when the model says a number — who sees it, and what they do.
The baseline rule
Before any model is trained, we build the dumbest thing that could work: the current rule of thumb, or simply predicting the most common outcome every time. That number goes at the top of the report.
It matters because “94% accurate” sounds impressive until you learn that guessing “no” every time scores 93%. Plenty of models in production are worse than the spreadsheet they replaced, and nobody noticed because nobody measured the spreadsheet. Every accuracy figure we give you comes with the baseline beside it, measured on data the model never saw during training.
What it will not do
Find a pattern that isn’t there
If the outcome genuinely depends on something you don’t record, no model will recover it. That result is worth knowing in week three rather than month six, and it’s why the data check comes before the contract.
Stay accurate on its own
Your customers change, your products change, and the model quietly gets worse. Drift monitoring and a retraining schedule are part of the build, not an upsell — that’s what retraining and monitoring cover.
Justify a decision about a person on its own
Anything touching credit, employment, housing or insurance carries fairness and explainability obligations that vary by jurisdiction. We build with explainable methods and documented feature importance in those cases, and we say plainly where you need legal review rather than more engineering.
How a model project runs
Phase 0 — the data check, before you commit
A sample export and a short piece of work: is there signal here, how many usable examples, what’s leaking, what’s missing. It ends with a yes, a not-yet, or a no. Priced small on purpose.
Phase 1 — baseline, then the first real model
Cleaning, feature work, a simple model to beat the baseline, then something better if the gain justifies the complexity. Simpler models that people trust get used; sophisticated ones that nobody understands get switched off.
Phase 2 — deploy where the decision is made
A score that lives in a notebook changes nothing. It goes into the CRM field, the dashboard, the queue ordering — wherever the person deciding will actually see it, which is integration work as much as modelling.
Phase 3 — watch it, and retrain on a schedule
Live accuracy tracked against the baseline, alerts when inputs drift, and a documented retraining cadence. You get the code, the weights and the pipeline — if you part ways with us, the model keeps working.
What our clients say
The businesses we’ve built for, in their own words.
★★★★★
“When I approached Abedin Tech with my land share selling plan, I wasn’t sure how it would work. But thanks to their precise strategy and powerful marketing, my business is now thriving. They truly understand their clients’ needs and go above and beyond.”
Owner, Richland Properties
Real Estate
★★★★★
“I approached Abedin Tech to develop my website with several specific functionalities. Their team delivered exactly what I envisioned, creating a beautifully designed website that met all my requirements. I highly recommend Abedin Tech.”
Rohit
Owner, Shop from China
★★★★★
“The decision to partner with Abedin Tech was the best decision we made. Our site looks great, our traffic is through the roof, and our sales are better than ever. Abedin Tech is the perfect digital partner that offers what is beyond your expectations!”
James Anderson
★★★★★
“We had an idea but no sense of direction. With each step of the way, Abedin Tech guided us and turned our vision into a beautiful website with functionality. The outcome is evident by the numbers!”
Isabella Scott
Send us a sample before you commit to anything
An export of the data you’d want a model to learn from, and a sentence about the decision you want it to inform. The data check tells you whether there’s a model in there — including when the answer is no.
How much data do we need to train a machine learning model?
The number that matters is examples of the outcome, not total rows. Fifty thousand customer records containing eleven churners is an eleven-example problem, and no modelling technique fixes that. Rare-event problems are solvable with a different approach, so establishing which kind you have is the first thing the data check answers.
Do we need a custom model, or will a general AI model do?
A large share of what once needed training — summarising, extracting fields, classifying free text into obvious categories — is now cheaper and faster as an API call with a good prompt, and we will say so. Training earns its cost when the pattern is specific to your business and absent from public data, when you need a number with a confidence interval rather than a sentence, when volume makes per-call pricing dominant, or when the answer must be explainable to an auditor.
How do you know whether a model is actually any good?
Every accuracy figure is reported next to a baseline — the current rule of thumb, or simply predicting the most common outcome every time — and measured on data the model never saw in training. Ninety-four percent accuracy is meaningless if guessing no every time scores ninety-three. Plenty of production models are worse than the spreadsheet they replaced because nobody measured the spreadsheet.
What is data leakage and why do you keep checking for it?
Leakage is when a model uses information that would not exist at the moment the prediction is needed — a churn model quietly relying on the cancellation-reason field, for example. It scores beautifully in testing and is useless in production, where that field is empty. We hunt for it deliberately, because a model that looks too good almost always is.
Who owns the model when the project ends?
You do — the code, the trained weights and the training pipeline. If you stop working with us the model keeps running and can be retrained by someone else. We also document the features and their importance, which is what makes that handover meaningful rather than nominal.
Does a model need maintenance after it goes live?
Yes. Customers change, products change and inputs drift, so accuracy decays quietly rather than failing loudly. Drift monitoring and a documented retraining cadence are part of the build rather than an upsell, and live accuracy is tracked against the original baseline so decay is visible rather than assumed.
What sits either side of a model
Models rarely fail at the modelling. They fail at the data before and the upkeep after.
