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Predictive Analytics Development

The predictive analytics development company that survives production

BinaryBrill is a predictive analytics development company building AI forecasting services and predictive models over the historical data you already hold — demand, churn, risk scoring, inventory and predictive maintenance. You work with in-house senior engineers who treat honest backtesting against a baseline, not a good-looking offline score, as the whole point of the exercise.

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A senior engineer replies within 24 hours — not a sales rep.

Why the predictions you have aren't changing any decisions

The forecast is accurate in the notebook and useless in the business

A model that scores well in a data scientist's notebook changes nothing if the number arrives after the purchase order is placed, lands in a spreadsheet nobody opens, or comes with no explanation a planner can act on. A prediction only has value when it reaches the decision early enough, and in the tool where the decision is actually made.

It scored brilliantly, then someone found the leak

Offline accuracy that looks too good usually is. A feature that quietly encodes the answer — a field only populated after the event you're predicting, a timestamp that reveals the outcome — inflates the score in testing and collapses in production, where that information isn't available yet. Catching leakage takes deliberate, sceptical evaluation, not a higher accuracy number.

Nobody set a baseline, so 'good' has no meaning

A model reported at 85% accuracy sounds impressive until you learn that predicting 'same as last week' would have scored 83%. Without a naive baseline to beat, an accuracy figure is theatre. Plenty of models that clear an internal bar add nothing over a simple rule that would have cost nothing to run.

It was accurate at launch and quietly drifted

Customer behaviour shifts, a competitor changes pricing, a supply chain reroutes, and the patterns the model learned stop holding. Without drift monitoring, accuracy erodes silently and the first sign is a planner quietly going back to their gut. A model is only as good as the last time someone checked it was still right.

How a predictive analytics development company should actually work

A prediction is only worth building if someone changes a decision because of it, and only worth trusting if it has been tested the way it will be used. We measure against a baseline, evaluate honestly, and design for the day the data shifts.

A baseline before any modelling

Before anything clever, we establish the simplest forecast that could work — last year's number, a moving average, the current rule of thumb — and measure everything against it. Often the baseline is closer to acceptable than anyone expected, which changes the budget conversation; and when a model can't beat it, that is a finding worth having early.

Feature engineering is where the accuracy comes from

For tabular prediction the model matters less than the signals you feed it. We build features from your transactional history, seasonality, promotions, price and relevant external data like weather or calendars — and we check that every feature would genuinely be available at prediction time, because one that isn't is a leak waiting to embarrass you.

Honest evaluation: backtest, don't just split

For anything time-dependent we backtest on rolling historical windows rather than shuffling rows at random, because a random split lets the model peek at the future and flatters the score. We report prediction intervals rather than single numbers, so a planner can see the uncertainty and decide how much to lean on it.

Built for the day the data shifts

The model ships with drift and accuracy monitoring, a defined retraining schedule, and delivery into the tool where the decision lives — the ERP, the planning sheet, the dashboard. A model that scores well offline and one that survives contact with production are different things, and we build for the second.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

AI Forecasting Services for Demand & Sales

AI forecasting services for demand, sales and volume — per product, location and horizon — built to land in the planning cycle early enough to change an order. The accuracy comes from features that capture seasonality, promotions and price, and from backtesting that proves the forecast on history before anyone relies on it.

Per-SKU and per-location forecasts at the horizon your planning cycle needs
Seasonality, promotion and price signals engineered in, not ignored
Prediction intervals so planners see the uncertainty, not just a point number
Backtested on held-out history and measured against a naive baseline

Churn & Propensity Modelling

Models that score each customer on the likelihood of churning, converting or responding, so effort goes where it changes an outcome rather than being spread evenly. A churn score is only useful with a reason attached and a threshold tied to what your team can actually action, so we build both.

Churn, conversion and propensity scores from your own behavioural and transactional data
Reason codes so the team knows why an account scored the way it did
Thresholds tied to the number of interventions you can realistically make
Delivered into the CRM or success tool where the outreach happens

Credit & Risk Scoring

Scoring models that estimate the likelihood of default, fraud or other risk from your historical records, built for the transparency a risk decision demands. Where a regulator or an underwriter has to understand a decision, we favour models whose reasoning can be explained over a marginal accuracy gain from a black box.

Default, fraud and risk scores calibrated on your own outcomes
Explainable models and reason codes where a decision has to be justified
Thresholds set against the review capacity and risk appetite you actually have
Honest evaluation on held-out history, including the base rate the score has to beat

Predictive Maintenance & Failure Prediction

Models over sensor and maintenance history that flag the equipment likely to fail before it stops production, turning unplanned downtime into scheduled work. The hard part is the false-alarm rate: cry wolf and maintenance stops listening, so the threshold is tuned to what the team can act on.

Failure and remaining-useful-life models from sensor telemetry and maintenance logs
Alerting tuned to the false-alarm rate your maintenance team can absorb
Lead time long enough to schedule the work, not just to watch it fail
Delivered into the maintenance system where the work order is raised

Custom Predictive Modelling for Business

Custom predictive modelling for the questions no off-the-shelf tool answers — inventory and staffing forecasts, pricing response, lead scoring, anything where predicting a number or a likelihood from your own data would change a decision. These are predictive analytics solutions for business, built and evaluated on your records rather than a generic benchmark.

Bespoke regression, classification and forecasting models scoped to your decision
Inventory, staffing and capacity forecasts that balance competing costs
Evaluation on your own history against a baseline, so the value is proven not assumed
Delivered into the tool where the decision is made, with monitoring after launch

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Modelling & machine learning

scikit-learn
XGBoost
LightGBM
CatBoost
PyTorch

Time series & statistics

Prophet
statsmodels
sktime
Darts
NumPy
SciPy

Data & warehousing

Snowflake
BigQuery
dbt
Apache Airflow
Pandas
Spark

MLOps & monitoring

MLflow
Feast
Evidently
FastAPI
Docker
Weights & Biases

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Data audit and baseline
We look at the history you actually have — how far back it genuinely goes, how clean it is, whether the outcome you want to predict is even recorded reliably — and establish the naive baseline any model has to beat. This tells you early whether the data supports the prediction you're after.
You get: A data assessment covering history, quality and the target variable, a documented baseline score, and an honest read on which of your intended use cases the data can support.
02
Feature engineering and a backtest harness
We build features from your transactional, seasonal and external signals, and set up backtesting on rolling historical windows — the way the model will actually be used. Nothing gets optimised until there is an honest scoreboard to optimise against, and every feature is checked for leakage.
You get: A feature pipeline, a backtesting harness on held-out history, and a scoreboard your team can read without a data science background.
03
Model against the backtest
Algorithm choice, features, hyperparameters and horizon are each an experiment with a number attached, measured against the backtest and the baseline. We keep what moves the metric and discard what merely looked better in a chart, and we report intervals rather than a single point estimate.
You get: A tuned model meeting the accuracy bar agreed in step one, with prediction intervals and an experiment log showing what was tried and how it scored.
04
Deploy into the decision and monitor drift
The model is delivered where the decision is made — ERP, planning sheet or dashboard — and runs beside the current process at first, so discrepancies surface while the old way is still there to catch them. Drift and accuracy monitoring and a retraining schedule are part of the deployment, not an afterthought.
You get: Production deployment into your decision tool, a parallel-run comparison, drift and accuracy dashboards, and a retraining runbook your engineers can operate.

Where we've applied this

Retail

Replenishment and demand forecasting per SKU and location that accounts for seasonality, promotions and price, so ordering stops relying on last year plus a guess.

Financial services

Credit and risk scoring with the reason codes a decision needs, plus churn and propensity models that flag the accounts worth an intervention this week.

Logistics & supply chain

Volume and ETA forecasting that factors in seasonality and disruption, and inventory forecasts that balance stockouts against the cost of holding.

Manufacturing

Predictive maintenance on sensor telemetry that flags the machines likely to fail before they stop the line, tuned to the false-alarm rate maintenance can staff.

SaaS & subscription

Churn and expansion-propensity models that tell customer success which accounts to call and why, not just a risk score with no handle on it.

Insurance

Claim-likelihood and risk models that price and triage more consistently, with the transparency an underwriter and a regulator both need to see.

Questions buyers ask us

How do we know predictive analytics will actually help us?

The test is whether a better prediction would change what you do. If you'd order differently, staff differently or call a different customer given an accurate forecast or score, there's value to capture. If the decision wouldn't change, or you already predict well enough by hand, a model adds cost without benefit — and we'll say so in discovery. The second test is data: you need enough clean history, with the outcome you want to predict actually recorded.

Do we need machine learning, or would a simple baseline do?

Often a baseline is enough, and finding that out early is one of the cheapest wins available. A moving average or a seasonal rule is cheap, transparent and sometimes within a point or two of a complex model — in which case the complex model isn't worth its running cost. We always build the baseline first and only recommend machine learning where it clearly beats it. Sophistication that doesn't move the number is just risk you pay to maintain.

Can you tell us how accurate the forecast will be before we start?

No one honest can, before seeing your data. What we commit to is telling you early: backtesting against your own history gives a measured error range within the first phase, and if that range is too wide to be useful for your decision, we'll say so rather than build it. A forecast with a known error band you can plan around beats a confident single number with no track record behind it.

What drives the cost of a predictive analytics project, and how long does it take?

Data condition first, integration second, accuracy bar third. Clean history in one warehouse is inexpensive to work with; reconciling systems that disagree, or building history that was never captured, is where the hours go. Getting from a decent model to a reliable one takes far more iteration than getting to decent. On timeline, a baseline and backtest is usually a few weeks; a deployed model wired into your decision tool with monitoring is typically a few months, depending mostly on the state of your data.

How much historical data do we need?

Enough to cover the patterns you want the model to learn, which for anything seasonal means at least a couple of full cycles — you can't forecast a Christmas peak from six months of data. For scoring problems like churn or default, what matters more is having enough examples of the outcome itself, not just total rows. The data audit in the first phase tells you where you stand, including when the honest answer is to collect more before modelling.

Will our data be used to train models for other clients?

No. Your data stays within your environment and your accounts wherever the architecture allows, and it isn't used for anything outside your project. The models, features and pipelines we build on your data are yours outright, from the first commit. Where a cloud warehouse or platform sits in the design we tell you exactly what runs where and under what terms before you approve it.

What happens when the model drifts after launch?

It will — behaviour shifts, prices move, supply chains reroute, and the patterns the model learned stop holding. That is why drift and accuracy monitoring is part of the deployment, not an afterthought. You get alerted when accuracy falls below the agreed threshold, and a retraining procedure documented well enough for an engineer who wasn't on the project. Many clients keep us on to run that cycle; others take it in-house with the runbook and monitoring we hand over.

When is predictive analytics the wrong tool for the job?

When the decision wouldn't change whatever the prediction said. When you don't have enough history, or the outcome you want to predict isn't reliably recorded. When the thing you're forecasting is driven by a one-off event with no precedent in the data — a model trained on normal times won't call the shock. And when a simple baseline already does the job, the model is expensive theatre. We'd rather establish that in discovery than bill you to find it out.

Who writes the code, and where are they based?

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted. You own the code, the models, the repository and the pipeline from day one, and the person demonstrating the work each sprint is the person who built it.

Tell us what you're trying to predict

Describe the decision you'd make differently with a better forecast or score, and whatever you know about the history behind it. A senior engineer replies within 24 hours with a straight read on whether your data supports it, what accuracy is realistically achievable, and whether a simple baseline would get you there first — including if the honest answer is that it wouldn't help.

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