[ Predictions on your own numbers ]
Forecasting, scoring, classification and detection built on your historical records. Not everything needs a language model — for numbers and tabular data, a well-built classical model is cheaper, faster, explainable, and usually more accurate.
Forecasting, scoring, classification and detection built on your historical records. Not everything needs a language model — for numbers and tabular data, a well-built classical model is cheaper, faster, explainable, and usually more accurate.
Tell us the decision you want to get rightGradient boosting on a well-built feature set beats a neural network on most business tabular problems, trains in minutes and can be explained to a regulator.
Feature importance and per-prediction explanations shipped with the model. "The system said so" does not survive a customer complaint or an audit.
Split by date, not at random. A model tested on data from before its training window looks brilliant and fails the day it goes live.
Inputs shift, behaviour changes, and accuracy decays quietly. Monitoring and a retraining schedule are part of the build, not a later project.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Demand, footfall, cash flow and stock, modelled with seasonality and events. Forecasts come with a range, because a single number invites false confidence.
Lead scores, churn risk, credit-style risk bands and recommendation ranking — written into the CRM so the team gets a sorted list, not a dashboard to interpret.
Anomalies, duplicates, fraud signals and automatic categorisation, tuned to the cost of a false positive versus a miss in your specific business.
Not "predict churn" but "who should the retention team call on Monday, and what does a wrong call cost". The framing decides the metric and the whole design.
How much history, how clean, how much leakage. Most machine learning projects are won or lost here, weeks before anything is trained.
Recency, frequency, ratios, seasonality — the domain knowledge encoded as columns. This is where accuracy comes from, far more than from the algorithm.
A baseline first — the rule your team already uses — then models measured against it. If the simple rule wins, we ship the simple rule.
An API or a scheduled batch that writes scores where the team already works, with the explanation attached and a fallback when the model is unavailable.
Input drift, prediction drift and realised accuracy tracked, with a retraining trigger. A model is a perishable asset.
Large language models have taken all the attention, and a lot of problems have quietly been handed to them that they are the wrong tool for. If your question is about numbers you already record — how many, how likely, which of these — a classical model trained on your history will usually be more accurate, hundreds of times cheaper per prediction, and explainable in a way a language model is not.
The work is not glamorous. Most of it is data auditing and feature engineering, and the modelling itself is often an afternoon. That is the honest shape of it, and pretending otherwise is how these projects overrun.
Every project starts with the heuristic your team already uses as the baseline. Surprisingly often it is hard to beat, and that is worth knowing early.
Validation on a random split leaks the future into the training set. Every model here is tested on the period after the one it learned from.
A score without a reason cannot be acted on, argued with, or defended. Both go into the record.
Despite everything, XGBoost and LightGBM remain the state of the art for the structured business data most companies actually have. Deep learning has not displaced them here.
Keeping training and serving features identical used to need serious infrastructure. It is now a manageable pattern at ordinary scale, and it removes the commonest source of silent failure.
Turning free-text notes, complaints and descriptions into usable columns is now cheap. The predictive model stays classical; the text gets pre-processed by a language model.
Drift detection and automated retraining are now standard practice rather than something bolted on after the first quiet failure.
For a tabular classification or scoring problem, a few thousand labelled examples with genuine variety is usually a workable start; forecasting wants two or three seasonal cycles. Volume matters less than coverage — a hundred thousand rows that all look alike teach less than five thousand that span your real range of cases.
Let’s talk about your machine learning project. No obligation, just a conversation.
Next service
AI Consulting