AI · Machine learning
Banao builds custom machine-learning models — forecasting, classification, recommendation, ranking, anomaly and fraud detection — trained on your data and wired into the systems that act on the prediction.
We own the whole lifecycle: the data audit, the feature engineering, the training, the validation, the deployment, and the drift monitoring that keeps a model accurate long after the launch demo is forgotten. It is the same engineering we run inside Banao before any of it reaches a client.
Banao — InterviewGod— scores every engineering applicant we receive, on a model we trained and keep retraining on our own hiring outcomes.
A model in production is not a notebook with good numbers. It is a data pipeline, a trained model, a serving layer, a validation harness, and the monitoring to know when it has drifted — we build all of it, not just the part that fits on a slide.
Supervised and unsupervised models trained on your data for your specific prediction — not a pre-trained API bent to fit a problem it was never measured on.
Models that predict demand, load, churn, or price across horizons your planners actually use, with confidence intervals instead of a single optimistic number.
Recommendation and ranking engines wired into your storefront, feed, or catalogue, tuned to the metric you sell on rather than offline accuracy alone.
Models that flag the rare, costly event — fraud, defect, failure, outlier — and route it for review, tuned to the false-positive rate your team can live with.
Credit, risk, lead, and churn scoring built for the decision it feeds, with the explainability a compliance or risk team will ask for before it goes live.
The pipelines that turn your raw tables into model-ready features — reproducibly, with the same transforms at training and at inference so the model sees what it expects.
Real-time and batch inference deployed to your cloud, with the latency, throughput, and rollback path a production system needs rather than a notebook re-run.
Versioned data, code, and models, automated retraining, and a release path so a new model can be tested and shipped without a person hand-copying weights.
Live monitoring of input drift and prediction quality, so you find out a model has gone stale from a dashboard and an alert, not from a quarter of bad decisions.
Honest offline and online evaluation on held-out data, leakage checks, and the explanations a regulated decision needs — built before launch, not bolted on after.
Most machine-learning work starts at the wrong end — with an algorithm someone wants to try. We start with the decision: what action changes when the prediction is good, who takes it, and what a wrong prediction costs. If no decision moves, there is no model worth building, and we will say so before you spend on one.
From there the work is mostly data and discipline, not modelling. We audit what data you actually have, build a stupid baseline first so there is a number to beat, then earn every point of accuracy through feature engineering and validation that would survive an audit — before anything is deployed.
We define the action the prediction feeds and the cost of being wrong, then pick the simplest model that moves it. A 2% accuracy gain no one acts on is not worth building.
A simple rule or a logistic regression is the bar a complex model has to clear. Often it clears the business need on its own, and we ship that instead of a heavier model.
Held-out data that respects time, strict leakage checks, and metrics that match the decision. Offline numbers that look too good usually mean the model has seen the answer.
Features are computed one way, used in two places. Train-serve skew is one of the quietest ways a model that tested well makes bad calls in production — we close it by design.
We have been called in to rescue enough stalled ML projects to see the same failures repeat, and almost none of them are about the model being too weak. They are about data, ownership, and what happens after launch — the engineering, not the algorithm.
We would rather name these on the first call than bill you to discover them on the third. If your last model demo never reached a real decision, it very likely died of one of the following.
The labels were thin, the history was short, or the target was defined three ways across the company. No algorithm fixes a dataset that cannot answer the question.
A model that predicts but feeds no workflow is a dashboard nobody opens. If the prediction does not change what a person or a system does, accuracy is theatre.
Teams spend months moving a metric from 91% to 93% while the business needed 85% and a deployment. We optimise to the decision threshold, then ship.
A model is most accurate the day it ships and degrades from there as the world moves. Without monitoring and a retraining path, a good model quietly becomes a liability.
A model is a perishable asset. The data it was trained on described a moment that is already passing: prices move, customers change, fraud adapts to whatever you deployed last quarter. A model that was right at launch is, by default, getting more wrong every week unless someone is watching it.
So we treat deployment as the start of the work, not the end. The serving layer, the monitoring, and the retraining loop are deliverables we build with you, so that when accuracy slips you see it on a chart and ship a fix — rather than learning from a quarter of decisions that quietly went bad.
We track the distribution of what goes in and the quality of what comes out, so input drift and falling accuracy raise an alert before they cost a reporting cycle.
New data, a re-run, a validation gate, a versioned release. Refreshing a model becomes a routine that runs on a schedule or a trigger, not a fortnight of someone's time.
Every model release is versioned and reversible. If a new model underperforms in production, you fall back to the last good one in minutes, not after the damage is counted.
We build so your engineers can read, run, and retrain the system without us. The goal is a model your team operates, not a black box that needs us on retainer to breathe.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.
Banao — InterviewGod
InterviewGod scores every engineering applicant Banao receives before a recruiter opens the pile, on a model trained on our own hiring data and retrained as outcomes come in. We run our own hiring on it before we offer the pattern to anyone.
RAK Ceramics
Industrial ML that watches production signals and flags the anomaly that precedes a defect, so a person inspects the right unit instead of a random sample. Built and run with one of the GCC's largest ceramics manufacturers.
E-commerce marketplace (anonymized)
A recommendation model wired into the storefront and a demand forecast feeding procurement — two models sharing one feature pipeline, tuned to revenue and to inventory cost rather than to an offline accuracy score.
Banao operates a ~300-person engineering company on its own machine learning before any client sees it. InterviewGod scores our own engineering applicants on a model trained on our hiring outcomes; Vikaas drives our own demand generation. Both are models making real decisions on our own operation, every working day, with our team in the loop.
That is the difference between a vendor who has read about ML and one who depends on it to run a business. When a model has to hold up against our own hiring and our own pipeline, the version that reaches your workflow is already past the failures most pilots die of.
We deliver from offices in India, the UAE, the UK, and the US, and we build to the data-residency, governance, and explainability rules each market expects of a model that makes decisions.
From Dubai we build industrial and retail ML across the free zones and the wider GCC, including production-line work with RAK Ceramics. Models and the data they train on are kept inside UAE boundaries where the PDPL and client policy require it.
Vision 2030 industrial, energy, and logistics programmes are moving from dashboards to operated forecasting and anomaly-detection models. We build Arabic-capable pipelines and keep data in-Kingdom to meet PDPL and SDAIA expectations for regulated workloads.
Reshoring and labour cost are pushing US enterprises toward forecasting and automation models that pay for themselves. For California and New York teams we build to SOC 2 controls, with the validation, audit-logging, and model governance procurement now asks for.
Our Cambridge UK presence supports fintech and enterprise credit, fraud, and risk models under UK GDPR and ICO guidance, where a clear explanation of why a model scored someone the way it did is a condition of going live, not a nice-to-have.
Bangalore and Chandigarh hold our delivery bench, so a build starts in weeks. We design to the DPDP Act and run cost-efficient model development and MLOps close to the engineers who ship and maintain it.
Most vendors will sell you a model regardless. We would rather tell you when not to train one — it is why technical and finance teams take our second call.
You have likely been pitched models by vendors who quote a build before they have seen your data. We start by proving a model should exist and that your data can support it, not by quoting a number.
We audit your data, define the decision a model would feed, test feasibility on the hardest case, and hand back a scoped model design, an honest data assessment, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We build the data pipeline, train and validate the model, and ship the serving layer together — feature engineering, leakage-proof validation, and deployment are deliverables, not afterthoughts.
We deploy to your cloud with drift and quality monitoring, set up the retraining pipeline, and keep the model accurate as your data moves — with a hand-off your own team can operate.
What is machine learning development?
Machine learning development is building software that learns a pattern from your data and makes a prediction — a forecast, a score, a recommendation, an anomaly flag — then deploying and maintaining it. The work is mostly data engineering, validation, and operations; the algorithm is the small part. The aim is a model that feeds a real decision and stays accurate, not a notebook with good numbers.
How is machine learning different from generative AI or an LLM?
A large language model is a general model trained by someone else that produces text. Custom machine learning is a model we train on your data to predict a specific thing — who will churn, what to recommend, which transaction is fraud. For most prediction and forecasting problems a focused trained model is cheaper, faster, more accurate, and far easier to explain than asking an LLM to guess.
How much data do we need to train a custom model?
It depends on the problem, but the honest answer is usually 'more labelled examples than you think, and cleaner'. For many classification and forecasting tasks a few thousand well-labelled records is a workable start; rare-event detection needs more. The Discovery Sprint exists to tell you whether your data can support a model before you spend on one — if it can't, the first project is fixing the data.
How long does it take to build and deploy an ML model?
A common path is a 2-week Discovery Sprint, a 6–10 week build for a first production model, and a staged rollout. Banao's ~300-engineer bench means delivery starts in weeks, not the months a fresh hire would take. A clean dataset and a clear decision shorten it; a data clean-up first extends it — we tell you which you're in during the Sprint.
What's the difference between a model in a notebook and one in production?
A notebook model runs once, on clean data, with a person watching. A production model runs every day on messy live data, at the latency your systems need, feeding a real decision, with no one watching each call. The gap — pipelines, serving, validation, monitoring, retraining — is where most of the engineering goes and where most pilots that 'worked' fall over.
How do you stop a model from getting worse over time?
We assume it will. A model is most accurate the day it ships and drifts as your data changes. We monitor the distribution of inputs and the quality of predictions, alert when either slips, and build a retraining pipeline so a refresh is a routine run rather than a project. Every release is versioned and reversible, so a bad new model rolls back in minutes.
Can you build on our existing data and cloud?
Yes. We work with the data and warehouse you already have, build the feature pipelines as part of the project, and deploy into your cloud — AWS, Azure, or GCP — so the model runs where your data and your governance already live. We keep data inside the region your policy or regulation requires: UAE, Saudi Arabia, UK, US, or India.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, a scoped model design, an honest read on whether your data supports it, and an ROI model you keep whether or not you continue. Worst case you have a free assessment that saves you a doomed build; best case you have your board business case.
Do you only build the model, or maintain it too?
Both, and the maintenance is the point. We build the model and the MLOps around it — monitoring, retraining, versioned releases — and hand off so your team can operate it. A model with no one maintaining it degrades quietly; we'd rather build one your engineers can keep accurate than one that needs us on retainer to stay alive.
Which use cases and industries do you build ML for?
Forecasting, recommendation, ranking, classification, scoring, and anomaly or fraud detection — across retail and e-commerce, financial services, manufacturing, healthcare, and logistics. We've built production-line anomaly detection with RAK Ceramics and run our own hiring on a scoring model. If a decision repeats often enough to learn from, it is usually a candidate.
Bring the decision you make most often — or the forecast you most wish were better. In 45 minutes we'll tell you whether your data can support a model, and what it would take to put one in production.
Book a 45-min scoping call
