Custom ML Model Development
Most Enterprise ML Models Never Reach Production. We Build the Ones That Do.
The model hits its accuracy target in the notebook, the demo impresses the room, and then it stalls—no deployment path, no monitoring, no one sure it still works six months later. Banao builds and operates custom ML models as production systems: instrumented, governed, and retrained on live data. We run the same discipline on our own 300-person operation, where ML scores every candidate through InterviewGod and drives demand generation through Vikaas before we ship it to you.
The first call is free · 45 minutes · no obligation
What we deliver
Why Models Stall Before Production
The gap between a working model and a production system isn't more data science—it's engineering. A deployed model needs an inference path, drift detection, an evaluation harness, and a retraining loop, or its accuracy decays silently until a business metric moves the wrong way and no one knows why. Most teams are staffed to build models, not to operate them. Banao treats every model as a monitored, governed system from day one—the same way we run InterviewGod and Vikaas across our own 300 engineers, where a model that drifts is our problem before it is ever yours.
AutoML for Rapid Prototyping
Stand up AutoML pipelines that compress data prep, model selection, and hyperparameter tuning—so you can prove whether a use case is worth building in weeks, not quarters.
Custom Model Development
Models built for your domain—classification, regression, forecasting, recommendation, anomaly detection—for when off-the-shelf accuracy isn't enough to trust the output.
Feature Engineering & Data Pipelines
Engineer the features that actually move accuracy, on versioned pipelines that feed training and inference the same data—closing the train/serve skew that quietly breaks models in production.
Validation & Explainability
Cross-validation, bias and fairness checks, and explainability—so stakeholders and regulators can see why a model made a decision, not just what it predicted.
Deployment Pipelines & MLOps
CI/CD for models: automated deployment, versioning, and rollback—so a new model ships, or reverts, without a fire drill.
Monitoring & Drift Detection
Telemetry on every prediction, drift detection on inputs and outputs, and automated retraining—so accuracy is watched continuously, not discovered after it fails.
Edge & On-Device Inference
Quantize and optimize models to run inference on mobile, IoT, and edge hardware—real-time predictions where round-tripping to the cloud isn't an option.
Integration Into Your Stack
Expose models behind governed, secured APIs and wire predictions into the systems your teams already use—so the model reaches the decision, not a dashboard no one opens.
How we deliver
How We Take Models to Production
- 01
Discovery & Problem Definition
We start from the business metric you're accountable for—churn, default rate, forecast error—and work backward to the model. We confirm the data exists, define how accuracy will be measured, and agree what 'good enough to deploy' means before any modeling begins.
- 02
Data Preparation & Feature Engineering
We clean, normalize, and version your data, then engineer the features that drive accuracy. One pipeline feeds both training and production inference, so the model sees in production exactly what it saw in training.
- 03
Model Selection, Training & Tuning
We evaluate AutoML and custom architectures against your accuracy target—regression, classification, deep learning—and tune for the metric that matters, not leaderboard scores. You see the trade-offs, not just a final number.
- 04
Validation & Explainability
Every model is validated on held-out, real-world data with bias and fairness checks. We instrument explainability so stakeholders can see the reasoning behind a prediction and sign off with confidence.
- 05
Deployment & Integration
We deploy behind governed APIs into your cloud or on-premise stack and wire predictions into the systems your teams use—so the model reaches the decision, not a report.
- 06
Monitoring & Continuous Improvement
After launch we monitor every prediction, detect drift on inputs and outputs, and retrain on new data automatically. Accuracy is maintained as a system, not audited once a year.
Recent work
Recent Work
Indian Oil Corporation runs one of the world's largest fuel distribution networks—and was managing it on lagging reports and manual forecasts. Banao built an AI-powered supply chain intelligence platform: demand and risk prediction, incident escalation, and crisis simulation over live operational data. It now tracks and analyzes 1,200+ supply chain incidents and surfaces 350+ AI-driven planning recommendations, with scenario simulations for crisis readiness.
Client reviews
What Enterprise Teams Tell Us
“Our last two models never left the notebook. Banao deployed a custom risk model into our stack with monitoring and retraining built in—six months on, it's still holding its accuracy bar.”
“They used AutoML to prove the use case in three weeks, then hardened it into a production service. We didn't pay for custom engineering until the model earned it.”
FAQ
Frequently asked questions
Why do machine learning models fail to reach production?
Usually not the model—the engineering around it. Without an inference path, drift monitoring, and a retraining loop, a model that tested well decays silently in production. We build those from day one, so the model that passes validation is the one that runs reliably.
AutoML or custom model development—which do we need?
Both, in sequence. We use AutoML to prototype and prove a use case in weeks, then build a custom model where domain accuracy and control justify it. You don't pay for custom engineering until the use case earns it.
How do you monitor models for drift after deployment?
We instrument every prediction with telemetry, track drift on both inputs and outputs, and trigger retraining when accuracy degrades past a threshold you set. You see model health on a dashboard, not in a postmortem.
Can you deploy models into our existing cloud or on-premise stack?
Yes. We deploy behind governed, secured APIs in your environment—AWS, GCP, Azure, or on-premise—and integrate with your data pipelines and applications. Your data and models stay in your infrastructure.
How do you keep models explainable and unbiased?
We run bias and fairness checks during validation and integrate explainability so you can see why a model made each decision—what lets risk, compliance, and leadership approve it for production use.
What's the timeline, and what if the model underperforms?
An AutoML prototype runs in 2–4 weeks; a custom model in production typically takes 1–3 months. We define the accuracy bar before we build, so underperformance surfaces at validation—not after deployment. You decide to scale only once the model clears the bar on your data.