AI Agents & Data

An AI development company for machine learning that reaches production

We build forecasting, prediction and computer vision models that plug into the systems your team already uses. Then we keep them accurate after launch.

Talk to an engineer What we do

AI Agents & Data

01Demand and sales forecasting
02Predictive models
03Computer vision
04Text and document classification
05Anomaly detection
06MLOps and deployment
07Model audits and rescue

Built withPythonscikit-learnXGBoostLightGBMPyTorch

At a glance

Machine learning development is the work of turning your historical data into a model that improves one business decision, such as a demand forecast, a risk score or a defect check on a production line. It suits mid-sized companies that already have the data. Tag4 Technology Solutions builds the model, ships it behind an API and sets up the monitoring and retraining that keep it accurate.

Updated September 2026

Best for: Mid-sized companies with years of orders, sensor readings or ticket data
Typical timeline: 4–8 week proof of concept, then 2–4 months to production
Typical budget: $25,000–$75,000 for a proof of concept
Core technologies: Python, scikit-learn, XGBoost, PyTorch, MLflow, Azure ML, SageMaker
What you get: A deployed model, feature pipeline, drift monitoring and a retraining schedule
Ownership: Code, trained weights and pipelines live in your cloud account

Most companies that call an AI development company already have the data. Years of orders, sensor readings, support tickets or inspection photos sit in a database, and someone suspects there's money hidden in the patterns. Our machine learning development services are for that moment. We usually work with operations, finance and product leaders at mid-sized US companies who want a model that improves one specific decision, like how much stock to order or which machine to service next week.

The algorithm is rarely what sinks these projects. They stall because the training data doesn't look like what the model sees in production, or because nobody agreed on what accuracy is good enough before work started. Plenty of models also die in a data scientist's notebook with no path into the app that needs the prediction. And we regularly see teams reach for deep learning when a gradient-boosted model on clean features would be cheaper to run and far easier to explain to an auditor.

So we start with the decision, then look at the data. Before training anything, we set a baseline (often the spreadsheet rule your planners use today) and the metric a model has to beat to be worth deploying. From there we build the feature pipeline, compare a few candidate models, and ship the winner behind an API. MLOps isn't a phase we bolt on later. Versioned datasets, reproducible training runs, drift monitoring and a retraining schedule are part of the first release.

A proof of concept on data you already have typically takes 4–8 weeks. Getting that model into production, wired into your ERP or app with monitoring, is usually another 2–4 months. Sometimes the honest answer is that a simple rule or a better report solves the problem. If that's the case, you'll hear it from us in the first few weeks, not after the budget is gone.

What we do

AI & Machine Learning services

Demand and sales forecasting

SKU, store or region-level forecasts that account for seasonality, promotions and lead times, delivered into the planning tools your buyers already open every morning.

Predictive models

Churn risk, late-payment risk, equipment failure, lead scoring. We pick the simplest model that meets the target and show which inputs drive each prediction.

Computer vision

Defect detection on production lines, counting and measuring from camera feeds, and reading labels or gauges. We run on edge devices when latency or connectivity demands it.

Text and document classification

Routing support tickets, tagging claims, sorting incoming documents by type. Often a small fine-tuned model beats a large general one on cost and speed here.

Anomaly detection

Flagging unusual transactions, sensor readings or usage spikes so a person looks at the handful of cases that matter instead of scrolling through thousands.

MLOps and deployment

Feature pipelines, model registry, CI for training code, containerized serving and drift alerts, so retraining is a routine job rather than a rescue project.

Model audits and rescue

Already have a model that underperforms or that nobody can retrain? We review the data, the evaluation method and the deployment, then fix what's broken.

How we work

What you can expect from us

Baseline before model

Every project starts by measuring the current method. If a model can't clearly beat it on your own historical data, we don't recommend building one.

Built for production from week one

We write training code as software, with tests and version control, so the path from experiment to a running service is short and repeatable.

Explainable where it counts

For credit, pricing or safety decisions we favor models whose reasoning you can inspect, and we add SHAP-style explanations your reviewers can read.

You own all of it

Code, trained weights, pipelines and documentation live in your cloud account and your repositories. Nothing is locked behind a platform fee of ours.

Tools and platforms

Python
scikit-learn
XGBoost
LightGBM
PyTorch
MLflow
Azure Machine Learning
Amazon SageMaker
Databricks
ONNX Runtime
FastAPI
Docker
Evidently

Process

From first call to production

Discovery
A few working sessions to pin down goals, users, constraints and what "done" means. You get a written scope and estimate.
Design
Architecture, data model and clickable UI before heavy coding starts, so surprises show up early and cheaply.
Build
Two-week sprints with a demo at the end of each one. You see working software, not status reports.
Launch
Automated tests, staged rollout, monitoring and a rollback plan. Go-live should be boring.
Support
Bug fixes, updates and new features on a monthly plan, or a clean handover to your team.

FAQ

AI & Machine Learning: common questions

How much data do we need to start a machine learning project?

Less than most people expect, but it has to be relevant. For forecasting, two or more years of history that covers seasonal cycles is a good start. For classification, a few thousand labeled examples often works, and we can help you label more if needed. We'll check your data in the first week and tell you if it's enough.

How much does a machine learning project cost?

A proof of concept usually falls somewhere between $25,000 and $75,000, depending on data preparation. Production deployment with integration and monitoring typically adds a similar or larger amount. Ongoing cloud costs for most tabular models are small, while computer vision and large models cost more to run.

Should we use machine learning or generative AI?

Use classic machine learning when you want a number or a category from structured data, like a forecast or a risk score. Use generative AI when the input or output is language, like answering questions over documents or drafting replies. Many real systems use both, and we build either.

What happens when the model gets less accurate over time?

That is expected, because customer behavior and supply conditions change. We set up drift monitoring that compares live inputs and outcomes to the training data, and a scheduled retraining job. When accuracy drops below the agreed threshold, you get an alert before it hurts decisions.

Can you work with our existing data science team?

Yes, and it's a common setup. Often your team owns the modeling and we build the pipelines, deployment and monitoring around it. We can also pair on the modeling work and hand it over with documentation.

Industries we build for

Planning a ai & machine learning project?

Tell us what you are trying to build. An engineer reads every message and replies personally. No sales sequences, no pressure.

Start a conversation sales@tag4tech.com