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Machine Learning Development

Machine learning for prediction problems ChatGPT can't solve.

We train custom models on your own data for fraud detection, churn, demand forecasting, and recommendations, prediction problems a language model was never built to handle.

What We Build
Trusted by businesses worldwide
85%Of ML projects fail, mostly on data quality
35%Of Amazon's revenue tied to recommendations
4–16 weeksPrototype to production timeline
100%Model & code ownership
Overview

What is machine learning development?

Machine learning development is training a model on your own historical data to predict, classify, or score an outcome, rather than connecting to a general-purpose AI model. A fraud-detection model learns your business's actual fraud patterns; a churn model learns what your specific customers do before they leave. That's a fundamentally different job from integrating ChatGPT, which draws on general language patterns, not your operational history.

Roughly 85% of machine learning projects fail, and poor data quality is the leading cause, not weak modeling technique. Basic ML solutions run under $100,000; advanced, large-scale initiatives can exceed $1,000,000, and the gap between those numbers is almost always data readiness and integration complexity, not the algorithm chosen.

01
Data audit first
Feasibility checked before a single model gets built.
02
Right-sized models
Gradient boosting where an LLM would be slower and costlier.
03
Drift monitoring
Retraining scheduled to how fast your data actually changes.
04
Explainable output
Predictions you can defend, not a black-box score.

Large language model, or custom machine learning?

These solve different problems. Picking the wrong one costs accuracy, money, or both.

ApproachWhat it's built forBest fit
LLM / ChatGPTLanguage tasks: conversation, summarization, answering questions from documents.Chatbots, support automation, content generation.
Custom Machine LearningStructured prediction: a number or category learned from your own historical data.Fraud risk, churn probability, demand forecasting, ranking.

Machine learning systems we build most often.

A common and costly 2026 mistake is reaching for an LLM on a structured prediction problem a lightweight gradient-boosting model would solve more accurately, more explainably, and at a fraction of the inference cost.

01

Fraud & anomaly detection

Models that flag suspicious transactions or behavior in milliseconds, learned from your own historical patterns.

02

Churn prediction

Scoring models that flag at-risk customers before they leave, so retention teams act early, not after the fact.

03

Demand & revenue forecasting

Time-series models for inventory, staffing, and revenue planning, tuned to your business's actual seasonality.

04

Recommendation systems

Ranking and recommendation models that surface the right product or content to the right user, at scale.

05

Predictive maintenance

Models that flag equipment likely to fail, trained on sensor and operational data, not fixed maintenance schedules.

06

Computer vision & classification

Image-based defect detection, document classification, and visual quality control models.

The Real Risk

Why we audit data before we model anything.

Model architecture rarely kills a machine learning project. The data underneath it does. We run a data audit and feasibility check before any model gets built, that step catches most of the projects that would otherwise fail four months in.

85% of ML projects fail
The failure rate across the industry, not a worst case.
Poor data quality is #1
The leading cause of failure, ahead of weak modeling technique.
12–18 months to strong ROI
The typical window before returns compound meaningfully.
Models degrade over time
Real-world data drifts from what the model was trained on.
Feasibility checked first
Whether your data can actually support the prediction you want.
Retraining planned in
A schedule matched to how fast your data changes, not a fixed cadence.

A clear path from raw data to production model.

Four stages, with feasibility proven before anything gets committed to a full build.

01

Data audit & feasibility

We assess whether your data can actually support the prediction, before committing to a build.

1–2 weeks · Audit
02

Feature engineering

We clean, label, and engineer features from raw data so the model learns from real signal, not noise.

1–3 weeks · Preparation
03

Model training & prototype

We train and evaluate candidate models, then hand you a working prototype proving the approach works.

2–6 weeks · Prototype
04

Deployment & monitoring

We deploy to production and monitor for data drift, retraining on a schedule that matches your data.

2–5 weeks · Deployment
Our Stack

The machine learning technology we use.

Production-grade tools, chosen for accuracy, explainability, and cost efficiency.

Core & Classical ML
Pythonscikit-learnXGBoostLightGBMPandas
Deep Learning
PyTorchTensorFlow
MLOps & Deployment
MLflowAWS SageMakerDocker

Proof, not promises.

Engineering built on real data, not a pitch deck.

All Case Studies
  • Data
  • Full-Stack
  • Marketplace
Client
BeesApp
Industry
Retail Tech
Location
Saudi Arabia
Timeline
6 Months

99.9%

Automated, Data-Driven Platform

BeesApp needed automated inventory decisions across a multi-vendor marketplace, the same data-pipeline discipline that any reliable machine learning system depends on. We rebuilt their platform on Next.js and FastAPI with 120+ API endpoints and real-time data flow, the operational foundation every ML deployment needs before a model can be trusted with a decision.

99.9%

Uptime

120+

API endpoints

40%

Lower server costs

BeesApp platform rebuild
Read Full Case Study

Explore more software services.

Machine learning is one of the more specific builds inside our broader AI development work.

AI Development

The full range of AI systems we build.

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Predictive Analytics

Forecasting applied to business planning decisions.

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ChatGPT Integration

Conversational AI for your product.

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Data & Analytics

The pipelines every ML model runs on.

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Cloud SaaS Apps

Infrastructure that scales ML workloads.

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API Integrations

Connect model outputs into the systems that act on them.

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Custom Software

Bespoke builds, MVPs & enterprise systems.

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Cloud & DevOps

Deployment pipelines and monitoring for model serving.

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FAQ

Machine learning questions

The things clients ask us most before starting a machine learning build.

Machine learning development is training a model on your own historical data to predict, classify, or score an outcome - fraud, churn, demand, or ranking - rather than connecting to a general-purpose AI model. The model learns patterns specific to your business instead of general language patterns.

Use a large language model if the task involves language: answering questions, summarizing, or holding a conversation. Use custom machine learning if the task involves predicting a number or category from structured data, such as fraud risk, churn probability, or demand. A common and costly mistake in 2026 is reaching for an LLM on a structured prediction problem a lightweight model would solve more accurately and far more cheaply.

Roughly 85% of machine learning projects fail, and poor data quality is the leading cause, not weak modeling technique. We audit data quality and feasibility before any model gets built, so a project doesn't reach month four before someone discovers the data can't support it.

Basic ML solutions run under $100,000; advanced, large-scale, or highly custom initiatives can exceed $1,000,000. Cost depends on data readiness, model complexity, and infrastructure needs. We scope your specific use case before quoting a fixed price.

A working prototype takes 4 to 8 weeks, and a full production deployment typically takes 8 to 16 weeks, depending on data availability and integration complexity.

Fraud detection, churn prediction, demand forecasting, recommendation systems, and predictive maintenance are the strongest fits. Amazon attributes up to 35% of its revenue to recommendation systems alone, which reflects how much value well-built prediction models can return.

Yes. Models degrade over time as real-world data drifts from what they were trained on, so we monitor performance and retrain on a schedule that matches how fast your data actually changes.

Yes. You own 100% of the trained model, code, and training pipeline in documented repositories, with no dependency on us to retrain or operate it.