Do we need a large dataset to get started?

It depends on the problem. Some models work with a few thousand records; others need millions. We assess your data in the first week and tell you honestly whether there's enough signal to build something useful.

How do you handle data privacy?

We sign DPAs and work under GDPR, CCPA, and HIPAA-compatible frameworks. Model training can be done entirely in your cloud environment so your data never leaves your infrastructure.

Can you improve an existing ML model we already have?

Yes. We audit the existing model, identify the main sources of error or drift, and propose improvements, whether that's feature engineering, retraining, or replacing the architecture entirely.

How long does it take to build and deploy a model?

For a well-defined problem with clean data: 6 to 10 weeks. For complex systems with messy data and multiple components: 12 to 20 weeks. We set a realistic timeline after the data audit.

What's MLOps and do we need it?

MLOps is the infrastructure that keeps a model working after launch: automated retraining, monitoring for data drift, version control for models and data. If you're deploying a model that needs to stay accurate over time, you need it.

Do you use LLMs like GPT?

When appropriate. For NLP tasks, fine-tuned language models are often the right tool. For structured data problems like churn prediction or demand forecasting, classical ML usually outperforms and is much cheaper to run.

Who owns the models we build together?

You do. The trained weights, training code, data pipelines, and all documentation transfer to you on delivery.

AI & ML Services

Real machine learning. Not just a chatbot wrapper.

We build predictive models, forecasting systems, recommendation engines, and computer vision pipelines, trained on your data, deployed in your infrastructure.

Talk to Our ML Team Book a 30-min Call

Free, no commitment. Takes about 30 seconds.

6 wksMedian to production

40%Avg manual work cut

3 daysFirst engineer shortlist

What we build

ML systems that produce business outcomes, not demos.

Most companies claiming AI expertise are wrapping GPT APIs with a UI. We build actual machine learning, custom models trained on your data, statistical systems that produce measurable predictions, and data pipelines that keep them accurate over time. Our ML engineers have shipped production systems in fintech, e-commerce, healthcare, and logistics.

Engineers placed by EnzRossi, running production systems

Core capabilities

What we actually do.

Predictive analytics

Train models on historical business data to predict future outcomes: sales volume, inventory demand, user conversion, risk scores.

Churn prediction

Identify customers with a high probability of leaving before they do. We build, train, and deploy classification models connected to your CRM.

Demand forecasting

Time-series forecasting models for supply chain, staffing, and resource planning. We handle seasonality, trend decomposition, and anomaly correction.

Recommendation engines

Collaborative filtering, content-based, and hybrid recommendation systems for e-commerce, media, and SaaS products.

Anomaly detection

Unsupervised models that flag unusual behavior in financial transactions, system metrics, sensor data, or user activity.

Computer vision

Image classification, object detection, OCR, and visual quality inspection using CNNs, YOLO, and custom architectures.

NLP pipelines

Sentiment analysis, named entity recognition, document classification, and information extraction at scale, using both classical NLP and fine-tuned language models.

Custom model training and MLOps

End-to-end: data cleaning, feature engineering, model training, evaluation, A/B testing, and deployment with monitoring for data drift.

Our stack

The tools we use to build production ML.

PyTorchTensorFlowscikit-learnXGBoostLightGBMCatBoostKerasHugging Face TransformersspaCyNLTKOpenAI APILangChainBERTGPT fine-tuningOpenCVYOLOv8Detectron2TorchvisionPILalbumentationsApache SparkAirflowdbtKafkaFlinkPandasPolarsDuckDBMLflowWeights & BiasesDVCBentoMLSeldonRayKubeflowAWS SageMakerGCP Vertex AIAzure MLS3BigQuerySnowflake

Real applications

What clients actually use this for.

Use case 01

E-commerce recommendation engine

Built a product recommendation system that increased average order value by 23% by surfacing relevant products based on browsing history, purchase patterns, and category affinity.

Use case 02

SaaS churn prediction

Deployed a churn model that scores every active account weekly. The sales team uses it to prioritize at-risk accounts for outreach, reducing monthly churn from 4.2% to 2.8%.

Use case 03

Logistics demand forecasting

Time-series model for a 3PL operator predicting daily inbound volume per warehouse. Reduced overstaffing costs by 18% in the first quarter post-deployment.

Use case 04

Visual quality control

Computer vision system for a manufacturing client that detects surface defects on products using a camera line. Replaced a manual inspection process with 99.1% accuracy.

Our process

From raw data to production model in structured phases.

enzrossi-ai, process

$ step.01

$ step.02

$ step.03

$ step.04

$ step.05

step 01

Data audit and problem framing

We assess your data quality, volume, and structure. We define the ML problem formally: what are we predicting, with what input, and how do we measure success.

step 02

Data pipeline and feature engineering

Clean, transform, and structure your data for model training. Build repeatable pipelines that keep the model up to date as new data comes in.

step 03

Model development and evaluation

Train multiple model architectures, evaluate on hold-out sets, and tune for your specific business constraints (precision vs. recall, latency vs. accuracy).

step 04

Deployment and integration

Deploy as a REST API, batch job, or embedded service. Integrate with your existing systems, CRM, data warehouse, application layer.

step 05

Monitoring and retraining

Set up data drift detection and model performance monitoring. Schedule retraining when the model's accuracy degrades past your threshold.

Engineers behind these numbers

AI Engineer

ML Engineer

Data Scientist

Full-stack Dev

Track record

Numbers from production deployments.

23%

Avg. AOV increase from rec engines

94%

Model accuracy on test sets

18%

Cost reduction from demand forecasting

99.1%

Defect detection accuracy (CV)

Before you decide

The doubts everyone has before hiring us.

And what we do about each one.

AI projects never make it past the prototype.
Your data is assessed in the first week, and you get an honest answer on whether there is enough signal to build something useful.
Data assessment in week one
This will just be a chatbot on top of an API.
Language models are used when they fit, like text and language tasks.
The right tool for the problem
Our data is too sensitive to leave our infrastructure.
Model training can run entirely in your cloud, so your data never leaves your infrastructure.
Training in your cloud
We'll end up locked in to you.
Trained weights, training code, data pipelines and all documentation transfer to you on delivery.
Models and code are yours

FAQ

Questions about AI & ML services

Ready to build?

Let's look at your data.

Book a technical call with our ML team. We'll review your data situation and tell you what's realistically buildable in the next 90 days.