AI and ML services that put models into production Machine learning trained on your data, deployed, monitored and kept accurate
Machine learning services for the decisions you make often Models that forecast, classify, read and see, built on your data
How our AI and ML development works From a data audit to a model that runs, and stays accurate
- Week 1Audit the dataWe check its quality, volume and access, and whether a model can learn from it.
- Week 2Define the use caseOne decision, the number it moves, and the accuracy it needs to be useful.
- Weeks 3 to 6Train and testWe try several models, test them on data they haven't seen, and pick the best.
- Weeks 7 to 8Integrate and deployThe model goes into the system where the decision is made, behind an API.
- Every weekMonitor and retrainWe watch accuracy and drift, and retrain when the data changes.
When machine learning services are the right fit And when a rule, a report or a language model will do
A good fit when
- You make the same decision often, and history shows what worked
- You have years of records, such as sales, claims or sensor readings
- A small gain in accuracy is worth a lot of money
Look elsewhere when
- A written rule already makes the decision well
- You need people to see today's numbers: build a dashboard
- There's little history to learn from yet
Our AI and ML capabilities in detail What each one does, and what you have when it's in production
Machine learning model development
We design, train and test models for decisions you make often: what to order, which claim to check, which customer might leave. Each model is compared with what you do today, so you can see whether it's better before you rely on it.
Predictive analytics and forecasting
Forecasts are the most common use of machine learning in business. We build them for demand, cash, staffing and failures, and show a range as well as a number, so planners know how far to trust it.
- Demand forecasts by item, store or region
- Churn and fraud scores
- Predictive maintenance for machines and fleets
NLP and computer vision systems
Natural language processing reads text: it sorts emails, pulls fields from contracts, and routes claims to the right team. Computer vision reads images: it spots defects on a line, counts stock on a shelf, and estimates damage from a photo.
Both work best on a narrow, well-defined job, with a person checking the cases the model isn't sure about.
LLM fine-tuning and prompt engineering
Large language models work out of the box for general tasks. For your domain, we test them on your real cases, write and refine the prompts, and fine-tune on your data when prompting isn't enough.
We work with OpenAI, Anthropic Claude, Google Vertex AI, AWS SageMaker, Azure Machine Learning and Hugging Face, and pick the one that fits.
MLOps and model monitoring
A model is only useful while it stays accurate. Our MLOps pipelines version every model and dataset, deploy through tests, and watch each model's accuracy, speed and cost in production.
When the data drifts, after a price change or a new product, the pipeline flags it and we retrain.
Secure data operations and compliance
Training data often holds personal or regulated information. We keep it in your cloud account, control who can see it, and use synthetic or masked data where the real data can't be used. We design for the rules you work under, such as GDPR and HIPAA.
Questions about AI and ML services What buyers ask us before they build a model
What business problems can AI and machine learning services solve?
Decisions you make often with data behind them: predicting demand, spotting fraud, flagging customers likely to leave, scheduling maintenance, and reading documents or images. If history shows what worked, a model can usually learn from it.
How do I know if my data is ready for AI?
A short data audit tells you. We check its quality, volume, access and compliance, and say whether a model can learn from it now or what needs cleaning first.
Which AI and ML platforms do you work with?
OpenAI, Anthropic Claude, Google Vertex AI, AWS SageMaker, Azure Machine Learning and Hugging Face, among others. We pick the platform that fits your cloud, your data and your budget.
How do you handle data privacy and AI compliance?
Data stays in your cloud account, access is set by role, and every model's decisions can be explained. We design for the rules you work under, such as GDPR and HIPAA.
How long does it take to go from proof of concept to production?
It depends on the data and the integration. We plan for production from the first week, so the proof of concept is built to be deployed, not thrown away.
Bring the decision you make every week
We'll check whether your data can predict it, and how accurate a model could be.