Any business already has the raw material for machine learning algorithms: orders, questions, claims, sensor data, support tickets, and returns. It often sits in separate databases and only shows up in monthly reports. A good model can learn from your data to predict future sales, detect potentially fraudulent transactions, prioritize leads, or prevent machine breakdowns.
PenguinPeak Technologies is a machine learning development services provider that develops models for companies operating in the USA, Australia, India, New Zealand, and Abu Dhabi. We develop each machine learning model for a specific task and test it on your data before deployment. We continue monitoring the models after their deployment because even an accurate model in March may fail by September.
Demand, revenue, churn, staffing, and machine failures. Our forecasts include a confidence interval, not a single estimate, so your planners can gauge the model's uncertainty.
Lead scores, credit risk scores, claim prioritization, and support ticket routing. Every score comes with key rationale behind it, for you to act on and communicate.
Product recommendations and next-best action based on customer behavior, integrated with your Shopify, Magento, or custom storefronts so recommendations pop up at checkout.
We classify, extract entities, analyze sentiment, and transcribe voice using classification, entity extraction, sentiment analysis, and speech-to-text.
If an image, sound, or sequence is too complex for other approaches to handle, we build and train neural networks. We apply neural networks where they justify their cost, and not just as a matter of course.
Clusters, lifetime value, and churn models identify the most valuable customers and how to approach them. We can use insights from this process directly in your SEO, PPC, and social media campaigns.
Not sure if machine learning is right for you? Our assessment starts with your data and problem, and we tell you straightforwardly whether you need a machine learning model, basic analytics, or a rules-based solution. This process follows our AI strategy and roadmap.
You have past instances whose results you know, and you wish to make predictions about future instances. Supervised learning works well for predicting sales, lead scores, identifying fraud, and prioritizing claims. Popular techniques are regression analysis, random forests, and gradient boosting.
You have lots of data but no labels, and you want to discover patterns. This type of machine learning works well for customer segmentation, anomaly detection, and product grouping. Techniques used include clustering and anomaly detection.
Your data is ordered and sequential, such as demand, energy consumption, machine readings, or web traffic.
Your data is images, sound, video, or text with complex patterns that simple techniques can't identify. These techniques enable image processing, voice recognition, and text recognition, and they rely on neural networks.
You want to match people with products, content, or offers based on what similar customers have done.
A system learns by trial and feedback inside a simulation or controlled setting. It can help with pricing, scheduling, and routing, though far fewer business problems truly need it, and we will tell you if yours does not. Not sure which applies? Our feasibility review matches your question and your data to the right approach before any model is built.
Describe your data to us along with the problem that you need solved. We'll tell you how much a model can predict, what it will take, and whether it makes sense. Book your free data and feasibility review now.
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Machine learning development builds software that learns patterns from your past data to make decisions, rather than following predetermined instructions. It is not just about picking an algorithm. It includes data preparation, modeling and testing, deploying the model in your environment, and monitoring after deployment. Most failures come down to poor-quality data and a lack of a strategy to keep the model up to date.
Preliminary research and development of a baseline model will take a couple of weeks. A deployment-ready system will take a couple of months.
Input data and prediction performance change as the real world evolves, such as changes in customer behavior, prices, and aging hardware and software. This leads to accuracy deterioration without any indication of error, called drift. We monitor data input and prediction performance, raise alarms when either falls outside acceptable limits, and plan periodic retraining. In addition, you will receive periodic reports on accuracy, costs, and business impact to measure the effectiveness of your model.
We use access controls, encryption, and private or in-region deployment where needed. We design to the rules that apply to you, such as HIPAA and CCPA in the US, the Privacy Act in Australia and New Zealand, and the UAE PDPL and ADGM Data Protection Regulations in Abu Dhabi.
Use machine learning when you have sufficient historical data, the decision is repeated frequently, and you can verify the forecast’s accuracy. Machine learning isn’t applicable when historical data is limited, business rules are well understood, or the consequences of being wrong are costly and hard to measure. In those cases, a simple spreadsheet, rules engine, or traditional analytics tools often work better, and we recommend them.
We use access controls, encryption, and private or regional hosting when required. We build to all relevant privacy regulations for your industry, such as HIPAA & CCPA in the USA; the Privacy Act in Australia and New Zealand; the UAE PDPL and ADGM Data Protection Regulations in Abu Dhabi.
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