Predictive Analytics Consulting Services
for Smart Machine Manufacturers

Trusted Predictive Analytics consulting partner for manufacturers

Machine performance or condition monitoring solutions remain largely reactive in nature until you add predictive modelling capabilities that can help forecast failures, throughput, defects etc. This needs augmenting historical machine, process, and other environmental data, modeling with the right algorithms, deploying and tuning it to generate accurate predictions.

At Saviant, our predictive analytics consulting and machine learning implementation teams help smart instruments and machines manufacturers to build custom solutions, with real-time & historical data. Their solutions are equipped with predictive & AI capabilities, to enable fault-free operations for business continuity of their Enterprise clients.

For example, their predictive analytics solutions can now:

Predict events with more than 90% accuracy

through timely alerts and alarms across operations, thereby preventing downtime events or failures.

Perform root-cause analysis & anomaly detection

using the identified patterns and correlations from real-time & historical data of machine and operations.

Provide faster decision-making capabilities

by predictive & advanced analytics solutions with customer’s real-time systems.

Enable intelligent insights on machine failure

as well as anomalies on the web and mobile apps to take corrective actions in real-time.

What we do - our service offerings

As machine manufacturers try to address industry challenges amid dynamic market conditions and latest technology trends, we help them accelerate their product development life cycle operations to build a resilient core, thereby speeding up time-to-market. Apart from helping in new product development initiatives, our teams also assist them in product migration and re-engineering their products to meet dynamic market conditions in terms of features, cost, and performance.

Product engineering & development

Starting from the innovation phase to product deployment and testing phase, we help you in complete product lifecycle. Whether it is cloud & mobility enablement, or adding intelligence to your existing products, our teams deliver all you need.

Product re-engineering

Scale-up your existing products to ensure tech readiness for the next 10 years. We help in re-architecting, re-factoring, or re-platforming existing technology stack and architecture, while enhancing/maintaining the present capabilities of your products, for scalability, security, and performance.

Continuous integration and deployment

We build DevOps strategies and CI/CD pipelines for new product development, deployment, and its continuous updates; through consulting, end-to-end implementation & infrastructure monitoring using DevOps tools like Maven, or Jira.

Product migration – Cloud, On-premises & mobile

Whether it is migrating your existing products to cloud, creating a hybrid structure, or extending your web-based products to mobile, our teams can help you.

Product testing & validation

We test and operate various products in a reliable way. Our teams leverage technology advancements for delivering quality-centric validation & verification services, to ensure your products make lasting impression.

Data platform & analytics

Extend your product capabilities with a combination of data analytics, ML-AI, and data mining, to deliver advanced products. Accelerate your product time-to-market using our proven industrial frameworks.

How we work with you – our approach

Our predictive analytics consulting & implementation teams follow a particular approach to build custom solutions and help achieve your goals, which includes:

1. Assessment or RA
of your data

Assessing what’s the data you are capturing, existing data sources, your objective to build a predictive analytics models, and determining what additional data is needed to create the use case.

2. Building machine learning models

Extracting, transforming, and labeling the required data. Shortlisting candidate ML models to build, test & evaluate them for feature engineering and hyper-parameter tuning.

3. Ongoing iterations and maintenance

Analyzing the results post-processing and comparing models’ behavior & hypothesis. Deploying Model as a Service and continually monitoring/retrain it to stay relevant and accurate.

4. Integration with the
rest of the system

Integrating the solution seamlessly with the existing system after the successful PoC implementation. Running it to check if the desired output has been achieved.