Solution Development


Avenir Digital use our skills in Applied AI, also known as Applied ML, to incorporate Machine Learning(ML) into our real-world business solutions.

Put simply, Applied AI is about creating a model – or prediction machine – that finds patterns in data and forecasts future outcomes from current observations.

The old IT axiom of garbage in/garbage out is nowhere more true than in Applied AI, where the quality and relevance of the data used to train the model is key to the accuracy of the model itself and its forecasts.

Avenir Digital have developed our own robust, tried and tested four-step process to combine conceptual business challenges with historical data, transforming them into applied machine learning models that solve real problems for our customers.

01

Invention, Hypothesis, Identify Data

02

Data Study

03

Proof of Concept Build

04

Minimum Viable Product

Minimum Viable Product


Traditionally, problem-solving required humans to design hypotheses and test them one-by-one in a painstakingly long and arduous process. Using Avenir Digital’s methodology, we can problem-solve at scale leveraging large data sets, cloud-based infrastructure, and the latest machine learning algorithms. All of the benefits of Applied AI, delivered rapidly and securely.

Avenir offer a comprehensive range of services for Applied AI, from developing solutions, to implementing them, and of course supporting them in a live operational environment.






Applied AI Benefits

From boosts in information availability and accuracy to cost savings and accurate decision-making, applied AI brings all kinds of advantages.

Rapid decision-making

Avenir Applied AI develops and uses human-like judgment to reduce errors and predict outcomes, achieving end-to-end process automation and enhancing smart device ecosystems.




Computer capability with human best practice

Avenir Applied AI bridges the gap between digital and physical worlds, combining the reliability and freedom from error of automation with observed and learnt human best practice.


Increased revenue

Applied AI increases profitability by identifying and helping solve complex business issues through its machine learning(ML) and adaptive capabilities.






Automation

Applied AI frees up employees from repetitive manual processes by increasing automation.

Efficiency

Applied AI improves efficiency and throughput, saving time and money.

Applied AI/ML Use Cases

Finance & FinTech ( AML, KYC) Cybr Security:

Fraud Detection: Leverage machine learning to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters and workflows. Lower your operational costs by limiting your exposure to fraudulent documents.
Insurance & InsurTech: Leverage machine learning to process underwriting submissions efficiently and profitably, quote optimal prices, manage claims effectively, and improve customer satisfaction while reducing costs. Detect your customer’s risk profile and provide the right plan.
Financial Analytics Platform: Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools.
Travel & expense management: Use deep learning to improve data extraction from receipts of all types including hotel, gas station, taxi, grocery receipts. Use anomaly detection and other approaches to identify fraud, non-compliant spending. Reduce approval workflows and processing costs per unit.
Credit Lending & Scoring: Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
Billing: Leverage accessible billing services that remind your customers to pay. Increase your loan recovery ratios. Use automated invoice systems for your business.
Robo-Advisory: Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
Regulatory Compliance: Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
Data Gathering: Use AI to efficiently gather external data such as sentiment and other market-related data. Wrangle data for your financial models and trading approaches.
Debt Collection: Leverage AI to ensure a compliant and efficient debt collection process. Effectively handle any dispute and see your success right in debt collection.






Operations

Robotic Process Automation (RPA) Implementation: Implementing RPA solutions requires effort. Suitable processes need to be identified. If a rules-based robot will be used, the robot needs to be programmed. Employees’ questions need to be answered. That is why most companies get some level of external help. Generally, outsourcing companies, consultants, and IT integrators are happy to provide temporary labor to undertake this effort.
Process Mining: Leverage AI algorithms to mine your processes and understand your actual processes in detail. Process mining can provide fastest time to insights about your as-is processes as demonstrated in case studies.
Predictive Maintenance: Predictively maintain your robots and other machinery to minimize disruptions to operations. Implement big data analytics to estimate the factors that are likely to impact your future cash flow. Optimize PP&E spending by gaining insight regarding the possible factors.
Manufacturing Analytics: Also called industrial analytics systems, these systems allow you to analyze your manufacturing process from production to logistics to save time, reduce cost, and increase efficiency. Keep your industry effectiveness at optimal levels.
Inventory & Supply Chain Optimization: Leverage machine learning to take your inventory& supply chain optimization to the next level. See the possible scenarios in different customer demands. Reduce your stock, keeping spending, and maximize your inventory turnover ratios. Increase your impact factor in the value chain.
Robotics: Factory floors are changing with programmable collaborative bots that can work next to employees to take over more repetitive tasks. Automate physical processes such as manufacturing or logistics with the help of advanced robotics. Increased your connected systems by centralizing the whole manufacturing process. Lower your exposures to human errors.
Collaborative Robot: Cobots provide a flexible method of automation. Cobots are flexible robots that learn by mimicking human workers’ behavior.
Cashierless Checkout: Self-checkout systems have many names. They are called cashierless, cashier-free, or automated checkout systems. They allow retail companies to serve customers in their physical stores without the need for cashiers. Technologies that allowed users to scan and pay for their products have been used for almost a decade now, and those systems did not require great advances in AI. However, these days we are witnessing systems powered by advanced sensors and AI to identify purchased merchandise and charge customers automatically.
Invoicing: Invoicing is a highly repetitive process that many companies perform manually. This causes human errors in invoicing and high costs in terms of time, especially when a high volume of documents needs to be processed. Thus, companies can handle these repetitive tasks with AI, automate invoicing procedures, and save significant time while reducing invoicing errors.

Drug manufacturing & sales

Machine learning can help forecast and prevent over-demand and under-demand, possible supply chain problems, failures in the production line, and much more. For pharma companies, this could mean substantial savings across their production and logistics processes. Using relevant background information like the environmental data and the movement of people, it is possible to predict where the need for specific kinds of medicine is increased.
This allows pharmaceutical companies’ sales teams to improve their efficiency and to provide medical institutions/patients with the medicine they need when and where they need it.
AI use cases in the pharmaceutical industry include predictive analysis, time-series predictions, and recommender engines, allowing for reduced research costs and a better overview of where to target sales.

Drug discovery & testing

Drug discovery is becoming increasingly competitive and expensive. In spite of technological progress, the cost of developing a new drug doubles every nine years.
The situation has driven the leading pharmaceuticals companies to search for new methods to reduce their R&D costs and avoid costly errors.





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