AI/ML & Data End-to-End solutions

Transform your business with AI/ML and Data end-to-end solutions.

AI/ML Solutions Secure, Scalable, and Ready for Business

At AddAxis, we specialise in delivering end-to-end Data, AI/ML solutions that meet the most challenging business needs. We leverage major cloud platforms like Azure, AWS, and Google Cloud to craft solutions that seamlessly integrate with your existing infrastructure and technology stack.

Our expertise spans the entire data lifecycle, from warehousing with Snowflake, BigQuery, and Redshift to advanced data ingestion, modelling, and visualisation. We incorporate the latest in AI/ML and Generative AI to keep your business ahead of the curve. Our commitment to best practices — DataOps, ML Ops, and DevOps — ensures every solution is robust, scalable, and fully secure.

We focus on API-driven, service-based solutions that easily integrate into your business processes. By prioritising test automation, security, and privacy, we safeguard your data and operations at every stage.

At AddAxis, we don’t just provide solutions; we collaborate with you to implement strategies that deliver real results, helping your business fully leverage the power of AI/ML and data.

At addaxis.ai, we help you design, build and deploy

E2E solutions deployed on AWS, GCP or Azure (all major cloud platforms)
Solution and enterprise architecture (design solutions to work on your broader ecosystem)
Cloud AI: AWS Sagemaker, AWS Bedrock, GCP Vertex AI, Microsoft AI
Data warehousing using Snowflake, Bigquery, Redshift
Reporting using Microsoft Power BI, Google Looker or AWS Quicksight

Security and reliability through DevSecOps practices (infrastructure as code, GitLOps etc.)
API design, deployment and management

ML Ops

Serverless solutions
Microservices and containerised solutions
Project management

Our Capabilities

DevSecOps

We help integrate security practices into your DevOps process, ensuring security is a shared responsibility throughout the software development lifecycle. The primary goal here is to embed security measures early in the development cycle rather than treating it as a separate or final step.

Benefits

Early Detection of Vulnerabilities

We incorporate security from the start. This way, vulnerabilities can be identified and addressed earlier in the development process, reducing the likelihood of security issues being discovered in later stages or after deployment.

Faster Development Cycles

We automate security testing and continuous integration to help streamline the development process, allowing faster and more efficient secure software delivery.

Improved Collaboration

Our DevSecOps approach fosters collaboration between development, operations, and security teams, creating a shared responsibility for security and breaking down silos that can lead to communication gaps and delays.

Enhanced Security Posture

We deliver continuous security assessments and automated monitoring to ensure that the security measures are always up-to-date and help proactively address new threats and vulnerabilities.

Reduced Costs

By addressing security issues early in the development process, we help reduce costs that would be higher post-deployment or during production. Our DevSecOps practices help reduce the overall cost of security by minimising the need for extensive remediation efforts later.

Increased Compliance

By integrating security practices and tools into the development pipeline, we help you adhere to regulatory requirements and industry standards, ensuring that security and compliance are consistently maintained.

Improved Risk Management

Through continuous monitoring and automated security checks, we help manage and mitigate risks more effectively, reducing the potential impact of security breaches and improving overall risk management.

Enhanced Quality

By embedding security into the development process, we help deliver higher-quality software. We ensure that security issues are addressed alongside other quality concerns, which results in more robust and reliable applications.

We embed security into every stage of the DevOps lifecycle to help create a more secure, efficient, and collaborative development environment, which leads to better and safer software products.

ML Ops

We help deliver MLOps (Machine Learning Operations), a set of practices and tools to streamline and enhance the deployment, management, and monitoring of machine learning models in production environments. We extend the principles of DevOps to machine learning workflows to improve collaboration between data scientists, engineers, and operations teams.

Benefits

Improved Efficiency

We automate the deployment, scaling, and monitoring of ML models, reducing manual intervention and accelerating the time from development to production.

Consistency and Reliability

We ensure that ML models are deployed consistently and reliably, reducing the risk of errors and ensuring that models perform as expected in production.

Scalability

We facilitate the scaling of ML models to handle large volumes of data and traffic, ensuring that models can be effectively deployed across various environments.

Continuous Integration and Delivery

We support continuous integration and delivery (CI/CD) practices for ML models, allowing for rapid experimentation, iterative improvements, and seamless updates.

Enhanced Monitoring and Management

We provide tools for monitoring model performance, detecting drift or anomalies, and managing model versions, which helps maintain the quality and effectiveness of deployed models.

Collaboration and Transparency

Our approach improves collaboration between data scientists and operations teams. We standardise processes and provide clear visibility into the ML lifecycle, which leads to more effective teamwork and communication.

Governance and Compliance

Our MLOps approach helps ensure ML models adhere to regulatory requirements and governance policies. This makes it easier to track model lineage, audit changes, and maintain compliance.

By integrating these practices, we help enhance the overall efficiency, quality, and manageability of machine learning deployments, ultimately leading to more robust and scalable AI solutions.

Microservices

We leverage microservices, an architectural style that structures an application as a collection of small, loosely coupled, and independently deployable services. Each service is designed to perform a specific business function and communicates with other services through well-defined APIs.

Benefits

Scalability

Through our microservices capability, individual services can be scaled independently based on their specific needs. This allows for more efficient resource usage and more effective handling of varying loads.

Flexibility in the Technology Stack

We develop different services using different technologies or programming languages, which allows teams to choose the best tools for each specific task, facilitating innovation.

Improved Fault Isolation

We ensure services are decoupled, so failures in one service are less likely to impact the entire system. This isolation helps in identifying, managing, and recovering from failures more easily.

Faster Deployment and Development

We develop, test, and deploy services independently. This leads to faster development cycles and more frequent releases. This also allows for parallel development efforts and more agile responses to changes.

Enhanced Maintainability

We develop smaller, focused services that are easier to understand, maintain, and update. This reduces the complexity of the codebase and simplifies managing changes and improvements.

Better Alignment with Business Domains

We ensure that the microservices are aligned with specific business domains or functions. This leads to more effective and tailored solutions for different aspects of the business.

Improved Team Autonomy

Our microservices approach allows development teams to work independently on different services. This promotes autonomy and allows teams to operate more efficiently without depending on other teams’ schedules.

Easier Integration and Upgrades

Our microservices can be updated or replaced independently. This facilitates easier integration of new features or technologies and enables gradual upgrades without disrupting the entire system.

Resilience and Reliability

We build more resilient systems by leveraging their modular nature. Our services are designed to handle failures gracefully and retry operations, enhancing the overall reliability of the system.

With microservices, we offer our customers significant advantages in terms of scalability, flexibility, and maintainability.

Serverless computing

We leverage serverless computing to build and deploy applications without managing the underlying server infrastructure. Instead of provisioning and managing servers, we focus solely on writing and deploying code, while the cloud provider handles the operational aspects, such as scaling and maintenance.

Benefits

Cost Efficiency

We leverage serverless computing to ensure you only pay for the actual execution time of the code. There are no costs associated with idle server time, as resources are allocated and billed based on demand, leading to cost savings.

Automatic Scaling

We help automatically scale your application based on the incoming workload. The infrastructure can handle varying traffic levels without manual intervention, ensuring optimal performance and availability.

Reduced Operational Overhead

By abstracting away server management and infrastructure concerns, we allow developers to focus on writing code and developing features, reducing the time and effort required for operations and maintenance.

Faster Time to Market

We enable rapid application development and deployment. Developers can quickly deploy and test code changes without worrying about managing server infrastructure, leading to a faster time to market for new features and updates.

Improved Reliability

Our platforms have built-in high availability and fault tolerance. The cloud providers manage the underlying infrastructure, enhancing application reliability without requiring developers to implement complex redundancy and failover mechanisms.

Simplified Scaling

Our approach to architecture handles scaling up or down in response to traffic patterns. This eliminates the need for manual scaling configurations and ensures that the application can handle spikes in demand seamlessly.

Event-Driven Architectures

Serverless computing integrates well with event-driven architectures. This enables specific events, such as HTTP requests, database changes, or message queues — to trigger certain functions. This promotes more responsive and modular application designs.

Enhanced Security

With our strategy, the cloud provider is in charge of managing security, which includes secure execution environments and automatic patching. This reduces the burden on developers to handle low-level security tasks and ensures the infrastructure is up-to-date with the latest security measures.

Reduced Resource Management

Since the cloud provider handles resource provisioning and scaling, the developers are freed from managing server resources, leading to a more streamlined and efficient development process.

Our approach offers significant advantages in terms of cost, scalability, operational efficiency, and development speed, making it an attractive option for modern application development and deployment.

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Tim Scholes

Founder of addaxis.ai

AI/ML strategist and architect