An agile team delivers work in small, usable, increments. This approach to software development promotes timely customer feedback and helps the team respond quickly to change.

A Proven Process

At Kanini, we have extensive agile experience with both Scrum and Kanban methodologies.

• With Scrum, your product is built in a series of fixed length sprints - giving all stakeholders a regular and predictable release schedule.
• With Kanban, the team uses a continuious workflow where updates are released as soon as they are ready. The emphasis in Kanban is to reduce wait times and blocking issues for all work-in-progress.

Agile at Scale

How can Agile practices span a large portfolio across multiple teams? How is continuous feedback and change compatible with long term planning and release commitments?

We use a Scaled Agile Framework (SAFe) to collaborate across teams, synchronize releases, and engage senior leadership on strategy and organizational goals.

DevOps

Continuious delivery (Kanban) and frequent, sprint-scheduled deliveries (Scrum) sound great in theory, but how do we incorporate these practices without interrupting of the flow of new work? The key is to automate release tasks and testing so that releases are not a drain on the team.

DevOps are agile principals applied beyond the software team. It aligns IT operations with agile development, and it enables a stronger agile practice. With DevOps, we define your product dependencies and infrastructure as code to automate the release process.

We help you plan, build, automate, and deploy your applications and workloads to Microsoft Azure.

Our certified Azure cloud architects have extensive experience in the execution of cloud initiatives using various cloud services including Hybrid, PaaS, IaaS, and SaaS.

Assess

The assessment offering entails meeting with you and discovering the scope of the engagement, inventorying the components of the existing solution (as needed), creating an architectural blueprint of the desired state (cloud solution), providing a financial analysis of the project and infrastructure costs, facilitating stakeholder buy-in, and the creation of a tactical implementation plan.

Migrate

Migration encompasses the execution of the tactical implementation plan that was output from the assessment offering. This includes the build out of the infrastructure in the cloud, development of software, testing, and deployment of software based on SDLC best practices.

Automate

Automation fully controls the deployment of infrastructure (IaC) as well as the creation of CI/CD for software and configuration artifacts. Leveraging the power of Azure DevOps, we are able to provide fully automated and governed infrastructure and software releases.

Secure and Monitor

Avoid costly business disruptions, meet compliance requirements, and secure your cloud data and resources against ransomware and human error. We architect your application to have automated backups and failover. As part of that effort, your application health and uptime is monitored, and logs are collected from multiple sources.

We use Azure Backup, Azure Monitor, Log Analytics, and Application Insights to secure and monitor your application workloads and data.

Optimize

Cloud offerings are continuiously improving, but keeping up on industry changes is a natural part of what we do. Let us help you choose services that have a bright future, and avoid design choices that lock you into questionable architectures.

Reguarding cost optomizations, we integrate with Azure's best monitoring solutions, including Azure Cost Management, to give you insight on your cloud spend. We help you trim and control cloud costs as cloud offering improve over time.

Support

Moving to the cloud is a long term commitment. We help you acquire the training and resources for long term maintentnace of your applications and workloads in the cloud.

Best-in-class data science is an iterative process that leverages both human domain expertise and advanced machine learning techniques.

Data Acquisition and IT

Our first goal is to identify where data exists to support the problem you're trying to solve. Then we connect to the source for ingestion and integration (with ETL and streaming data) to develop a machine learning system.

Since ingestion and integration depend on an appropriate technical layer to store and process data, we also engage IT to create and maintain that supporting infrastructure.

Typical sources include ERP databases, mainframes, IoT devices, data warehouses, IT logs, NoSQL document stores, multimedia storage, and centralized/monolithic application databases.

Machine Learning (ML) and Data Mining

Once data is acquired, we identify how to prepare it for ML execution. Steps here include data transformation, normalization and cleansing, as well as the selection of training sets for supervised learning.

Data mining consists of developing and running models that classify, segment, associate, and detect data anomalies:

1. Classification: the ability to classify a record or case as either belonging to a certain group or of taking on a particular value.
2. Segmentation: the ability to create groups (segments or clusters) from a set of data. The groups may be created using either continuous or discrete measures, or a mix of these measures.
3. Association: the ability to understand activities that may occur together (for example people who buy bread, also buy milk) or in sequence (for example, people download a song, then download the entire CD)
4. Anomaly detection: the ability to find those items which do not fit the pattern. This is often used in fraud detection or in the identification of aberrant activities.

First we determine ML algorithms to be used for training or clustering, then validate and run the model on acquired data. This process will likely comprise many cycles of running the ML routine and tuning and refining results.

Analytics and Insights

Analytics processes convert model results into descriptive statistics, predictive statistics, simulations, and other analytics.

People from the front lines of sales to deep within your business – not just “geeks” – are needed to run an analytics operation that turns data into insights and successfully implements those insights in the business. The crucial capability in today’s Big Data world is being able to “translate” analytics and data-driven insights into business implications and actions.

Processes to capture this value from data must be assessed for their ability to deliver at scale. Some old processes might need to be adapted, some might need to be fully automated, and others might need to be made more agile.

Gain meaningful insight from geospatial data. We help you collect, store, analyze, and visualize any data having a location description.

From the beginning, Kanini has helped organizations build complex geospatial applications. Our agile software development, data science, and cloud computing service offerings are a rich compliment to the geospatial domain knowledge that we bring to your project.

Collection and Curation

Vast stores of GIS data are publically available and often free to use. Additionally, we work with several data providers to help you collect and curate data layers for your applications.

Storage and Hosting

Spatial data is often dense and difficult to manage. As part of our cloud computing and data science business, we can help you host this data properly and make it readily available to your analysts and end users.

Analysis

The right location intelligence solution turns spatial data into efficient delivery and service routes, optimized logistics, better site plans, better resource utilitation, environmental protection, and streamlined mobile workforce management.

Let us analyze and automate the analysis of your spatial data.

Business Process Integration

With agile software development, we go beyond giving you commercial off-the-shelf software. While COTS is sometimes the right approach, our experts can architect a solution that blends open source and commercial software with custom GIS applications.