TEKStack AI

TEKStack AI - Kubernetes Technology Stack

Data-Driven application platform tools and processes, specialized in building cloud native applications.

Our team of Kubernetes Certified (KCSP) engineers has worked on all the major cloud platforms on projects ranging from cloud native applications, DevOps, data engineering, chatbots, natural language processing, machine learning all built on top of Kubernetes with a focus on data.

TEKStack Kubernetes Technology Stack is a collection of Kubernetes platform tools, best practices and processes driven by clients’ needs and requirements.

Supported by open source and production-ready.

Replatforming
Modern Applications
Kubernetes DevOps
Enterprise Search
Smart Data Lake
End-to-End Machine Learning
Applications and Insights

Cloud Native Kubernetes Tools

Technology can make everyone’s job easier – when you take a holistic and human-centric approach.

TEKStack AI platform tools are built from the ground up to support your enterprise data-driven applications. Save time and money by using TEKStack AI tools for your next project. TEKStack AI can be used for your new project or expand your current offerings; support your entire data journey, from inception to insights to successful customer engagements.

Kubernetes
Helm
Prometheus
Terraform
Spinnaker
Vault
Jenkins
DevOps - Kubernetes

Delivering Amazing Kubernetes Solutions

Expand your limits and enhance your productivity – so you can be more agile with evolving customer needs and market shifts.

TEKStack AI tools are built on the foundation of Kubernetes to make your business in the cloud faster, more secure, and scalable. TEKStack AI saves you money by delivering your application to the end user more quickly. Our DevOps features support your enterprise applications by monitoring and reacting to your application’s needs in real-time. With TEKStack AI, continuous integration and continuous delivery, we can integrate and automate pipelines.

100% GitOps Based
Configuration as Code
Declarative Pipelines
Observability
Cloud Agnostic (AWS | GCP | Azure)
High Dev-Prod Parity
Machine Learning

Data Experts Alongside Our Tools

Technology is only as powerful and effective as its users.

Our TEKStack AI platform tools draw their strength from the people that support them. Our human expertise creates applications that best leverage your data while drawing on ML technologies – this approach puts your applications ahead of what the very best have to offer. The platform tools accommodate an end-to-end ML lifecycle, with a particular focus on data engineering, data ingestion and pre-processing smart data lake design, and lightweight ML pipelines. Our data journey expertise allows us to automate elements of the ML pipeline in ways that make it easier to create assets, which in turn allows us to address a greater range of customer challenges.

Enable Machine Learning
Supported by Open Source
Easy to Use
Empowered Decisions
Managed Kubeflow
User Authentication and Authorization
Knowledge Graph

Enterprise Search

Search your data faster than you can think – and make data-informed insights and decisions.

TEKStack AI applies natural language processing (NLP) technologies to our enterprise search solutions, with the goal of creating faster and improved database search. This allows for context-dependent search results, more informed insights, and faster business decision-making. We first process information from the databases we ingest into our platform, then tag information based on their type (name entity recognition). We then leverage this information to produce enterprise search solutions that are easier to use, and more informative than traditional full-text search options.

Knowledge Graph
TensorFlow
KubeFlow
Airflow
Kafka
Smart Data Lake

Derive Greater from Multi-Model Data Lake

The right data at the right time has value greater than the sum of its parts.

TEKStack AI platform tools include a scalable, multi-model data lake that supports any and every database best suited for the data type ingested into our platform. These databases are consolidated in a way that centralizes and simplifies data security and governance, and defines the source of truth for the datasets. The data lake is “smart”, in that it contains metadata that describes itself, which can be efficiently queried using a built-in data catalog. The data catalog allows users a greater ability to identify their data of interest across a number of different databases quickly, without the need to enlist help from a database expert.

Multi-model Data Lake
Data Governance
Elasticsearch
Blockchain- Immudb
Neo4j
MinIO
Amundsen