Edge Data Platform
for the Autonomous World

Reduce time-to-access the right data by 10X
Improve efficiency for compute and storage 4X
Boost productivity for Data Scientists by 2X

Now available as on-demand videos, two insightful sessions on implementing large scale AI/ML:

Akridata integrates, modernizes, and automates
data processing, data storage, and data transmission
from edge to core to cloud


Visibility and Control

Boost productivity by locating the most relevant data within minutes, not days. Offload the burden of collecting, cleaning, organizing, and providing access to data, no matter where it is.


Smart Data Management

Simplify data operations using automated workflows and a global data catalog . Think of it as a decentralized database that brings structure and meaning to all your AI data.


Lower Costs

Save on IT costs because smart data processing at the edge, core, and cloud saves you time, tracks and protects your data, and increases the efficiency of your infrastructure.

The autonomous world’s
requirements are new


Deep Learning

Unattended services, even in mildly complex situations, require advanced AI models generated by Deep Learning. They require massive data with rich data formats from complex sensors.


Continuous Feedback

Autonomy means continuous improvement, ability to learn new objectives, respond to new scope, or manage new services. This generates a continuous data flow for continuous learning and continuous feedback from deployments.


Edge Data

In the Autonomous world, Edge, Core, and Cloud form must form a single seamless end-to-end infrastructure, cooperating on data processing, data communications, and data storage.

Do you have a data problem?

According to Gartner, 75% of enterprise-generated data will be created and processed at the edge, outside a traditional centralized data center or cloud. 

Yet, most of the industry’s attention is on data platforms for the cloud, not the edge.

In AI environments, edge devices can produce tens of terabytes of data per day in many types and formats.  That leads to unnecessary costs in data processing, data storage, and data communications.

Today, 80% of a data scientist’s time is wasted because it is too difficult to find and stage the right data, too time-consuming to ingest, pre-process, categorize, and catalog the data, and nearly impossible to to track and trace where it came from and how it transformed when it influences a decision.

Akridata: Edge Data Platform for the Autonomous World

The Akridata Data Ops Platform modernizes data ops for AI boosting productivity for data scientists and ML teams and lowering CapEx and OpEx for IT departments and site operators.

The Akridata solution is used in diverse areas like autonomous and assisted driving, smart cities, medical imaging, genomic analysis, cashier-less retail, and manufacturing.


Why Akridata

Improved Productivity

By automating the manual, time consuming data management tasks, Akridata allows data scientists to spend more time building better AI models faster, and less time on labor-intensive tasks like finding, cleaning, and reorganizing huge amounts of data. Add to that better IT efficiency and lower costs and the result is a 2x improvement in data scientist productivity.

  • Access the right data in minutes or hours vs. hours or days
  • Free up time for critical tasks
  • Efficiently handle growing data volumes at scale
More Accurate AI Models

Akridata pre-processes, catalogs, and prioritizes unstructured data at the edge to get relevant data more quickly, which helps train AI models more accurately. Only Akridata can browse, search, and access specific data regardless of where it resides (edge, core, cloud) and regardless of its geography, storage tier, version, etc. — undeterred by constantly changing data relevancy or evolving data pipelines.

  • Train and iterate AI models faster
  • Improved accuracy, improved safety
  • Access at scale, anytime, anywhere
Lower Costs

Akridata lowers costs by enabling more efficient reuse of existing infrastructure and software assets, avoiding unnecessary costs in data storage and transfer, and improving data scientist productivity. We call this “smart processing at the edge” because data science teams can immediately focus on the 1%-10% of data that is of value which significantly reduces the time and cost required to access, identify, process, transfer, and store data.

  • Avoid moving large amounts of data to the cloud
  • Reduce spend on storage/compute
  • Decrease bandwidth and latency

Akridata allows organizations to extend their existing infrastructure with an Edge to Core to Cloud AI Data Ops platform that retains your hardware investments, processes, and applications. The Akridata platform is also completely customizable to support your unique requirements.

  • Frictionless deployment
  • Leverage existing investments
  • Fully customizable
Data Protection and Compliance

Data tracking is necessary for debugging models and forensic analysis of data. Akridata treats data as code, which gives it the unique ability to track and trace data lineage/versioning from source to AI model to production use in the field. Tracking data lineage provides support for enforcing and verifying regulatory compliance with incidence management and data privacy regulations such as GDPR (EU), CCPA (California) as well as industry standards like HIPAA, FACTA, and GLBA.

  • Retrieve data anywhere, anytime
  • Track data lineage and provenance for each data object in the system




Numerous, data-rich sensors in autonomous vehicles generate data at the alarming rate of 4-8 terabytes per hour, or even more in some cases. Akridata gives organizations the ability to intelligently ingest, transfer, store, and immediately access relevant data at massive scale — regardless of where the data resides at that moment, while also keeping up with the constantly changing nature of data relevancy.



One of the most promising areas of health innovation is the application of AI in medical imaging for uses like diagnosis, annotation, or redaction. The accuracy of AI models directly impact patient safety and adoption of AI solutions by radiologists. Akridata enables builders of these AI models to find the right data, access it securely, and iterate quickly to improve their models. 

Smart Retail


Making sense of the growing volumes of data ranging from online customer behaviors to intelligent retail locations create new challenges for retailers looking to optimize operations and enhance the customer experience. Akridata automates the manual, time consuming data management tasks so data scientists can immediately focus on the 1%-10% of data that is of value.

Smart Cities


Whether it’s security surveillance or traffic management, maintaining citizens’ safety and privacy is a critical challenge for urban leaders. Not only does Akridata allow data teams to quickly focus in on the most relevant data, it also significantly reduces the time and cost required to transfer, store, and access data.


Kumar Ganapathy

CEO, Co-Founder

  • Founder/CEO, Virident (acq. WD)
  • Founder, VxTel (acq. Intel)
  • 75 patents, 10 papers
  • PhD UIUC, IIT Madras
Vijay Karamcheti

CTO, Co-Founder

  • Founder/CTO, Virident (acq. WD)
  • Scientist, Google
  • Prof. Computer Science, NYU
  • 90+ patents, 75 papers
  • PhD UIUC, IIT Kanpur
Sanjay Pichaiah

VP, BD and Partnerships

  • Founder, Trupeco (analytics)
  • Director, Mitsui Investments
  • Bear Stearns, Engineering, Level One (acq. Intel) 
  • CSU, Cornell
Sunil Samel

VP, Business Development

  • VP Strategic Alliances, Virident (acq. WD)
  • VP BD, Virsec
  • Founder, CoWare (acq. Synopsys)
  • IIT Varanasi, U. of Antwerp (Belgium)
Ajith Kumar

Director, Software Engineering

  • Sr. Manager, Advanced Technology, WD
  • Principal Engineer, Virident (acq. WD)
  • Systems Software, HPE
  • JSS University, Mysuru
John Wilson

VP, Sales

  • Automotive OEM Sales Director, Ansys
  • Automotive Sales Director, NI



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