AXYS, An Enterprise Data Operation Platform


AXYS - The Ultimate Convergence of Data Fabric, DataOps, and Generative AI

Executive Summary

SVCIT Engineering & Axys Platfrom

Are you looking for a cost-effective solution to unlock your organization’s full potential with seamless digital and data transformation? empowering internal solutions and generative AI models to thrive on harmonized data from diverse sources for unmatched efficiency and high-quality results?

Silicon Valley Cloud IT (SVCIT) leveraging AXYS DataOps & Data Fabric Platform to provide a comprehensive solution for businesses seeking to manage their digital and data transformation needs efficiently.

The platform simplifies data management for generative AI applications and internal solutions, using high-quality data from various sources like databases, files, and business applications such as CRM or ERP.

Generative AI Solutions

The AXYS offers a no-code data integration and management system, centralized data pipelining, custom data solutions, and automatic API layer generation. These features provide unified, real-time search and data understanding, data filtration, and intelligent tagging capabilities to streamline your data management system. Automating the data preparation process from collection to visualization and integration service enables businesses to start digital modernization and transformations in days rather than months and at a fraction of the cost of traditional consulting services.

With the help of the Axys platform, SVCIT offers a robust and reliable enterprise software development solution for businesses of all sizes. This game-changing solution is adaptable to new digital transformations and saves on licensing costs and resources. The platform’s existing marketplace of connectors and the ability to build custom solutions make it easy to connect any data source within your company in a fraction of the time.

SVCIT’s expert engineering team can drive your company data to any AI solution or analytic tools to generate insights or provide custom solutions for your company data in record time. By automatically generating advanced API layers on data silos for company departments, Axys eliminates months of development time and offers powerful search and filtering capabilities.

The platform transforms traditional data management pipelines into modern, streamlined solutions that leverage data fabric and AI solutions to put businesses in control of their customer and data insights.

In summary, the AXYS DataOps & Data Fabric platform, powered by SVCIT experts, enables organizations to efficiently manage their data for generative AI applications and internal solutions, leading to valuable insights, better decision-making, and increased business growth. SVCIT is the ideal partners for your next data management project by offering rapid integration, reduced engineering resources, and cost-effective solutions.


Unifying silos distributed data with Axys

SVCIT with AXYS Modernizing Data Integration

Approximately 70% of enterprises seek help from IT solution companies for their digital and data transformation needs, or creating infrastructure to feed their company data into generative AI solutions, but traditional consulting engagements can be lengthy, laborious, and expensive. SVCIT has automated the data preparation process with the Axys platform, which fully automates data preparation from collection to virtualization and API generation.

Axys delivers scalability to accommodate increasing data sources, volumes, and complexity without the need for additional resources. With SVCIT and Axys, enterprises can start their digital transformation in days rather than months and achieve similar results at 10-20% of the cost of traditional consulting services.

SVCIT with 15+ years of experience has been serving over 700+ medium to large enterprises by offering a flexible, dedicated senior engineering team at a competitive price. SVCIT services cover the complete software lifecycle process. Each client is assigned a dedicated manager who understands their specific business needs. SVCIT responds quickly to customer-initiated changes, provides excellent support, and maximizes ROI. We are proud of our multiple successes and experienced resource-saving strategies.

Data Preparation Cost & Effort

For digital transformation and data silo problem solving or delivering data to generative AI solutions, companies might use ETL/ELT solutions to integrate their data sources, move, and transform data from multiple sources into a single high-maintenance data store, such as a data warehouse, data mart, or data lake. Based on SVCIT’s long experience in data pipeline creation, we explain the general steps for creating an ETL (Extract, Transform, Load) process for data and the common problems and efforts that arise in each step.

We also provide tips and highlights of modern problems that organizations face today in ETL, such as handling larger and more complex data sets, real-time data, data quality and governance, cloud computing, and integrating unstructured and semi-structured data. We also present common mistakes in ETL design and implementation and the range of engineering resources required for ETL processes, eventually making it highly unaffordable to many companies if not carefully planned and executed.

The general steps for creating an ETL process for data

Extract: Determine the data sources from which you need to extract data. Identify the specific data fields required for your analysis or processing, and extract the data from the sources in the required format. Common sources of data include databases, spreadsheets, logs, and APIs.

Transform: This step involves cleaning, filtering, and converting the extracted data to a format that is suitable for your analysis or processing. It may involve tasks such as data mapping, data type conversion, data cleaning, and data validation. You may also need to merge data from different sources, remove duplicates, and aggregate data.

Load: Once the data has been transformed, it needs to be loaded into the target system, such as a database or a data warehouse. This step involves defining the data schema and structure, selecting the appropriate data storage format, and loading the transformed data into the target system. You may also need to define the data integration and data validation rules.


Data source identification

Identifying the relevant data sources from which to extract data can be challenging, especially when dealing with large and complex datasets. Data engineers need to determine which sources are required for the analysis or processing and which specific data fields are needed.

Data source compatibility

Different data sources may have different formats, data types, and data structures, which can make it difficult to extract data in a uniform way. Ensuring compatibility between the data sources and the ETL system requires significant planning and development.

Data quality issues

Data quality issues such as missing, inconsistent, or inaccurate data can arise during the extraction process. These issues can have significant downstream impacts on the accuracy and reliability of the data.

Performance issues

Extracting large amounts of data from multiple sources can put a strain on the ETL system and cause performance issues such as slow data extraction, network congestion, and resource contention.

Data security

Extracting data from different sources can pose data security risks if the data is not properly protected.

Data governance and access

Determining ownership and access rights to the data sources can be complex, especially in large organizations with multiple stakeholders.

Inaccurate data mapping

One of the most common mistakes in ETL design is inaccurate data mapping.

Inadequate testing

ETL processes must be thoroughly tested before deployment to ensure that they are working correctly. Inadequate testing can result in data errors, processing delays, and other issues that can impact the accuracy and reliability of the data.

Poor data quality

ETL processes can be impacted by poor data quality, including incomplete data, inconsistent data, and inaccurate data. This can result in incorrect data being loaded into the target system, and can impact downstream analysis and decision-making.

Lack of scalability

ETL processes must be able to handle large volumes of data, and must be scalable to accommodate future growth.

Resource Extensive

Range of engineering and domain expert constant requirement. For data management process an organization will require to have the given below resource at minimum.

The SVCIT Automatic Data Integration & Management Service is a scalable solution that eliminates the need for traditional ELT solutions, enabling businesses to handle increasing data sources, volumes, and complexity with ease. Our service can help you complete your digital modernization or transformation in weeks instead of months.


By leveraging the Axys Data Ops & Data Fabric, SVCIT simplifies the complexities of managing and accessing data from various sources. With virtualization technology, Axys creates a single, unified view of all data across the organization, making it easier to access, analyze, and visualize data more efficiently and effectively or to be used for any solutions.


The Axys platform seamlessly connects to multiple data sources, including databases, data lakes, and cloud platforms, and integrates data from various applications and systems. It also offers robust data access control and governance features to maintain data quality, security, and compliance. Moreover, the platform provides advanced analytics capabilities such as predictive modeling and machine learning (ML), empowering businesses to gain insights and make data-driven decisions. Additionally, data visualization tools facilitate the creation and sharing of dashboards and reports.


Cost of Typical Resources Annually and Scope of Project : Based on SVCIT many years of experience in related projects, ETL design and development is an iterative process that can take weeks to months and may be affected by various factors. To ensure efficient completion, clear understanding of project requirements, a detailed development plan, skilled team, necessary resources, and effective communication are crucial. The given estimate is based on a proof of concept and is not an enterprise-grade solution despite Axys platform. The project typically takes 8-14 months, including the development of a final solution for the customer’s specific use case.

Traditional Resource Estimate without Axys Platform:



Resource Cost Annually (U.S)

Continues Maintenance Effort

Annual Requiring Cost

Enterprise Software Architect





Data Engineer:

Responsible for designing and implementing ETL processes





Data analyst:

To work closely with data engineers to design and test ETL processes.





Database Administrators:

Manage databases to ensure proper configuration, optimization, and security.





Infrastructure Engineer:

To ensure ETL infrastructure is scalable, secure, and reliable.





QA Engineer:

Testing ETL processes to ensure accuracy and consistency.





DevOps Engineer:

Manage the ETL process to ensure it is secure and reliable.





Backend Engineer:

To develop code and integrate 3rd party solutions for ETL process.


($180-$220K) X 2



Front-end Engineer:

To develop end user access points.


($110-$150K) X 2



Project Manager:

To manager project priorities and scope







$1,220,000.00 -


$385K - $625K

Time to Production/Market

8 to 14 Months

Upfront Cost

Resources and Services - $100K-$150K Monthly

Prototyping Ability

Requires Backend Development

How to avoid Significant cost and effort given above?

With SVCIT experienced engineers & Axys Data Ops & Data fabric

SVCIT by leveraging Axys platform can build any custom solution upon request with time to production within a few weeks. Our senior hands-on engineers with deep knowledge and expertise in the Axys platform can help your company build any custom solution, completely eliminating the need for backend data management solution development.



SVCIT Resource Cost Monthly

Continues Maintenance Effort

Annual Requiring Cost

Backend Engineer: (Optional)

To develop code and integrate 3rd party solutions for ETL process.

Up to Full Time

Call Us

Upon Request


Front-end Engineer: (Optional)

To develop end user access points.

Up to Full Time

Call Us

Upon Request


Axys Platform


Introductory Rate $3K/M





$3K + Optional Resources for Contract / M

Time to Production/Market

  • Instant Data Preparation, Integration, Pipelining, Tagging, Search/Data Understanding, and Automatic API Layer Generation (Up to a day)
  • Typical Dashboard and Custom Solutions:
    2-4 Month Development with Resources for Contract (optional). i.e. Generative AI Integration and Management Dashboard

Upfront Cost


Prototyping Ability

Instantly and Dynamically


Leveraging SVCIT’s 15 years of experience and expertise in serving 700+ enterprises, we have developed a technology-driven approach to enterprise data operations, solving traditional and costly integration challenges. Our fully dynamic process minimizes engineering effort for custom solution development, allowing you to kickstart your digital transformation in days instead of months, and at a fraction of the cost of traditional consulting services. Embrace the future of data operations with Axys and SVCIT’s comprehensive and agile solution.

Extensive Experience: SVCIT has proven track record and expertise to deliver exceptional engineering solutions for your organization.

Technology-Driven Approach: SVCIT’s technology-driven approach ensures the most efficient, scalable, and innovative solutions for enterprise data operations, keeping you ahead of the competition.

Cost-Effective Transformation: Opting for SVCIT’s expert engineers and automated data preparation and integration services means achieving digital transformation in days, rather than months, at a fraction of the cost of traditional consulting services.

Custom Solution Development: SVCIT’s skilled team is adept at minimizing engineering efforts for custom solution development, tailoring services to your unique business needs and objectives.

Seamless Integration: SVCIT understands the complexities of enterprise systems and has a track record of solving traditional and costly integration challenges, ensuring smooth and seamless data operations across your organization.

Trusted Partner: As a leader in the field of enterprise data operations, SVCIT is a reliable partner dedicated to helping your company navigate the ever-evolving landscape of technology and data-driven solutions.

Commitment to Excellence: SVCIT prides itself on delivering top-notch engineering work and consistently maintaining high standards, ensuring your organization benefits from the best possible solutions and services.



According to Accenture in order to be a data driven company despite 80% that are failing by 2025, the organization must prepare for development of following process:

1. Build the right Data Foundation
2. Establish good Data Management and Governance Practices
3. Turn data into Insights
4. Realize Business Value from data

Reference: Accenture (Closing The Data Value
Gap – How to become data-driven and pivot to


Axys has full cover for Data Preparation & Integration:

Axys provides massive competitive advantage

Below is list of Axys capabilities & coverage compare to any other tools provided in market.

1- Data Foundation

  • Data Ownership
  • Legacy Application Data Connection
  • Custom Connector Development
  • Connector Marketplace
  • API Layer
  • Local Environment Deployment (Company Cloud)
  • Managed Cloud Environment Deployment
  • On-Premise Data Storage Access
  • Platform Enabled
  • Ease of Deployment
  • Ease of Maintenance (Backup, Fault tolerance and Auto Recoverability)
  • Data Lock by Vendor
  • Auto Scale
  • Cloud Agnostic
  • Admin Management Portal

2- Data Management & Governance

  • Build Data Relationship (Automatic)
  • Keyword/Data Value Ranking
  • Manage Data Transformation Logic
  • Inherit Company Access Policy
  • Data Compliance Compatible (Ownership vs. Shared with 3rd Party)
  • Data Streaming (Indexing Schedule)
  • Document Understanding
  • Write back to original data source
  • Departmental Data Access Control
  • Data Aggregation
  • Search User Interface Modification

3- Insight Generation

  • Usage Analytics
  • Data Export
  • Link back to Original Data
  • Data Analytics Engine
  • Smart Profiles
  • AI/ML/NLP Integration
  • Custom Smart Profiles Creation
  • Customize API Layer (Auto Update)

4- Value Realization

  • Data Drill-Down (Smart Tag)
  • Historical Search Activity Report
  • Ability to Extract Custom Insight
  • Internal and External Data Sharing
  • Data Federation
  • Flag Data for Correction
  • Build Your Own Solution (Unlimited Use-case)
  • Build Your Own Search
  • Build Custom Insight from Multiple Source
  • Departmental Search Result (i.e. Sales, Business, Finance)
  • Advance Custom Search
  • Dynamic Data Filter
  • Create Data Journey
  • Developer Community

Solution Benefits

Performance Benefits


AXYS brings all your software together and makes all your data accessible for any solutions, and searchable by people, places, projects, documents, conversations and more.

  • Axys Platform Architecture supports teams
  • Platform Architecture
  • Platform Architecture
Architecture flow
axys architecture flow
  • Data Regulation and Compliance
  • Normalization and centralization
  • Accelerated Analytics


Axys, the no-code DataOps & Data Fabric platform, and SVCIT, the technology-driven enterprise data operations leader, have joined forces to save engineering time and streamline your data processes. By handling all aspects of data operations, such as pipelining, indexing, normalization, prioritization, security, sovereignty, and governance, our combined solution consolidates disparate data sources in one place, making it seamless, efficient, and cost-effective to secure and access data for any AI-driven solution.

Why Axys Exist?

Data is Growing Exponentially

Accenture reported in the past eight years, data has grown 10X. In the next five years, we will have 175 zettabytes of data. While data is becoming increasingly ubiquitous and distributed, the value generated from that data has not kept pace. In fact, 66% of all data that is produced is not analyzed because 70% of companies don’t have the capabilities, resources, or expertise to realize tangible and measurable value from their data. The gap between data production and utilization is getting wider. According to Gartner, 80% of businesses looking to scale their digital businesses will fail if they don’t modernize and adapt to analytics governance by 2025.

Data Silos Everywhere

McKinsey reported employees spending 1.8 hours every day— 9.3 hours per week, on average—searching and gathering work information. Finding information that matters in day-to-day operations doesn’t have to be like finding a needle in a haystack. Data silos and scattered information disrupt business focus and day-to-day workflow. The first step is to reduce data silos and improves high-quality business productivity.

Recession Layoff and Budget Cuts

Many business leaders are turning to the familiar recession playbook of belt-tightening. According to a recent KPMG survey, 91% of U.S. CEOs believe we are hurtling toward a recession within the next year, and two-thirds believe it won’t be a short one. One top target for cuts identified in the survey is environmental sustainability initiatives, with 59% of executives planning to pause or reconsider their spending. About half of CEOs also plan to fire employees, or in modern-day parlance, to “downsize their employment base.” From our surveys, many organizations maintain internal DevOps engineering resources to build internal portal for information and knowledge share or relentlessly develop custom code to retrieve data into legacy systems or heterogeneous applications.

Streamlined Data Collaboration and Knowledge Discovery

Axys unifies your work distributed data into one place for instant access so you can quickly find what you need wherever it lives like BambooHR, Okta, Salesforce, Jira, FreshDesk or Google Workspace etc.

Axys unique data management platform replaces the need for users to log into multiple applications, enabling secure and dynamic data accessibility across various solutions, including Generative AI. We’ve simplified data engineering, connectivity mapping, compatibility, and data structure management, preparing organizations for daily changes and rapid transformation. Axys unified real-time search and abstracted dynamic API layer empowers users to unlock the power of organizational data, gaining valuable insights instantly within your company’s private network.

No Code API

Finding and retrieving insight and information is vital in every workflow of any company. Knowledge worker revolves around documents, emails, text-to-speech transcripts, messages, videos, charts, and issues every day. Axys no-code API allows you to create business logic without the limits you experience in coding or hiring DevOps engineering. Do more with less.

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