Orbyfy’s Fabric+ is the data fabric for the Metaverse, providing a unified data network, simplified data management, built-in data integration, self-service, centralized data store, and federated governance. Integrated data management for the generative AI revolution & more.

1. WearetheincubatorfortheMetaverse.
Webuildideasinuntappedmarkets.
Orbyfy.com
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Orbyfy Fabric+
Integrated,Connected,Contextualized&
NetworkedDataPoweringtheGenerative
AIWave
DataasDataware
February2023

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Orbyfy's Fabric+
Orbyfy’s Fabric+ is the data fabric for the
Metaverse, providing a unified data network,
simplified data management, built-in data
integration, self-service, centralized data store,
and federated governance. Integrated data
management for the generative AI revolution
& more.
Harness the 70% of data that you never use.
Connect. Orbyfy.com
Integrate for the last time.
Couple the enterprise with generative AI.
TM
1

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Orbyfy's Fabric+
Orbyfy’s Fabric+ is the fabric of the Metaverse: networked data in
a contextualized fabric where a data-centric architecture breathes
life into interconnected challenges like never before. Decoupling
data and data management from the traditional linear technology
stack. Powering a new wave of AI. Find out more. Connect.
Generative AI revolution will create and destroy business
models at a pace never seen before.
Generative AI will split the market: either companies will
be ready or they will not.
Today, 70% of enterprise data never gets used to create
even 1 unique insight.
Why?
Data
Information
Knowledge
Insights
Gap to insights continues to widen.
Data management is a
mess. 85% have failed to
create a comprehensive
data strategy and adopt the
right tools, technologies,
and platforms.
70% have failed at digital transformation.
You are not ready.
TM
2

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Current state
By the numbers
50% of every IT budget on the planet is
consumed by data integrations.
40% of IT development time is spent just
making interfaces work together.
70% of enterprise data is never connected
together, it never gets used for insights.
90% of data in the world is replicated data, so
is true for most enterprises.
80% of enterprises have failed to meet their
digital transformation goals.
TM
3
Disconnected Data
We are building the most impressive AI, yet data and data
management is still a landmine of app traps and data silos. The data
fuel that feeds the AI revolution is missing because it is disconnected.
Data Fragmentation
We demand connected, contextualized, networked data to power the
Metaverse, yet data integration and data copies create a mess of
fragmentation.
Knowledge Gap
We cannot make use of the data we even have and the knowledge
gap continues to widen between available enterprise data and
insights from that data.
Point-to-Point Integrations
We continue to manage data as a function of application
requirements, not enterprise requirements, or strategic requirements
- proliferating point-to-point data integrations.
We need to shift our thinking from hardware to software to dataware.
Dataware will form the fabric of the Metaverse. Orbyfy Fabric+.

5. Data is our new fuel source, and AI is our weaponry of strategy.
Every use case, application, solution, business model, value
proposition, and company (including ours) is trying to capitalize on
traversing the thought hierarchy of data to unique insights.
Data
Information
Knowledge
Insights
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Drill down
And while data is generated at an exponential pace, the translation
of data to information (the first step) requires an abstraction layer -
the very means and mechanisms of representing the 'physical' by
the 'digital' via a data model. The orchestration of the data model
(data model schema) itself across the organization forms the
biggest hurdle to truly unlocking the value of enterprise data and
everything downstream in terms of insights that can be used for
decisions.
A schema defines how data is organized in a database or
datastore or wherever data is persisted. It forms a model of how
the data is organized. In a relational database, a schema includes
logical constraints like table names, fields, data types, and the
relationships between these entities (i.e. how data points are
organized and connected within a relational database).
The problem with conventional approaches to application
development and downstream analytics and AI, is that most
enterprise organizations have adopted (through no fault of their
own), an application-centric architecture. Data is stuck in
applications and tightly coupled to the applications in which that
data has been generated.
TM
4
'Data' to 'Insights'
gap keeps
widening
'Data' to 'Information'
is the first step

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Drill down continued
60-70% of enterprise data never gets used to generate
insights and decisions because it's trapped in rigid
applications.
50% of IT budgets are used just to unlock data from
applications via point-to-point integrations.
Small-to-medium sized businesses use 40 apps on average;
enterprise businesses with 1,000+ employees have 200+
apps generating and consuming data.
It costs $20K to build a relatively simple API, and costs
$100K-$200K to build a relatively complex API interface to
move data between applications (multiply by number of APIs
and interfaces across the enterprise).
Data model schema is tightly coupled to the codebase. The logic
and code that controls how the application interacts with inputs
and outputs - all of that is controlled by the application in the
application layer. In order to change the data model or the
schema, in almost all instances you have to go into the codebase
and change code.
The main takeaway is that for most data across any enterprise,
data resides in applications, and those applications form data silos
in which data models and schema are tightly coupled to the
codebase. This creates not only integration challenges, but what’s
more is the downstream value pipeline of taking data and
translating it to insights is nearly impossible.
This forms a very costly problem:
Now what if data itself could be persisted in an interconnected
data model, networked, contextualized, and always available as a
single-source-of-truth? What if building APIs to connect data was
as simple as low-code queries? What if data no longer lives in
applications but data itself becomes the application?
TM
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7. Full data traceability: protected & secured
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Fabric+ architecture
Orbyfy’s Fabric+ is the data fabric for the Metaverse - providing a
unified data network, simplified data management, built-in data
integration, self-service, centralized data store, and federated
governance. Integrated data management for the generative AI
revolution & more.
Data Fabric Integration Layer
Data Fabric Model Schema Layer
Data Fabric Protection Layer
Data Fabric "Blockchain" Layer
Data Fabric Governance Layer
Flexible and adaptable data model architecture based on
semantic knowledge graph, change schema without breaking
code, persisted one-time copies, connected data.
Copyless integration to liberate universal data
model, low-code drag-and-drop reusable
query-based APIs, code-configure-update in
real-time.
Logic controls and security built into
the data layer at every cellular node -
every packet of information is built
like an application - data becomes
the application.
Inferences about historical usage and lineage
of data, where data is being used and being
consumed and what applications, complete
track-record of data evolution over time.
Data governance and compliance at the cellular node level to
manage complete data lifecycle and interactions - whether
by person, system, or algorithm.
Data Fabric
Enterprise Value
Layer
Connected, contextualized
and networked data across
the organization is now
available for large scale AI
generating insights to
power decisions
Connected, contextualized, networked data
Universal enterprise data & copyless integration
Data as an application - "dataware"
Data lifecycle governance: access & control
Actual enterprise-wide data fabric
representation of a medium-size organization.
Fabric+ data browser.
TM
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"A data fabric is 3D data."

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Fabric+ architecture
Foundation for enterprise-wide
generative AI use cases.
Unified Data Network
Connect data from any on-prem or cloud app or system and
originate new data - semantic knowledge graph architecture.
Built-in Data Integration
Eliminate app-to-app integrations by disturbing master data
through a multi-domain hub
Centralized Data Store
Use a common data platform to accelerate application
development & delivery
Simplified Data Management
Persist a one-time copy of data integrated with existing
systems and applications
Business User Self-Service
Give users direct access to change, control, and originate
data through an easy-to-use data browser
Federated Governance
Guarantee consistency of role and attribute-based data-
layer controls across all applications
TM
7
The power of dataware: last-copy integration,
business-user self-service, access-based
collaboration - all connected to powerful
enterprise generative AI.

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Fabric+ value
+ Payback & ROI
Immediate payback in speed and time with positive ROI after
1st project: last-copy low-code data integration
+ Forward Investment
Investment in model AI-ready stack: data fabric, data mesh,
pseudo-blockchain governed data
+ Transformation Speed
Factor 10x speed: enterprise technology transformation
initiatives and application development
+ Knowledge Growth
Exponential knowledge growth: data to insights to decisioning
across the enterprise
+ Innovation in AI
Accelerate AI-led innovation & business models immediately:
ChatGPT, LLMs, large-scale AI connected to all enterprise data
Connected, contextualized, networked data
across the enterprise to power the most
sophisticated generative AI models like
ChatGPT - unlock data.
TM
8

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Key differentiators
TM
9
Agility: A data fabric architecture can provide greater agility than an API
management tool or enterprise service bus middleware because it can
handle a wider range of data types and formats. This means that data
can be accessed and analyzed more quickly, making it easier to develop
and deploy applications and models.
Flexibility: A data fabric architecture can also offer greater flexibility
than API management tools or enterprise service bus middleware, as it
can integrate data from a variety of sources, such as databases, data
lakes, and streaming sources. This enables organizations to be more
responsive to changing business requirements and to scale up or down
as needed.
Scalability: Data fabric architecture can handle large volumes of data
and scale up or down as needed, making it easier to handle the demands
of large-scale AI applications, including large language models.
Unified View of Data: A data fabric architecture provides a unified view
of data across the enterprise, allowing teams to access and analyze data
from a variety of sources more easily. This can enable better
collaboration and faster decision-making.
Advanced Analytical Capabilities: A data fabric architecture can use
advanced analytical techniques such as machine learning and natural
language processing to identify patterns and relationships within the
data. This can enable more sophisticated analysis and modeling, leading
to more accurate predictions and better business outcomes.
While API management tools and enterprise service bus middleware can
provide benefits for managing data and applications in an enterprise
architecture, a data fabric architecture offers some advantages over these
tools.
Logic and controls in the data layer
Graph-based visual interface
Federated governance and access
Meta data experiences
Low-code apps on the fabric
Brain for your company

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Get started via solutions
TM
10
Business Intelligence Engine
Data science to decision science.
Connected, contextualized, and networked data.
Queryless business intelligence.
Directly question the data fabric.
Removing analytics barriers from decision insights.
Subject Matter Experts to Exbots
Static knowledge-base to dynamic knowledge-fabric.
Find-and-seek to ask-and-know in intuitive query format.
Capture and transcribe expertise on a universal fabric.
Enterprise self-service multi-domain know-how.
Data catalog, data dictionary, data browser.
Chatbots & AI
360° customer profile data across all enterprise systems
capturing current and past interactions.
Connected customer insights by query of data fabric.
Customer journey built-to-suit apps at light speed.
ChatGPT-ready enterprise customer-centric data fabric.
Metaverse AR/VR-ready connected customer 360° data
for personalized immersion.
+
Fabric+ ChatGPT
Our co-development incubation hub.
We are creating new bridges between technology and people
interactions. For the greater good.
Let's begin.
BusIE
Exbots
ChAI

12. 51.2 million customers and $5.2T assets
in financial services
Engineering new technologies in risk,
analytics & financial insights for 180+ years
Worldwide retail brands with 400+
locations and $1B+ in revenues
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TM
Orbyfy's Fabric+
Customers benefiting
Commercial real estate management
across 65+ countries and 400 offices
40M+ mobile telecom customers across
APAC delivering the best 5G experience
Saved $1.5M on their first project
Delivered a digital loan solution in 5 days
Now have 1/3 of the organization connected daily
Other customer stories