Our data apps.
Generative AI & LLMs.
Orbyfy’s data apps help connect data across systems, assets, networks, IoT and more. Powering the next wave of generative AI.
Scalability for AI: Our data apps can scale up or down easily to handle data growth, making it easier to train large language models with vast amounts of data.
Agility: With our data apps, organizations can quickly access and analyze data from different sources, enabling them to be more agile in developing and deploying large language models.
Accessibility: Our data apps makes data accessible to all teams within an organization, regardless of their location or department. This allows for collaboration and faster development of large language models.
Efficiency: Our data apps can help reduce the time and cost involved in data preparation, allowing data scientists to focus more on model development and refinement.
Security: Our data apps can help ensure data privacy and security, with features such as data encryption and access controls.
Use cases.
Generative AI will revolutionize every aspect of business, necessitating the support of a robust data network pipeline to keep pace with this transformative technology.
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Data science to decision science.
Connected, contextualized, and networked data.
Queryless business intelligence.
Directly question the data fabric.
Removing analytics barriers from decision insights.
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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.
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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.
Build a business intelligence engine with data apps.
We are eclipsed by data we don't even know how to use.
We don't have the available manpower to make sense of it.
We are now on a trajectory towards the full democratization of data and analytics.
Problem
People are evolving, jobs are evolving, skillsets are changing, and business intelligence functions will transform to natural language based querying.
A moderately complex query performed by a data analyst costs an estimated $900-$1300 each (fully burdened - 2 days timeline for turnaround). *Assuming multiple connected data sets required.
A complex analytics study performed by a data scientist costs an estimated $100,000 per project (fully burdened - 30 days timeline for turnaround). *Assuming multiple connected data sets required.
While most data analytics and data scientist professionals prefer to work in technology sectors, the average size of a data science organization in the industrial, energy, manufacturing, transportation, etc, for the Fortune 500, range between 50 to 200 people - equating to $10M to $40M in annual data analytics practice budgets.
70% of enterprise data never gets used to power AI or generate any insights needed for key decision-making.
By 2025, more than 150 ZetaBytes of big data will need analysis.
By 2026, 28% growth in data science; 2.7M unfilled jobs for data scientists.
Deep dive
In the worlds of marketing, strategy, engineering, customer service and more - much of the time, the role of business users is to ask thoughtful questions to direct decisions. Questions like: “Where does a certain customer segment live?”, “What variables impact shipping volumes?”, “What is the best time of day to release a targeted social campaign?”, etc.
And in most organizations, data analysts, or data engineers, or business intelligence users, or IT then prioritizes these requests involving asking questions of data. And they do this by querying data in various means.
Most of the time, these are generally rudimentary queries (certainly nothing fancy like machine learning or utilizing advanced neural network algorithms).
They invoke answers from data by running queries on data in a database using SQL coding. And much of the world interacts with data by running queries on singular databases - in effect, asking questions to a data silo in an effort to get thoughtful insights to direct decisions.
But in the real world data intersects systems and platforms, like a CRM, an ERP system, operational data, asset management systems, etc. If you want to ask questions of interlinked or connect data, then you need to start building SQL joins - joining databases together.
All of this takes a lot of time and energy. It also requires the need to know code. In the realm of an organization, the data analysts or engineers who know SQL querying that perform these functions are also typically one step removed (IT, Data Science) from the business users that have the thoughtful questions in the first place. This puts internal organizational frictions of asking questions and receiving thoughtful responses, as well as a time-lag to results.
A lot is being done to democratize data analytics to put it in the hands of business users, but the data integration, data management, and data connectivity piece is still not solved for.
Solution
But now imagine the real future, where you do not have to code, you do not even need to know so-called “low-code”, you ask intelligent questions in normal sentences, and receive (in seconds) responses in normal sentences - based on all enterprise data that is available, connected, contextualized, networked. A game changing proposition that forms the real democratization of data science and analytics - powered by Orbyfy’s data apps.
Benefits
Data science to decision science.
Connected, contextualized, and networked data.
Queryless business intelligence.
Directly question the data fabric.
Removing analytics barriers from decision insights.
Build digital subject matter experts, encoding knowledge in AI, with data apps.
We are facing the biggest skills gap ever and an ever-aging skilled workforce.
We recognize that much of our knowledge-base is encoded in our everyday life.
We are now on a trajectory towards the commoditization of raw intelligence.
Problem
People are scarce, knowledge is encoded in people, and hiring critical resources remains a critical challenge in certain industries.
Job position openings requiring critical skill sets and professional degrees last on average 50 days - losing retained knowledge results in material impacts.
On average, it costs $115K-$175K every time a critical person leaves an organization (opportunity costs, lost knowledge & expertise, gap in continuity) - not to mention $30-$45K in recruitment and training expenses for new talent.
Numerous studies show that knowledge workers spend roughly 30% of the day searching and gathering information rather than executing on actions.
Deep dive
The jobs market is seeing unprecedented strain across many dimensions. We are on the brink of the largest skills gap in a century. Generation Z, the youngest workers among us, have entirely different ambitions. Raised firmly in the trenches of social technologies, nearly 50% of them want to own a business and make sustainable income from content creation and influencing.
Currently, 30% of current job openings are in STEM fields, yet on average, only 11% of the US population has a degree in STEM. By 2030, the US is likely to see 2.1M unfulfilled jobs with a cost of $1T to the economy.
But microscopically, we are seeing the effects everyday. Every company has critical resources - operations and performance usually rests in a few capable hands. Most companies try to encode organizational know-how in static limitedly maintained knowledge-bases based on unconnected data in sharepoints, raw document repositories, intranets, and wikis. Finding useful information takes time, energy, sifting, and sorting. Most knowledge-bases do not tap into raw enterprise data, they tap into a limited subset of user-generated content, instructions, steps, procedures, and more.
Trends towards self-service means 40% of business users would prefer self-service over human-centric contact, and 90% would use a knowledge-base if it were available and tailored to their needs.
So what’s missing in this landscape? Purely and simply, the ease of connected data.
Solution
Imagine a future where you ask thoughtful questions to real challenges or to inform real decisions, and you are met with Exbot whom you can interact with to receive thoughtful guidance - all backed with the completeness of the entire data backbone across your enterprise. Move away from find-and-seek, move into ask-and-know.
In the connected Metaverse, you no longer have to be a Subject Matter Expert, you don’t even have to be a Specialist, all you have to do is ask thoughtful questions and be provided with tailored guidance and customer experience from datasets encoded from your organization, company, team, or social network - powered by Orbyfy’s data apps.
Benefits
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.
Build enterprise chatbots powered by AI, with data apps.
We want legendary people-centric customer experiences.
We want seamless interactions, not just questions and answers.
We are moving from customer service to customer experience to customer immersion (CS to CX to CI).
Problem
People are scarce, knowledge is encoded in people, and hiring critical resources remains a critical challenge in certain industries.
1.4B people are using chatbots and by 2027 chatbots will become the primary customer service channel for roughly 25% of organizations.
While 70% of customers have interacted with a chatbot in the past 12 months, 80% said chatbots increased their frustration level.
Nearly 90% of buyers are willing to pay more for great customer experience & 30% of customers will walk away from a company after a single bad experience.
Deep dive
Experience is everything - today, every brand is a digital brand. This means that the way users interact with brands is more complex than ever — there are more channels, touchpoints and opportunities to connect than most companies can keep up with. Beyond customer experience, customer immersion requires revisiting expectations - customers expect service at every “now” (instant & around-the-clock service).
While these new, more nuanced ways of interacting with users are exciting, they’re also a challenge for the developers tasked with maintaining the complex underlying code and connecting customer data together in all its forms.
And when that code gets too complicated, bugs emerge, leading users to encounter errors, hard to navigate experiences and other headaches that can send their business or engagement elsewhere. At the same time, these overwhelmed developers are facing pressure to deliver more features faster — often forcing them to choose between speed and quality. At that point, user experience suffers, leading to unhappy users and unsuccessful brands.
So how does an organization traverse from customer service to customer experience to customer immersion? Customer 360°.
The concept of Customer 360° refers to a holistic approach to understanding and engaging with customers that allows organizations to create exceptional customer experiences. It involves gathering and analyzing customer data from multiple sources, including customer interactions, purchases, and feedback, to create a comprehensive profile of each customer.
By creating a 360°-degree view of the customer, organizations can better understand their needs, preferences, and behaviors. This allows them to tailor their products, services, and communication to better meet the needs of individual customers, leading to higher customer satisfaction, loyalty, and retention.
To implement a Customer 360° approach, organizations typically use a variety of tools and technologies, such as customer relationship management (CRM) systems, social media monitoring tools, and data analytics platforms. By combining these tools with a customer-centric culture and a commitment to ongoing improvement, organizations can deliver exceptional customer experiences that set them apart from the competition.
Solution
Consider a future where you can connect customer data at the speed-of-light across an enterprise, integrated, contextualized, and networked data fabric architecture to bring legendary customer immersion to life. Orbyfy’s data apps connected to Customer 360°-data creates individualized insights, personalized recommendations, customized feedback, and tailored trouble-shooting or problem-resolution chains - all backed by a linked profile of each-and-every customer, across each-and-every interaction.
Benefits
360° customer profile data across all enterprise systems capturing current and past interactions.
Connected customer insights by simple query of data fabric.
Customer journey built-to-suit applications created at light speed.
ChatGPT-ready enterprise customer-centric data fabric.
Metaverse AR/VR-ready connected customer 360° data for personalized immersion.
Learn more.
Start with ideas.
Build with data apps.
