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The New Superpower: Augmenting Knowledge Worker Productivity with Graph and AI Solutions

Illustration depicting a Superman and circles connected by lines and brain over his head showing the superpower of graphs
In the first few months of 2023, the unemployment rate has hovered comfortably below 4 percent, and between bank failures and inflation worries, the overall economic situation remains uncertain at best.


Those all affect your business, but as part of the engineering org that’s supposed to deliver technical solutions to help your peers solve business challenges, another issue probably hits closer to home. There are too few data analysts and knowledge workers to do the jobs required. Your organization is already short-staffed, and the job market for new hires is thin.


This deficiency sends your organization down two trying paths:


  • The few data analyst and knowledge worker foks you have on staff are in high demand for solving every data-related problem facing your business, even if it’s beyond their initial job description. They have no feasible way to process the endless requests they receive, leading to longer and more stressful hours—eventually, burnout.
  • These folks are tasked with solving new business problems and delivering solutions to their customers, but they’re hamstrung by tedious, time-consuming, and human-intensive tasks, which become a bottleneck for the whole organization to compete at the pace of business today.


As a member of the technical team, you can stop this endless sacrifice of time, energy, and well-being your peers endure. With a composable graph + AI platform, you can quickly deliver solutions to save them from drowning under the load placed on them. By accelerating AI solution development, you deploy solutions that help your peers do far more, which is more essential than ever with the current economic and hiring trends.


You can give them superpowers.

What is a composable graph + AI platform?

A composable graph + AI platform combines and integrates graph and AI capabilities to create a unified environment for developers who deliver augmenting solutions to their teams and the knowledge workers who leverage the data to generate insights and ultimately solve business problems.


To be considered a single platform, the vendor/provider must integrate these services intentionally so that you, as an engineer or developer responsible for accelerating AI solution development, don’t have to worry about picking out tools and building the integrative glue to get them working in harmony. With a single development platform for both graph and AI solutions, you can get back to building impactful solutions for your peers.


But let’s not forget composable—each component must be modular and flexible, allowing you to pick and choose exactly which services your peers need to develop knowledge and ultimately solve the challenges that face your organization and your customers.


GraphGrid is the world’s leading composable graph + AI platform that helps you accelerate graph solution development, but for now, let’s finish defining all the constituent parts.

The must-have services of a composable graph + AI platform

First and foremost, these services and components must be fundamentally based around a native graph engine, which stores data with a model optimized for handling relationships. Instead of forcing your peers to put their data into more boxes, like spreadsheets, you can deliver a technical solution that’s easy for them to understand, which helps them practice graph thinking: a mindset that recognizes the connectedness of your organization’s data and prioritizes discovering those relationships in context to solve specific business problems.


That simple structure has immediate benefits, like simplifying how your peers query data from your data lake/warehouse and returning data with its complete and essential context. If they query a single name, they can immediately explore the network of related names or businesses based on their distance, read full-text articles where they’re mentioned, see similar entities as analyzed by natural language processing (NLP), and much more.


On its own, a native graph engine can’t augment your peers’ abilities or streamline their time-consuming analytic tasks—its core purpose is to store connected data and optimize it for returning queries based around relationships. You need additional services to build solutions that help your peers do faster and more informed work, like forming relationships between data entities and performing full-text search on unstructured data to uncover previously-unknown insights.


  • Graph
      • Knowledge Management: Get the knowledge graph out of individuals’ heads (see: The Danger of Human-based Knowledge Graphs) and democratize it on a shared knowledge graph, where individual data analysts and knowledge workers can experiment, explore, and share while keeping everyone apprised of their work. By making knowledge accessible, everyone can collaborate to solve challenges in unexpected but productive ways.
      • Graph-Based Natural Language Search: Deliver your peers a search experience based on simple natural language, not keyword aggregations or query builders, that delivers results that take context, connection, and relevance into account.
      • Graph Change Data Capture (CDC): Push relevant changes to data analyst and knowledge worker peers (or your AI, ML, and NLP training models) to save them from their tedious and time-consuming daily check-in to see what’s changed.
      • GraphOps: You should be able to do all the above without getting your organization bogged down with operations-related work.
  • AI
    • Machine Learning: Merge traditional ML techniques with graph theory, such as graph structure, distance, and context, to develop models that are far more accurate for helping your peers solve business challenges.
    • Natural Language Processing (NLP): Transform text into knowledge by automatically processing and integrating the troves of unstructured data already sitting in your data lake, which your people can use to make decisions or train ML models in far less time.
    • Deep Learning: Help peers get results where non-graph AI models couldn’t because the data is “too messy,” of “low quality”, or “too small” by developing neural network-based models and training them with GPU power.
    • AIOps: Deploy AI models faster and without wasting time on operations because you know your API services have a known and reliable path to production.

What else you should look for in a composable graph + AI platform

Your qualifications for the right platform for your organization should go beyond the constituent parts. Take time to validate whether the data formats used by the composable platform are open source, which is most relevant for the graph engine itself. Having an open source foundation prevents lock-in and gives you the flexibility to reconfigure and extend the platform to your changing needs.


There should be SDKs, APIs, and drivers available in common programming languages to broaden usage, plus operations-focused functionality, like monitoring and logging, to simplify operations as much as possible.


Finally, this composable graph + AI platform should run wherever you want your graph data to live:


  • A fully managed cloud, which lets you stay focused on delivering value to your data analyst and knowledge worker peers, not worrying about operations.
  • Private clouds, which integrate graph + AI technology with your preferred providers, preventing cloud lock-in.
  • Your existing hybrid cloud, which lets you fully manage your IT assets while leveraging the scalability of the cloud.
  • An on-premises cluster, for limitless graph + AI solutions exclusively on your owned hardware.

The productivity benefits of accelerating graph solution development

Now that we’ve covered plenty about what this platform looks like and the technical requirements you should have for it, we can’t forget about what solution and value you’ll be able to deliver to your data analyst and knowledge worker peers using graphs and AI.

Use natural language processing to organize and deliver data automatically

As part of the technical team, you can offload the tedious and time-consuming task of processing enormous amounts of unstructured text documents through an NLP service. When you initialize and train domains and models, like Named Entity Recognition (NER) or keyphrase extraction, based on the challenges your peers are trying to solve, the NLP service processes new or existing data and makes the additional context available for your peers to explore and analyze.


You can then extend the value by building your peers a technical end solution that links together your structured data with context extracted by NLP, helping them see connections across text documents that would’ve otherwise gone unnoticed. All of a sudden, your peers are no longer wasting valuable hours trudging through documents in needle-in-a-haystack searches for relevant information. Instead, they can get to work understanding your organization’s trove of unstructured text because it’s been analyzed by models that help them see connections across text documents in seconds, not hours.

Help your peers work more proactively with change data capture (CDC)

Imagine that instead of asking your peers to constantly check in with their data lake to discover changes to their area of focus, they were given a customized alert on their device letting them know exactly what they should investigate in context in a knowledge graph. Suddenly, you’re delivering them a solution that saves them valuable time and guarantees they’re always seeing the complete context of their data.


GraphGrid goes a step further with their composable graph + AI platform. Their CDC service also pushes changes to your NLP/ML services with the latest changes, which can eventually trigger a re-training process that prevents AI drift and ensures your AI-based workflows provide the most accurate and relevant results.

Help unlock previously hidden insights with full-text search 

With a relational data lake, your trove of unstructured text simply sits there, unread, unused, and obfuscated from even complex SQL queries. You pay for it, maintain it, and yet your peers get little to no value from it being there.


With a full-text search service interacting with your native graph engine, you can develop search solutions that let your peers search your entire data lake for complex topics using only their natural language, much like they would experience when using Google or a conversational AI like GhatGPT for general research. Graph-based search goes beyond the content of your unstructured text, taking into account how these documents and data entities are related to deliver richer searches and full context.


With the most relevant results at their fingertips, your peers can quickly branch out into connected data, or filter those results to highlight specific attributes or relationships, allowing them to find and codify insights that might’ve otherwise gone unnoticed.

The shortest path to productivity with graphs and AI

GraphGrid is a composable graph + AI platform with all the building blocks you need, as part of an engineering team, for accelerating graph and AI solution development and delivering the solutions your peers need to supercharge their efficiency. With less tedious and time-consuming tasks absorbing their valuable hours, they can get a handle on their backlog of analytics requests while also making meaningful headway on solving the biggest problems that face your organization and customers.


To get started, tell us about your highest-impact data challenges or opportunities, and we’ll schedule a free solutioning session at your convenience. We’ll go a step further, showing how much we stand behind our promise of efficiency—we’ll tailor this session toward your use case and the results you need, maximizing your time and getting you as far down the graph + AI road as possible.


Get started for free!