Choosing the WhyHow.AI Knowledge Graph Studio

Key Features of WhyHow.AI for more deterministic and structured control

Structuring your data contextually allows LLMs to deterministically and reliably retrieve your context for Agentic and information retrieval workflows. With two Knowledge Graph PhDs in the WhyHow.AI team, we wanted to reimagine and simplify the way contextually structured data can be created, shortening the process to create meaningful graph structures from weeks and months to hours and days.

Knowledge Graphs do not have a standardised definition, which causes a lot of confusion. Ultimately, what people want to do in the age of LLMs is be able to combine both unstructured LLM search with data that is structured together in a contextual way. While there are a few different ways to achieve this, many techniques are basically a black box, and ultimately better data structures lead to better results.

So how does WhyHow help with data structures? Built on top of MongoDB, WhyHow & MongoDB represent the best of schema-less relational data, vector chunks and graph structures, combining the best of data structures into a unified way to view your structured and unstructured data.

A lot of LLM workflows, especially those that incorporate unstructured data, require infrastructure that is especially adapted for the LLM age. This is a better approach than attempting to rejig and orchestrate multiple legacy databases together to attempt to take advantage of LLMs.

Since WhyHow’s query engine does not utilize Text2Cypher, the results are also up to 2x more accurate, as can be seen in this benchmark here, where the same Graph created with Langchain’s LLMGraphTransformer gets better retrieval responses from WhyHow’s query engine than Text2Cypher.

WhyHow incorporates Research-Validated Design

We keep up to date with State of The Art so you don’t have to

Infrastructure is great, but does infrastructure keep up with the advances in implementation patterns and design?

WhyHow works with end-clients and provides services to large enterprises to not only build the graph structures they need, but, as an option, the end to end agentic infrastructure design and implementation, helping clients deliver ROI and business outcomes. With this end to end experience, it is clear that AI systems are not just about better infrastructure, but also about being able to support a range of implementation designs that reflect different data priorities, use-cases, and industries.

With this in mind, besides WhyHow.AI being a pioneer of a range of different graph structures and designs like chunk linking and modular graph infrastructure, we also stay on top of academic papers so that our infrastructure is able to flexibly employ implementation ideas and patterns as they emerge.

This flexibility allows us to build a range of case studies for different industries. This lets you focus on delivering value to your end-customer and not have to worry about your infrastructure being future-proof.

If there are other frameworks and design patterns we should account for, please let us know so we can prioritize it on the roadmap! Based on client needs and business use-cases, we have a range of opinions on what types of graph structures are ultimately common and needed.

Case Studies

We have a range of case studies that showcase different types of graph structures for different use-cases and industries, as well as the overall process and time taken to build them, across healthcare, finance and legal use-cases.

Medical Records -> Temporal Knowledge Graphs

Congressional Transcripts -> Temporal Knowledge Graphs

Legal Contracts -> Document Structure Knowledge Graphs

Financial Report 10-K -> Company-Specific Multi-Document Knowledge Graphs

File Directory Knowledge Graphs

This allows us to implement many different types of graph structures that are suitable for different scenarios. This flexibility is crucial, especially given the complexity of enterprise data and business use-cases, and the increased recognition by the market that a number of simplistic one-click graph creation frameworks are insufficient to create the data structures needed to get reliable business outcomes.

Open Source Ecosystem

Fostering an Open, Evolving Ecosystem

As knowledge graphs become increasingly central to AI systems, our aim is to advance the core technologies that make them powerful for RAG workflows. Our platform brings an opinionated approach to triple extraction, entity resolution, graph management, schema construction, and more, building on the inherent strengths of knowledge graphs that make them so valuable for reliable and explainable AI. With our open-source release, we’re excited to share these advancements with the community, supporting the broader adoption of robust, graph-enabled AI solutions and pushing the boundaries of what graph-based RAG workflows can achieve.

As part of our open source ecosystem, check out Knowledge Table, our Multi-Document Extraction & Graph Creation tool, that allows you to easily adapt your data extraction processes and simplify graph creation.

An Invitation to Get Started

There are 3 ways to engage with the WhyHow.AI Knowledge Graph Studio:

• Enterprise implementation of the open-source Knowledge Graph Studio
• Our Cloud-Hosted platform with our UI
• Knowledge Graph / System Design & Implementation Services

WhyHow.AI provides tools, services and processes for Structured Knowledge, Knowledge Graphs and more reliable Agentic RAG solutions. If you are interested in exploring any of our open-source tools (KG Studio, Knowledge Table) and consulting services, feel free to chat with us here.

If you’re thinking about, in the process of, or have already incorporated knowledge graphs in RAG for accuracy, memory and determinism, follow our newsletter at WhyHow.AI or join our discussions about rules, determinism and knowledge graphs in RAG on our Discord.