Using HASH

Capabilities

Knowledge graph generation

Quickly create a trustworthy, comprehensive, and useful knowledge graph (or "web"), capturing information about your business, industry or any area of interest in a strongly-typed, semantic graph.

Deep research

Expand your knowledge graph at any time: flesh out missing attributes of existing entities, discover new entities, and expand the information in your web to cover entirely new areas.

Advanced analysis

Calculate new metrics and generate statistics from information in your web, both proactively and at the point of need, enabling more confident AI answers to complicated questions.

Global visibility

Via HASH's browser extension, any information contained within a website or web app can be extracted passively as users browse, or as-required in the background, invisibly to users. This allows information locked within proprietary data platforms and other silos to be synced with your web, and used by AI, without the need for 1:1 integrations to be developed, or services to have or expose any kind of public API. Provided a service is accessible to an end-user, it can be explored and used by HASH.

Ontology management

Create and manage types with a best-in-class ontology builder and type system, allowing your organization to formally describe the sorts of data it cares about, and map internal data assets to semantically meaningful representations of them in a graph.

Coming soon: semantic types can also be automatically proposed or created from existing private data, as well as publicly available or otherwise accessible sources.

Workflow automation

Flows allow automated sequences of steps to be actioned using HASH, and AI workers are capable of planning and executing new flows, with or without humans in the loop.

Why?

As an auto-growing, self-checking knowledge graph, HASH helps with a wide range of business use cases:

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  AI application development: make AI-powered applications smarter by using HASH as a datastore for more effective retrieval-augmented generation (RAG), and as a tool for querying previously inaccessible, siloed sources of information. To learn more about using HASH in this way, see the HASH developer docs.

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  Sales: populate a CRM with leads matching parameters you define

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  Recruitment: populate an ATS with qualified candidates

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  Research: compiling comprehensive datasets

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  Competitive Monitoring: track competitor pricing, features, customer feedback and updates

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  Public Relations: find new journalists and generate new pitch ideas

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  Founders: from fundraising and go-to-market, to operations and reporting

HASH can also be directly queried in natural language, and connected to a growing range of external tools.

Future plans

Our long-term roadmap for HASH includes plans to make HASH suitable for a wide range of other use-cases, including data warehousing, business intelligence/dashboarding, and agent-based simulation. HASH was founded in 2019 as a multi-agent research lab.

HASH also experimentally supports block-based pages, making it suitable for certain kinds of knowledge management and document-related work. In the future we plan to expand this functionality to support no-code, block-based toolbuilding. In combination with HASH's built-in semantic representation of entities, we believe this will unlock a more user-friendly, LLM-ready, and generally intuitive kind of application development, and website building.

Limits to using HASH

Many people use HASH for just one or two things to start with, and over time begin to use it for more. If you're unsure whether HASH is a good fit for a problem you're trying to solve, or if you're wondering how you might achieve something with HASH, get in touch with us.