Pre-seed round Selected pilot and validation collaborations open
Dimension Requivo General-purpose LLMs e.g. ChatGPT, Claude
What it is An operational knowledge system A general-purpose AI assistant
What it creates A governed operational knowledge layer that stays in the organisation Responses, drafts, summaries, and analysis
What it works with Large volumes of scattered enterprise information, documents, workflows, and expert know-how Prompts and context supplied by the user
Role of experts Expert elicitation and validation are built into the core workflow Expert input depends on the user’s own interaction with the model
How knowledge is handled Builds a maintained knowledge layer with structure, validation, and ownership Uses available context to produce an immediate answer or output
How gaps are handled Continuously surfaces weak, missing, or conflicting knowledge areas and routes them into structured expert follow-up Can identify gaps or conflicts when prompted, but only within the current interaction
Long-term value Builds a reusable organisational knowledge asset Produces useful outputs, but not a maintained knowledge layer by itself
Enterprise fit Model-flexible by design, allowing organisations to align model choice, infrastructure, and data-residency requirements Usually tied to the model, policies, and infrastructure of a specific provider
Key distinctions
01

LLMs respond to the context available in the moment. Requivo turns fragmented enterprise information into a governed operational knowledge layer.

02

LLMs can identify gaps or conflicts when prompted. Requivo surfaces them systematically and routes them into expert follow-up.

03

LLMs produce useful outputs. Requivo builds a reusable organisational knowledge asset that can be maintained over time.

Requivo turns fragmented enterprise information and expert know-how into a governed operational knowledge layer — a reusable organisational asset that can be validated, maintained, and used across teams, workflows, and AI systems.

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