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Inferal

Live data layer that triggers AI agents on business rules

Raised to date

$250K

of $1M offered across 2 Form D filings

Still open
$750K
Investors
1
Last filed
May 11, 2026

About

Inferal builds what it calls a data-native operating system for AI agents. The platform connects to a company's existing databases and message queues and maintains live operational models of the entities, relationships, policies and business rules those systems describe, so agents act on current data rather than on stale snapshots pulled through request-response queries. Rather than having agents poll, Inferal continuously matches declared rules against incoming data and activates the relevant agent with its context attached when conditions align. Every activation and decision is recorded against an ontology-based semantic layer so the reasoning is traceable end to end. The company was founded in 2023 by Yurii Rashkovskii and grew out of Omnigres, his earlier open-source Postgres-extension project; as of mid-2026 it is pre-general-availability and engaging customers through a design partner program.

Inverts the usual data access pattern for agents: instead of an agent querying a database and waiting, business rules are declared once against an ontology and continuously evaluated, so the system wakes the agent at the moment conditions are met and hands it the context. Combined with implication graphs and full audit trails, that makes each decision explainable, which is the gating requirement in the regulated domains it targets.

Funding

  • OtherFiled May 11, 2026Form D/A
    $250K/ $1M
  • OtherFiled May 8, 2026Form D
    $250K/ $1M

Product

Inferal

Data infrastructure / operational intelligence platform

A data infrastructure platform that sits on top of a company's existing databases and message queues and turns them into an event-driven substrate for AI agents. It keeps live operational models of entities, relationships, policies and rules in sync with data at rest and data in motion, evaluates declared business rules continuously, and activates agents with the relevant context when conditions are satisfied.

  • Continuous condition matching that activates agents when rules are satisfied, removing the need to poll
  • Operational models of entities, relationships, policies and rules kept in sync with source systems
  • Ontology-based semantic layer so facts, rules and agents share explicit definitions, including competing definitions of the same concept

Built for

Enterprise data and engineering teams deploying AI agents in decision-critical operations, concentrated in financial services (fraud detection, AML), risk and compliance, and logistics and supply chain, with stated applicability to healthcare and biotech, e-commerce, manufacturing, energy and utilities, and telecommunications.

People on file