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Inferal

4 viewers · 30d

Total raised

$250K

+$250K this year

2 filings since 2026 · latest Other filed

Cumulative raised
LAST ROUND
Other · $250K
ROUNDS
2
INVESTORS
1
FOUNDED
2023
HQ
San Francisco, CA
SECTOR
Infrastructure
EMPLOYEES
N/A
30D VIEWERS
4

AI overview

Updated

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.

What sets it apart

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 history

2 rounds
OtherApr 17, 2026
Form D
+$250K$250K total
OtherApr 17, 2026
Form D/A
+$250K$250K total

Products

1 tracked

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
  • Full audit trails and implication graphs making every decision traceable and every consequence explainable

Links

WebsiteLinkedInSEC EDGAR filings