Build a governed knowledge base your AI can actually trust.
Turn unstructured documents into a reviewed, structured, citeable knowledge base: model the domain, extract entities and relationships, and process documents with OCR.
The same governed core, across every kind of work.
Model the domain
Design entity types, relationships, and templates for your domain, with an Arabic-first data model.
Extract from documents
Pull entities and relationships out of unstructured text, then review and approve before they go live.
Process documents with OCR
Detect blocks, extract text, and anchor each chunk with a citation locator, even on scanned PDFs.
Shown in the product, not just described.
Every use case is delivered the same disciplined way.
From a real situation to a measured outcome, with the governance that makes it safe to trust.
The situation
A real, high-stakes task: a citizen's question, a contract to check, a process to run.
How Phronexa handles it
It reasons over trusted sources, acts through approved tools, or routes to a person.
The governance
Grounded and cited, scoped by permission, approved where sensitive, and fully audited.
The outcome
A defensible answer or a completed action, recorded and attributable, measured on your data.
The hard part is trust. It's already built.
Making AI safe enough to act inside a ministry or a bank is the real work, and it is the core of the platform.
Only approved content
Maker-checker review means only reviewed, approved knowledge can ever be used in an answer.
Citeable by construction
Every chunk carries a locator, so answers can point back to the exact article or passage.
Governed lifecycle
Content has effective dates and status, so answers never use superseded material.
Your administrators own it
Knowledge is engineered and maintained through an admin console, not buried in code.
A trustworthy, governed knowledge base that produces grounded, cited answers, owned and maintained by your own team.
View all use casesSovereign, Arabic-first AI, ready to prove its value on-premises, in weeks.
Start with one high-value use case and measure it on real data.