Intelligence
from within.
The most valuable intelligence about a company is already inside it. LOGION turns that operational knowledge into answers, decisions and action — on infrastructure the company controls.
Companies don't suffer from a lack of data. They suffer from a lack of connected understanding.
Transactions, documents, exceptions and past decisions tell the story of how an organisation really works. But that story is fragmented across systems and people.
LOGION is being built to connect those fragments into an operational model: a common context from which teams can investigate, decide and act.
Not another assistant. An understanding of the enterprise.
A shared operational memory makes it possible to ask better questions across functional boundaries — without losing sight of the original evidence.
ERP history, documents, business rules and previous decisions are interpreted together.
Finance, procurement, logistics and production work from a connected view of operations.
Start with source-linked answers; expand toward governed workflows and agent-led execution.
memoryConnected
and traceable
Ask a business question.
Follow the evidence.
One shared context, different functional perspectives. These examples illustrate how LOGION can reason over a company's own records.
The intelligence is yours.
So is the control.
LOGION is designed to run its AI inference within the customer's approved environment. Data access, permissions, audit trails and hardware capacity are part of the architecture, not retrofitted later.
Source systems
Approved ERP interfaces, documents and enterprise records.
Operational memory
Local context, retrieval and the relationships between events.
Local AI compute
Inference on appropriately sized on-premise GPU infrastructure.
Access & audit
Role-aware permissions, source references and controlled actions.
Small beginnings.
Material questions.
Potential savings identified
Opportunity surfaced in a retrospective review of operational and financial records. This is identified potential, not realised savings.
Financial reporting turnaround
Q1–Q3 statement and transaction-based comparison prepared in under twenty minutes in an early customer example.
Prove one thing that matters.
Find the consequential question
Identify one problem, establish its business baseline and map the relevant sources.
Validate with real operations
Test answers, evidence quality, access rules and local compute with actual users.
Expand what has been proven
Introduce additional processes, departments and sites only after measurable validation.
Industry experience.
Scientific depth.
Branko Zecevic
Group President of Metalfer. Industrial leadership across steel, mining and energy.
Pavle Acimovic
Princeton background in neuroscience, statistics and machine learning.
What does your company already know — but cannot yet see?
Bring us an operational question that crosses systems, departments or years of decisions. That is where we like to start.
Talk to LOGION ↗