AI that remembers, so every answer costs less
eVitalyst keeps the context and results of every AI conversation in a local vector memory inside its own PostgreSQL database, and reuses them for similar questions instead of calling an external AI model every time.
AI cost grows with every question
Most AI assistants forget everything after each answer, so the same kind of question is paid for again and again, and the bill rises with adoption.
Local Intelligence in eVitalyst
Memory of every conversation
The question, its context, how it was interpreted and the result are stored as vectors that capture meaning.
Local search first
A new question is first matched against this local memory, so similar questions worded differently are recognised.
Fewer model calls
When a close match exists, eVitalyst reuses what it already worked out, saving tokens and cost.
Role-based access on every question
Every question passes the role-based access check in eVitalyst, whether it is answered from memory or by the AI model.
Guardrails per role
Set AI guardrails for each role, so internal data stays protected as more people use the AI.
Knowledge kept in-house
What the AI learns stays in your eVitalyst database, built on PostgreSQL with pgvector.
Four steps, people in control
- 01
Ask
A question arrives and passes the role-based access check.
- 02
Search memory
eVitalyst looks for a close match in the local vector memory.
- 03
Reuse or reason
With a match, it reuses the stored context and result; without one, it calls the AI model.
- 04
Remember
New results are stored for the next similar question.
The agents behind it
Local Intelligence
Remembers context and results from earlier questions in a local vector memory and reuses them, instead of calling an external AI model every time.
AI Business Assistant
Answers questions about any module in plain language and analyses the answer: trends, risks and recommendations, with tables and charts.
Local Intelligence, answered
Can someone see data they are not allowed to through the AI memory?
No. Every question passes the role-based access check in eVitalyst, whether it is answered from local memory or by the AI model, and guardrails can be set for each role to protect internal data.
What is local intelligence in eVitalyst?
A memory of AI conversations, stored as vectors in eVitalyst's own PostgreSQL database with the pgvector extension, which is searched before an external AI model is called.
How does it reduce AI cost?
AI models are paid per token. When a similar question has been answered before, eVitalyst reuses the stored context and result instead of making a new model call.
Where is the memory stored?
In eVitalyst's own PostgreSQL database, alongside the ERP data, not in a third-party AI service.
Agentic ERP: a guide for leadership teams
The five levels of agentic ERP, where value comes first, how to keep control, the economics of AI and a 90-day roadmap. Sent to your business email.
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