Data Intelligence

Cortex: conversational multi-agent enterprise brain

Client: NextAI in-house product

Client
NextAI in-house product
Sector
Data Intelligence
Alignment with today's offering
Enterprise Brains
The Challenge

The starting point

Companies generate data but cannot converse with it; committees waste time consolidating spreadsheets.

The Solution

What we built

We built Cortex, a conversational multi-agent environment with 15 AI specialists and a Virtual Debate Room where agents discuss a topic in real time and deliver prioritised conclusions, with persistent memory and continuous learning.

Key features
  • 15 specialist AI agents
  • Real-time Virtual Debate Room
  • Persistent memory and continuous learning
  • Prioritised actionable conclusions
  • Connection to enterprise data sources
  • Natural conversational interface

Anatomy of the solution

Converse with context, not isolated data

From input to outcome: explore the four parts of the journey.

Question

A business query in natural language.

Explanatory diagram based on the described solution. It simplifies the journey; it is not a deployed infrastructure diagram or a live monitor.

Reading the project

The thinking behind the technology

01 / Business

What really needed solving

A useful business conversation needs more than document access. It must retain the question, compare perspectives and present a reviewable conclusion. The described environment brings AI specialists and persistent memory together in a debate room.

02 / Design

Why this structure matters

Specialisation separates perspectives; memory maintains continuity; the conversational interface reduces access friction. Coordinating multiple agents does not prove their conclusions correct: value depends on how views are compared and tied to business context.

Connection to today’s offering

Enterprise Brains

The context connecting the company

Explore the service

Current relevance and possible evolution

This connects to Enterprise Brains and Digital Cortex infrastructure. Extending analysis to operational execution requires tools, permissions and approval points; an actionable conclusion does not mean an action is already executed.

This connects the case experience to our current services. Proposed extensions are not presented as features already delivered.

Impact
70%
less analysis time
50%
more decision efficiency
80%
fewer manual reports
15
specialist agents

Method and scope

How to read the outcome

What to measure

Measure time to reviewable analysis, reference quality and human corrections. Separate reporting savings from actual end-to-end execution capability.

What not to infer

Agent count is an architectural characteristic, not evidence of accuracy, autonomy or superiority over other approaches.

Basis of this analysis: the description, capabilities and stack published in this case. No new measurements are added and no independent audit of its results is implied.

Tech stack
NextAI EngineLangChainPythonReactFastAPIPostgreSQL
In context

How it fits with the rest

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