Key technologies and capabilities
Northstar LLM
Our proprietary large language model, trained on specific business data through simulations in virtual worlds on Google supercomputers. Performance comparable to Gemini 3.0. Technical specifications:- Trained using advanced reinforcement learning algorithms
- Optimized for audit and financial analysis tasks
- 96.8% accuracy on FinQA benchmark
Super agent (the most powerful Agent)
The platform’s main executor, which:- Works faster, better, and more scalably than competing solutions
- Capable of autonomously planning and executing complex action chains
- Manages other AI agents as a unified control center
- Intelligently delegates tasks to the most suitable agents
- Synthesizes outputs from all agents into a unified result
Vibe tasking
A concept allowing users to convey the “vibe” (intention, idea, context), and the platform automatically executes the full work cycle from planning to result.Dynamic planner
A dynamic planner that helps plan and divide tasks into subtasks. Analyzes processes and builds an implementation plan. Capabilities:- Breaks down complex tasks into logical step sequences
- Coordinates execution among multiple agents
- Adapts the plan in real-time based on results
Contextual workspace
A contextual workspace where all information is stored:- Memory — short-term and long-term
- Personal preferences — how to better understand and analyze the user
- All project data — files, context, history
Modular architecture
Neuro AI is built on a sophisticated modular architecture, where each module performs a specific function. Together, these modules ensure the system operates as a true autonomous agent.Key modules
AI agent module- Functions as a specialized intelligent agent
- Performs tasks analogous to an employee’s work in a specific role
- Capable of making independent decisions within its competence
- Based on large language models (LLMs)
- Understands and generates text in natural languages
- Analyzes information and performs logical tasks
- Finds and extracts information from the internet
- Works with databases and other sources
- Provides current data for analysis
- Learns from its own experience
- Improves performance over time
- Adapts to new situations
- Manages file operations
- Reads, writes, organizes, and processes documents
- Works with various data formats
- Maintains short-term and long-term memory
- Ensures context preservation between sessions
- Learns from past interactions
- Monitors and checks work results
- Ensures quality and accuracy before providing to user
- Manages output data quality
- Integrates with external APIs, software systems, and services
- Allows Neuro AI to interact with the broad digital ecosystem
- Provides connectivity with 200+ services through MCP
- Breaks down complex tasks into logical step sequences
- Coordinates execution among multiple agents
- Synchronizes the entire system’s work
Advanced technologies
A2A (agent-to-agent) technology
Technology that allows multiple AI agents to communicate and collaborate with each other:- Information exchange between agents
- Action coordination
- Joint solving of complex tasks
MCP (model context protocol)
Ensures consistent context management:- Unified context across different modules and agents
- Maintaining coherence throughout complex workflows
- 500+ integrations/apps with external services
Autonomous execution engine
Once the goal is defined, Neuro AI’s engine:- Breaks the task into a logical sequence of steps
- Executes these steps using various agents and tools
- Works without constant user input
Multi-agent collaboration
Neuro AI uses a team of specialized AI agents:- Research agent — gathering information from the web
- Data analysis agent — processing information
- Content generation agent — composing reports
- Agents work in coordination, passing information and results to each other
Integrated toolset
The virtual workspace includes:- Web browser for interacting with sites
- Code editor for writing and running programs
- File system for working with documents
- Ability to interact with the digital world like a human
Persistent memory
Unlike many AI models with limited context windows:- Long-term memory system
- Remembers past interactions
- Stores user preferences
- Remembers project details across multiple sessions
- Learns and adapts over time
Real-time monitoring
- Transparent view of system operations
- Ability to track progress in real-time
- User can intervene and provide feedback
Technical performance metrics
Measurable performance indicators:
- Sub-2-second latency for complex queries
- 96.8% on FinQA benchmark
Key differences from competitors
What makes Neuro AI a true AI agent
Four key capabilities of a true AI agent:1. Perceives the world — sees, hears, reads, and analyzes the environment. Processes text, images, video, and data from multiple sources.2. Understands context — doesn’t just react to keywords, but understands the full task context, including goals, constraints, and connections between information.3. Makes decisions based on experience and memory — maintains short-term and long-term memory, learns from past interactions, and applies knowledge to new situations.4. Acts autonomously — calls APIs, sends emails, manages software, controls systems, and executes complex workflows without step-by-step instructions.
Comparison with traditional AI systems
| Criterion | RAG systems | Voice assistants | Neuro AI |
|---|---|---|---|
| Technology essence | ”Smart search + text generation" | "Script + voice" | "Intelligent system that understands, remembers, plans, and acts” |
| Perception | Text from database only | Voice commands | Text, images, video, data from multiple sources |
| Understanding | Extracts fragments from vector database and inserts into response | Recognizes command keywords | Analyzes context, goals, connections between data |
| Memory | No long-term memory | No experience memory | Short-term + long-term memory, learning from experience |
| Decision making | Doesn’t make decisions | Executes predefined scripts | Reasons, plans, makes independent decisions |
| Actions | Doesn’t perform actions | Limited API commands | Calls APIs, writes reports, sends emails, manages CRM, updates systems |
| Learning | Doesn’t learn from experience | Doesn’t adapt | Learns from mistakes, accumulates knowledge, transfers experience between tasks |
| Autonomy | Requires constant management | Reacts only to commands | Works autonomously, executes complex multi-step tasks from start to finish |