Test-Time Memory Framework

Eliminate hallucinations in foundation models through real-time contextual memory integration.
Deployment-ready
Space-hardened
99.9% Reliability
Built for developers requiring absolute reliability.

# Install via pip

pip install memories-dev

Also available via conda: conda install -c memories-dev

Memory Verification Framework

EARTHS-1S-2S-3DATA VALIDATIONInput VerificationTRUTH VERIFICATIONMulti-Source CheckRESPONSE VALIDATIONOutput Verification
Stage 1: Input Validation

Prevents corrupted or invalid data from entering the memory system using advanced validation rules and structured verification protocols.

Stage 2: Truth Verification

Cross-validates information using multiple sources to establish reliable ground truth. Implements consistency checks and verification algorithms for data quality.

Stage 3: Response Validation

Real-time verification of outputs against verified truth database. Applies confidence scoring to ensure response accuracy and validity.

Advanced AI Applications

Our satellite-verified memory system powers a wide range of cutting-edge AI applications where factual grounding and reliability are mission-critical.

Space-Based Applications

Upstream Ground Systems

Enhance pre-launch verification with AI that can validate mission parameters against physical constraints, preventing costly errors before they reach orbit.

Deployment scenario: Command validation for upcoming payload deployments
In-Orbit Decision Making

Enable autonomous spacecraft to make reliable decisions during communication blackouts by maintaining factual context about their environment and mission parameters.

Deployment scenario: Autonomous science operations during Eclipse periods
Downstream Data Processing

Process satellite telemetry and science data with context-aware AI that can detect anomalies, classify observations, and prioritize findings without hallucinations.

Deployment scenario: Automatic feature extraction from Earth observation imagery

Robotics & Physical AI

Planetary Rovers

Ground exploration robots maintain accurate terrain understanding and mission objectives even with delayed or limited communication with Earth control systems.

Deployment scenario: Autonomous sample selection during comms blackout
Autonomous Orbital Assembly

Enable robotic construction of space structures with AI that maintains accurate spatial awareness and construction plans verified against physical constraints.

Deployment scenario: In-space manufacturing and satellite servicing
Automated Docking Systems

Secure critical spacecraft rendezvous operations with AI that verifies approach trajectories and maintains accurate relative positioning without hallucination.

Deployment scenario: Resupply missions to space stations and habitats

Advanced Earth Applications

Autonomous Vehicles

Combine satellite positioning with local sensor data to verify AI navigation decisions, ensuring safe operation even in changing environments or unusual traffic scenarios.

Deployment scenario: Level 4/5 autonomous driving in dynamic environments
Physical Industrial AI

Factory automation and industrial IoT systems that verify AI recommendations against physical production constraints, preventing expensive or dangerous errors.

Deployment scenario: AI-guided precision manufacturing with human oversight
Disaster Response Systems

Combine satellite imagery with ground sensor data to provide reliable AI-assisted decision support for emergency services during natural disasters.

Deployment scenario: Real-time resource allocation during wildfire response

Contextual Memory Architecture

Our framework integrates real-time environmental context into foundation models during inference time, providing verifiable outputs for mission-critical applications.

Developer-Centric Reliability

Our memory framework provides a simple API that lets your AI systems cross-check responses against environmental facts and context, reducing hallucinations while maintaining the flexibility developers need.

Environmental Context
Real-time Memory
Test-Time Memory Framework

1. AI model generates initial response

2. Memory framework retrieves contextual data

3. Response is verified against contextual information

4. Verified response delivered to application

# Simple integration with any foundation model
import { MemoriesSDK } from 'memories-dev' # Initialize with your API key memories = MemoriesSDK(api_key="your_key") def process_with_memory(user_query): # Get response from your foundation model initial_response = your_llm.generate(user_query) # Verify against your environmental context verified_response = memories.verify( response=initial_response, context=get_mission_context() ) return verified_response

Applications Beyond Space

The same technology designed for space missions provides critical reliability for developers deploying foundation models in any mission-critical application.

Healthcare


In medical diagnostics and treatment planning, our memory framework ensures AI systems provide accurate recommendations by verifying outputs against real-time patient data and established medical protocols.

Particularly valuable for remote medicine, emergency response, and critical care environments where accuracy can be life-saving.

Transportation & Logistics


For autonomous vehicles, air traffic control, and logistics management, our framework helps reduce decision errors by incorporating real-time environmental data into AI decision processes.

Enhances safety and efficiency in complex transportation networks where environmental conditions constantly change.

Financial Services


For algorithmic trading, fraud detection, and risk assessment, our technology helps prevent costly errors by verifying AI decisions against current market conditions and regulatory requirements.

Particularly valuable in high-frequency trading environments and critical regulatory compliance applications.

Earth Observation Benefits

The space-based context verification techniques provide unique advantages for any mission-critical AI deployment:

For ML Engineers
  • Simple API integration with any LLM
  • Minimal latency overhead (< 100ms typical)
  • Production-ready with comprehensive logging
For System Architects
  • Horizontal scaling for high-throughput needs
  • Distributed verification architecture
  • On-premise or cloud deployment options
For Safety Teams
  • Comprehensive audit trails
  • Real-time monitoring dashboards
  • Configurable verification thresholds

Technical Principles

Context

Environmental Awareness

Integration of real-time situational data into inference processes

Verification

Multi-source Validation

Cross-checking outputs against multiple reliable data sources

Latency

Minimal Processing Overhead

Optimized for fast response times in time-sensitive applications

Reliability

Fault-Tolerant Design

Resilient architecture for operation in challenging environments

Open Source Community

We believe that reliable AI requires collaboration across disciplines. Join our growing community of researchers, engineers, and domain experts building the future of trustworthy AI.

Contribute to the Project

Our open source approach allows anyone to contribute to improving AI reliability in critical applications. We welcome contributions in:

Core Framework

Enhance the central memory architecture and verification systems

Integration Connectors

Build connectors for different data sources and AI frameworks

Documentation

Improve guides, examples, and API references

Use Cases

Share implementations in various domains and applications

Commercial Support

While our core framework is open source and free to use, we offer additional services for enterprise and mission-critical deployments:

  • Expert implementation support for mission-critical systems
  • Custom integration with proprietary data sources
  • Extended validation and verification protocols
  • Training and certification for technical teams
  • Service level agreements (SLAs) for production deployments
memories.dev

Advanced memory systems for AI applications. Build intelligent systems with structured memory architecture.


© 2025 Vortx AI Private Limited. All rights reserved.