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Octopus Server

Octopus Server is a central part of the Octopus Software. It provides basic infrastructure and communication interfaces for all parts of the software.

Octopus Client

Octopus Server's high-level features

Octopus Server provides the following features:

  • Chat features.
  • Communication interface to OpenAI/Azure OpenAI/Anthropic/Ollama LLMs. It has support for almost 300 LLM models.
  • Data privacy. It makes sure that user private or company-sensitive data will not be accidentally sent to third-party LLMs.
  • It allows running Python-based Octopus AI Services that enhance system capabilities with additional, customized AI models.
  • It allows users to generate, with the use of LLM, their own custom AI Service that will be running together with other Octopus AI Services.
  • Administrator is allowed to upload own custom AI Services. All AI services are scanned for malicious code patterns to prevent installation of the services that would have malicious features.
  • It allows running TypeScript-based Octopus WASP Applications that enhance chat experience with UI applications that can communicate with LLMs and can provide additional business logic and features.
  • It allows users to generate, with the use of LLM, their own custom WASP Applications that will be running together with other Octopus WASP Applications.
  • Administrator is allowed to upload own custom WASP Applications. All WASP Applications are scanned for malicious code patterns to prevent installation of the applications that would have malicious features.
  • It allows running HTML/CSS/JS-based Octopus HTML Applications that enhance system capabilities.
  • Administrator is allowed to upload own custom HTML Applications. All HTML Applications are scanned for malicious code patterns to prevent installation of the applications that would have malicious features.
  • It provides internal, built-in system commands that can be called from the chat level like Octopus AI Services.
  • Role-based privileges system that allows separation of public and private chat activities depending on user roles.
  • Chat tokens audits. Allows the administrator to check LLM tokens usage.
  • User files allow users to store their work in files that could be used in different chat sessions.
  • Prompting agents allow users to schedule LLM prompts for automating their workflows and repetitive tasks.
  • Task Assignment System allows supervisors to create tasks and tests for the users that are automatically checked for completion by AI.
  • It allows to suggest which LLM and model should be used to answer particular questions during chat session.

Examples of additional AI services that can be run in the Octopus Server

Here is a longer list of possible additional AI models that can be run in the Octopus Server. This list contains some existing services because they were created for particular companies. Other services are just examples of what can be created.

Please keep in mind that this list is not complete. If you don't find here use cases for your company, please send your company data to [email protected] and we will get back to you with the custom offer made for your company.

  • Human Resources:
    • Resume Screening Models - Automate the process of reviewing and shortlisting candidates’ resumes.
    • Candidate Matching Models - Match job descriptions with suitable candidates.
    • Employee Onboarding Models - Streamline and personalize the onboarding process for new hires.
    • Performance Evaluation Models - Analyze employee performance and provide feedback.
    • Employee Engagement Analysis Models - Measure and improve employee engagement through surveys and sentiment analysis.
    • Attrition Prediction Models - Predict which employees are likely to leave the organization.
    • Diversity and Inclusion Models - Monitor and promote diversity and inclusion within the workplace.
    • Training and Development Models - Personalize learning and development programs for employees.
    • Workforce Planning Models - Forecast workforce needs and optimize staffing levels.
    • Payroll Automation Models - Automate payroll processing and ensure accuracy.
    • Benefits Administration Models - Optimize and personalize employee benefits programs.
    • Talent Acquisition Analytics Models - Analyze recruitment data to improve hiring strategies.
    • Employee Feedback Analysis Models - Analyze employee feedback to improve workplace policies.
    • Chatbots for HR Support - Provide automated assistance for common HR queries.
    • Remote Work Monitoring Models - Monitor productivity and engagement of remote workers.
    • Succession Planning Models - Identify potential future leaders within the organization.
    • Compliance Monitoring Models - Ensure HR policies comply with labor laws and regulations.
    • Workplace Safety Monitoring Models - Enhance safety through real-time monitoring and risk assessment.
    • Employee Satisfaction Prediction Models - Predict and improve employee satisfaction levels.
    • Recruitment Marketing Models - Optimize recruitment campaigns and attract top talent.
  • Legal:
    • Legal Document Review Models - Automate the review and analysis of legal documents.
    • Contract Analysis Models - Extract and analyze key terms and clauses from contracts.
    • Legal Research Models - Assist in finding relevant case law and legal precedents.
    • Predictive Case Outcome Models - Predict the outcomes of legal cases based on historical data.
    • E-discovery Models - Identify and classify relevant electronic documents during litigation.
    • Intellectual Property (IP) Management Models - Automate the management of IP portfolios and patent searches.
    • Compliance Monitoring Models - Ensure that organizations comply with legal regulations.
    • Fraud Detection Models - Detect fraudulent activities and potential legal violations.
    • Document Summarization Models - Summarize lengthy legal documents for quicker understanding.
    • Contract Drafting Models - Assist in drafting and automating contract creation.
    • Litigation Risk Assessment Models - Evaluate the risk and potential impact of litigation.
    • Sentiment Analysis for Legal Opinions - Analyze sentiment in legal opinions and case summaries.
    • Case Prioritization Models - Prioritize legal cases based on urgency and importance.
    • Client Onboarding and KYC Models - Automate the Know Your Customer (KYC) process for legal firms.
    • Billing and Time Tracking Models - Automate billing processes and track time spent on legal cases.
    • Legal Chatbots - Provide automated legal assistance and answer client queries.
    • Alternative Dispute Resolution Models - Support mediation and arbitration processes.
    • Regulatory Change Monitoring Models - Track and alert on changes in legal regulations.
    • Data Privacy Compliance Models - Ensure compliance with data privacy laws such as GDPR.
    • Language Translation Models for Legal Documents - Translate legal documents accurately across multiple languages.
  • Manufacturing:
    • Predictive Maintenance Models - Predict equipment failures before they occur.
    • Quality Control Models - Automate defect detection during production.
    • Supply Chain Optimization Models - Optimize supply chain operations and logistics.
    • Inventory Management Models - Predict optimal inventory levels and reduce excess stock.
    • Production Scheduling Models - Optimize production schedules to maximize efficiency.
    • Energy Optimization Models - Reduce energy consumption and improve efficiency.
    • Process Automation Models - Automate repetitive manufacturing tasks.
    • Demand Forecasting Models - Predict product demand to better plan production.
    • Robotic Process Automation (RPA) - Automate mundane tasks using AI-driven robots.
    • Predictive Analytics for Raw Materials - Forecast raw material needs and manage procurement.
    • Digital Twin Models - Create virtual replicas of physical systems for analysis and optimization.
    • Worker Safety Monitoring Models - Monitor and enhance workplace safety using AI.
    • Supply Chain Risk Management Models - Predict and mitigate risks in the supply chain.
    • Product Lifecycle Management (PLM) Models - Optimize the product development cycle.
    • AI-driven CAD Design - Enhance computer-aided design processes with AI.
    • Anomaly Detection Models - Identify anomalies in manufacturing processes to prevent defects.
    • Natural Language Processing (NLP) for Maintenance Logs - Analyze maintenance logs to identify recurring issues.
    • Real-time Monitoring and Control Systems - Monitor production lines in real-time for efficiency.
    • Custom Product Configuration Models - Enable customization of products based on customer requirements.
    • Sustainability Models - Optimize manufacturing processes to reduce environmental impact.
  • Retail:
    • Personalized Recommendation Engines - Suggest products based on customer behavior and preferences.
    • Demand Forecasting Models - Predict future product demand to optimize inventory.
    • Customer Segmentation Models - Group customers based on purchasing behavior for targeted marketing.
    • Dynamic Pricing Models - Adjust prices in real-time based on demand, competition, and other factors.
    • Visual Search Models - Allow customers to search for products using images.
    • Chatbots for Customer Support - Provide automated assistance and answer customer queries.
    • Inventory Management Models - Optimize stock levels and reduce inventory costs.
    • Fraud Detection Models - Identify and prevent fraudulent transactions.
    • Sentiment Analysis Models - Analyze customer reviews and social media for sentiment insights.
    • Store Layout Optimization Models - Optimize store layout for better customer flow and product visibility.
    • Customer Lifetime Value (CLV) Prediction Models - Predict the long-term value of customers.
    • Supply Chain Optimization Models - Enhance efficiency and reduce costs in the supply chain.
    • Product Recommendation Models for Cross-Selling - Suggest complementary products to customers.
    • Augmented Reality (AR) Models - Provide virtual try-ons for products like clothing or makeup.
    • Churn Prediction Models - Predict which customers are likely to stop buying and take preventive actions.
    • Price Optimization Models - Find the optimal pricing strategy to maximize profits.
    • Shelf Stocking Optimization Models - Automate and optimize shelf stocking based on demand.
    • Loyalty Program Analysis Models - Analyze and optimize loyalty programs to increase customer retention.
    • In-Store Analytics Models - Track and analyze customer behavior in physical stores.
    • Promotional Effectiveness Models - Evaluate and optimize the impact of marketing promotions.
  • Transportation:
    • Autonomous Vehicle Navigation Models - Enable self-driving cars to navigate roads safely.
    • Traffic Prediction Models - Predict traffic patterns to optimize routes and reduce congestion.
    • Fleet Management Optimization Models - Improve fleet efficiency by optimizing routes and schedules.
    • Predictive Maintenance Models - Anticipate maintenance needs to reduce downtime for vehicles.
    • Driver Behavior Analysis Models - Monitor and improve driver performance and safety.
    • Logistics and Supply Chain Optimization Models - Streamline transportation logistics for better efficiency.
    • Demand Forecasting for Ride-Sharing Models - Predict demand for ride-sharing services in specific areas.
    • Route Optimization Models - Calculate the most efficient routes for deliveries and travel.
    • Accident Detection and Prevention Models - Detect and predict potential accidents to enhance safety.
    • Real-Time Passenger Information Systems - Provide passengers with real-time updates on transportation schedules.
    • Cargo Load Optimization Models - Optimize cargo loading for better space utilization and fuel efficiency.
    • Smart Traffic Signal Control Models - Adjust traffic signals dynamically to reduce congestion.
    • Public Transit Scheduling Models - Optimize public transit schedules based on passenger demand.
    • Drone Delivery Path Planning Models - Plan optimal routes for drone deliveries.
    • Toll Collection Optimization Models - Automate and optimize toll collection processes.
    • Energy Consumption Optimization Models for EVs - Optimize energy usage for electric vehicles.
    • Environmental Impact Assessment Models - Assess and reduce the environmental impact of transportation.
    • Customer Satisfaction Prediction Models - Analyze feedback to improve transportation services.
    • Incident Response Models - Automate responses to traffic incidents and emergencies.
    • Vehicle Sharing Optimization Models - Optimize the allocation and availability of shared vehicles.

If you want to hear more about how Octopus Software can improve the productivity of your company employees, please contact Metric Space.

Technical documentation