Introduction

Imandra
Connectivity

Imandra
Connectivity

Powered by breakthrough AI for algorithm analysis and automated reasoning technology

Digitize FIX “specs” to remove bugs and ambiguity, apply AI to auto-generate test cases and onboarding packs, apply ML to extract actionable intelligence from production FIX flow and much more.

Key
Values

  • Digitise Specification

    Remove all ambiguity and create machine readable (and reasonable!) specifications:

    • Improve accuracy
    • Increase efficiency
    • Improve testing
    • Improve collaboration
    • Decrease markets fragmentation

    Take the development and collaboration experience to the next level.

  • Automate Testing

    Apply AI to auto-generate conformance and onboarding test packs, execute them and create coverage and certification reports:

    • Improve accuracy and consistency
    • Instant feedback
    • Increase confidence
    • Leverage scalability

    Ensure that algorithms perform as expected in real-world scenarios, no matter how complicated.

  • Monitor the performance

    Apply AI to extract actionable intelligence from your FIX flow to quickly troubleshoot any issues and ensure your customers take full advantage of your services:

    • Improving operational efficiency
    • Enhancing safety and security
    • Enabling data-driven decision making
    • Improving customer satisfaction
    • Increasing revenue

    Intelligent solution providing improve operational efficiency, higher safety, better customer experience, and increased revenue.

DisruptionLet's redefine
FIX Connectivity

Imandra Connectivity is AI for designing, auditing, and calibrating complex trading connectivity.

As the part of the Imandra Markets, our solution is the end-game for the algorithmic part of trading connectivity.

We connect all of the stakeholders

We connect all of the stakeholders involved in FIX connectivity over a digitized specification, and bring AI to bear on the entire gateway lifecycle:

Design

  • Digitize the FIX API with special programming language - Imandra Protocol Language (IPL).
  • Rules and requirements are coded as executable programs prone to AI analysis.
  • Optimised for automation and co-simulation with real market data.

Test

  • IPL model leveraging Imandra to generate a test suite.
  • Test suite covers all edge cases.
  • Execution of tests against the system ensures consistency between system and IPL model.
  • Co-simulation of IPL model with production data allows explanation of every customer interaction.
  • Identify all issues early.

Convey

  • Communicating the FIX specification to your customers is easy.
  • Automatically create specyfication from your system IPL model.
  • Let customers spin up IPL-generated simulators on-demand and get answers to any questions they might have.

Onboard

  • Automatically generate onboarding scenarios for clients.
  • Select desired scenarios for customer certification.
  • Web-based onboarding expirience.
  • Customer walkthrough of scenarios.
  • Support for two certification modes: Simulator and Proxy.
  • Generation of certification report upon successful completion.

Monitor

  • Ensure clients are making the most of your API (their systems may have bugs too!)
  • Quickly troubleshoot any issues they might have.
  • Extract actionable metrics covering how your customers are using your platform.
  • Get additional assurance that your production system operates in accordance with the specification.

System
Features

On-Demand
Simulators

Converts IPL models into customizable simulators and investigate behaviour.

Any IPL model can be quickly turned into a simulation environment. No additional scripting required - Imandra will use the logic contained within an IPL model to simulate how your system may respond to the client's requests. These simulators are highly customizable and leverage Imandra's powerful constraint solving techniques to replicate the non-trivial behavior of your system.

On Demand Simulators
Flow Insights

Flow
Insights

Uncover valuable insights from client sessions.

Extract actionable intelligence from your clients' sessions. Flow Insight overlays the IPL-derived edge cases onto the session log files to give you unprecedented and systematic insight into how your clients use your systems.

Rule
Synthesis

Extract logical patterns from complex sequential data

Rule Synthesis is a Machine Learning (ML) technique for extracting logical patterns from complex sequential data (e.g., FIX logs). Imandra's Rule Synthesis feature speeds up IPL development by analyzing historic log files and converting recognized logical patterns into an IPL model. While manual review/edit of the IPL model would still be required (as the logs may not reflect the entire current state of the interface), Rule Synthesis gets you 80% there.

  • Aggregates all relevant data and checks every outcome against the formal model.
  • Visually displaying individual traces and the aggregate performance statistics.
  • Know even the smallest functional deviation occurs.
  • No other tool will give you this level of insight into how your system operates.
Rule Synthesis
IPL Interfaces

Imandra Protocol
Language (IPL)

Imandra Protocol Language is a domain-specific language for encoding ROE (Rules of Engagement) documents.We created IPL to make it as easy as possible to precisely encode documents like FIX specifications.
Unlike an MS Word document, IPL forces the developer to be very precise, so the resulting model is susceptible to analysis by AI. With a push of a button, you can turn an IPL model into a documentation PDF, an on-demand simulation environment, a production log auditor, or use IPL to generate actionable client intelligence.

Check out IPL page!

Region
Decomposition

Imandra's Region Decomposition is a novel AI technique for exploring systems' vast (virtually infinite) state-spaces

  • Inspired by testing methods used in avionics for autopilot systems.
  • Identify unique edge cases in system behaviors.
  • Construct test vectors and compare results against the formal model.
  • Useful for systems with complex and dynamic state-spaces.
  • Improves system reliability and performance.
  • Reduces the time and resources needed for testing.
Region Decompposition

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