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Project Aurras - MVP - Phase 2

  • Team Name: HugoByte AI Labs Private Limited
  • Payment Address: Fiat 20.10.2023, 06:33 UTC
  • Level: 2

Project Overview 📄

This is an application for a follow-up grant:


Aurras is a middleware that acts as an event processor and a low code workflow orchestration platform. Aurras is being pitched as a next-generation system for enabling decentralized push notification. This middleware solution listens to events from blockchain applications and propagates them to a registered pool of MQTT brokers. The broader architecture consist of parachain from which the middleware listens for the events.

Aurras as a part of Substrate / Kusama / Polkadot / Web 3 Ecosystem

  • A core part of Decentralized Push Notification Architecture
  • Can be used for business-critical and production-grade IoT devices to listen to events from blockchain
  • Can be used with microservices across industry verticals
  • Can be expanded to get integrated with data analytics, knowledge visualization, and machine and deep learning (ML/DL) algorithms

Project Details


Flow Diagram


  1. Apache OpenWhisk is an open source, distributed Serverless platform that executes functions (fx) in response to events at any scale. OpenWhisk manages the infrastructure, servers and scaling using Docker containers. The OpenWhisk platform supports a programming model in which developers write functional logic (called Actions), in any supported programming language, that can be dynamically scheduled and run in response to associated events (via Triggers) from external sources (Feeds) or from HTTP requests
  2. Apache Kafka is an open-source distributed event streaming platform. Kafka works well as a replacement for a more traditional message broker. Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc.). In comparison to most messaging systems, Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications.
  3. Tackle box is a declarative DSL for building modular utilities and code generators. Tackle box is plugins based and can easily be extended by writing additional hooks or importing external providers creating a web of interoperable CLIs.
  4. Docker
  5. Rust
  6. Go


1. Event Sources (Substrate Event Feed)
This is the source for events from the blockchain and the events will be sent to the OpenWhisk system. The MVP event source will be implemented using polkadot-js/api. For the actual implementation, we will be using a custom version of the substrate archive. Substrate archive will be indexing the blocks and an event actor will be decoding the events and data from the block and posting them to the OpenWhisk system.

The indexer will use a NoSQL database to store the last indexed block and metadata. Using the substrate indexer as our event source will help us in solving many challenges ranging from fault tolerance to scalability to uniquely identifying multiple events with the same payload within a single block. The indexer will be connected to a substrate-based chain and RocksDB where the chain data is stored. When a new block is produced by the chain, the indexer collects the raw blocks from the RocksDB and sends it to the event actor. The event actor processes the block. The block data is decoded using the parity scale codec and mapped with the data types provided to the runtime. Once the event is decoded, the event actor posts a trigger to the event trigger manager.

2. Event Trigger Manager
Event trigger manager is composed of multiple OpenWhisk actions, including a database to store trigger URLs and their respective auths, the Kafka provider, consumer and producer. Once the event manager receives an event from the event feed, this data is produced to a topic. The feed action in the trigger manager lets the user hook into the system. That is, once an event is indexed to a particular topic, it can invoke a particular action. While creating the workflow, users can choose the event trigger as feed and provide necessary parameters from which chain it should be listening to and from which block it should start listening.

Under the hood, a feed action is invoked with create lifecycle, which accepts the mandatory parameters the lifecycle, auth, trigger name, and other optional parameters of the event source. The feed action invokes the related actions of creating the entry in the database, adding to the Kafka consumer group, etc. The next component in the event trigger manager is a persistent connection to Kafka where it is used to produce and consume the stream of data. Once data is received in Kafka, the event trigger manager invokes the action to check the consumer groups for that particular topic and if found any, the trigger for the users under that particular group is invoked, which in turn invokes the workflow.

3. Workflow Composer
The workflow composer is at the heart of Aurras and is responsible for composing and deploying the necessary workflows. It consists of an async Rust library to compose multiple triggers, a deployment configuration generator, providers for Tackle Box, several operators, macros and a whisk deployment tool.

To create a workflow, the only necessary input is the YAML configuration file. The composition, direction of flow and tasks are laid out in the YAML file. This YAML is parsed using Tackle Box, which in turn composes it into a WASM which can be deployed to OpenWhisk. Once deployed to a namespace, each workflow will have a unique workflow id.

Workflow configuration comprises the endpoint URL for workflow tasks, the sequence of processing tasks, and argument structure. Arguments must match the task input parameters.

4. Web API Gateway and Backend Service
This is the end-user facing component that enables easy and intuitive usage of Aurras. This final component is has a backend application that provides user registration, workflow registration and workflow management. Users can also easily create, select or manage workflows along with providing the necessary input parameters.

Ecosystem Fit

Similar projects

What makes us different is

As a part of the Web 3 Community and Ecosystem

  • First class support to connect blockchains
  • Deployment and definition of workflows will be done through easy-to-write YAML configuration.
  • Workflow tasks can be RPC calls or OpenWhisk actions which can be written in multiple languages - NodeJS, Go, Java, Scala, PHP, Python, Ruby, Ballerina, .NET and Rust (We prefer and recommend WASM-compatible Rust)
  • First class support to MQTT Endpoints.

Team 👥

Team members

  • Muhammed Irfan K - CTO & Co-Founder - HugoByte AI Labs
  • Hanumantappa Budihal - CIO, Project Manager & Solutions Architect, DevOps - HugoByte AI Labs
  • Shreyas Kura Subramanya - Development Team Lead - HugoByte AI Labs
  • Shanith KK - Systems Developer - HugoByte AI Labs
  • Amogha Sudarshan - Project Analyst - HugoByte AI Labs
  • Immanuel Stanley - Junior Blockchain Tester - HugoByte AI Labs


  • Registered Address: HugoByte AI Labs Private limited, 1st & 2nd Floor, IBIS Hotel, Hosur Road, Bommanahalli, Bengaluru, Karnataka, India, 560068
  • Registered Legal Entity: HugoByte AI Labs Private limited, CIN: U72900KA2020PTC140106

Team's experience

Muhammed Irfan K
Muhammed Irfan K is a strategic thinker and implementer of innovative ideas with an emphasis on educating and bringing back privacy to the people. Having co-founded HugoByte AI Labs and serving as its CTO, he envisions a world where AI is open, distributed and democratized by building a decentralized infrastructure and policy framework to regulate it. He has extensive experience in designing and building Highly Scalable Enterprise Architecture, leading high-impact teams, Full-Stack Development, Microservices and Machine Learning.

Hanumantappa Budihal
Hanumantappa Budihal is the Chief Information Officer and Solutions Architect at HugoByte AI Labs. He has worked for 4 years as an Application Developer and Azure DevOps Engineer. He is adept at designing, describing and managing the solution engineering for specific business problems by finding the best tech solution by analysing all possibilities. He is proficient in defining features, phases, and solution requirements. He is currently invested in decentralising the blockchain space and is working on the enhancement of the Blockchain Transmission Protocol to achieve it.

His technical skills include ML, AI, Blockchain, .NET Core Framework, Azure DevOps, SQL Developer, ASP .NET MVC, REST Web API, WPF, Microsoft Azure, Cognitive Services, Algorithms Design for a complex business requirement, and Watson Discovery, with good knowledge of Application Architecture and Design pattern implementation and data analysis skills. He has also worked on cognitive application development using various IBM Bluemix services such as Conversation, Natural language understanding, Watson discovery, and has developed chatbots using and Microsoft bot framework. As a testimony to his exemplary technical skills, he is now pursuing his MTech in computer science at IIT Madras.

Shreyas Kura Subramanya
Shreyas is an experienced software developer with great zeal in implementing solutions in the software domain. He is skilled in Blockchain, smart contracts, ethical hacking, Rust and Golang. He currently is the Development Team Lead at HugoByte AI Labs and is instrumental in driving the team to great success. He obtained his Bachelor's degree focused in Computer Science from Bapuji Institute of Engineering & Technology, Davanagere.

Shanith K K
Shanith is a Software Engineer and a Web developer. Having graduated his M.Sc in Software Engineering, he is a diligent and industrious technocrat. He has published a paper titled on "Segmentation: A review" in JETIR (JETIRCG06006). He has worked on machine learning using python programming. He has substantial and working knowledge about programming languages like Rust, Python, C, C++, and he is currently working on blockchain development in HugoByte AI Labs. He has an insatiable passion for coding and is exceptionally good at problem-solving.

Team Code Repos

Team LinkedIn Profiles (if available)

Development Status 📖




Development Roadmap 🔩


  • Description Development of Project Aurras - MVP - Phase 2
  • Total Estimated Duration: 528 Person Days
  • Full-time equivalent (FTE): 4
  • Total Costs: 42880 USD

Milestone 1 — Workflow Composer - Part 1

  • Estimated Duration: 30 Working days
  • FTE: 4
  • Costs: 19200 USD
  • Estimated Delivery Date: 20/01/2023
NumberDeliverableSpecificationUser Story
0a.LicenseApache 2.0
0b.DocumentationWe will provide the following documentation: Inline Code Documentation, Flow design, Readme file
0c.Testing GuideWe will compose a testing guide to describe how to run tests. The code will have unit-test coverage (min. 50%) to ensure functionality and robustness
1a.Workflow Composer: Rust OpenWhisk Client LibraryWe will create a Minimal Implementation to OpenWhisk Rust ClientUsers can invoke workflows that employ certain pre-deployed actions in the OpenWhisk.
1b.Workflow Composer: ComposerWe will build a Wrapper Library compose yaml to wasmUsers can compose the WASM from the workflow YAML.
1c.Workflow Composer: PipeWe will build an interface to connect other operators and tasksUsers can facilitate the usage of the output of one task as the input of the next.
1d.Workflow Composer: structured YAML fileWe will provide a YAML file with a predefined structure to configure the workflow composerUsers can utilize the pre-defined YAML file to create custom workflows.
1e.Workflow Composer: Concat OperatorWe will build an operator interface to merge two flowsUsers can facilitate the concatenation of two tasks to get a combined final output that can be consumed by the next task.
1f.Workflow Composer: Map OperatorWe will create an operator to map the inputs to the outputs of each flowUsers can facilitate the transformation of the output of one task to a format required by a subsequent task.
2Predefined boiler plateWe will build a predefined Rust code for flow generationUsers can create workflow WASM that utilizes the boilerplate code to implement the necessary functionalities that carry out the tasks defined in the YAML.
3a.Flow Provider for Tackle BoxWe will create a provider to employ the necessary hooks for the workflowUsers can use the flow provider to generate the code to implement the flow of data between the tasks.
3b.Task Provider for Tackle BoxWe will create a provider that will generate a struct with the given macros and argumentsUsers can use the task provider to generate the code necessary to execute the tasks defined in the workflow YAML.
3c.Workflow Provider for Tackle BoxWe will create a provider that will interface the operators and tasksUsers can use the workflow provider to generate the code necessary to execute the workflow as defined in the workflow YAML.

Milestone 2 Workflow Composer - Part 2

  • Estimated Duration: 40 Working days
  • FTE: 4
  • Costs: 10880 USD
  • Estimated Delivery Date: 03/02/2023
NumberDeliverableSpecificationUser Story
0a.LicenseApache 2.0
0b.DocumentationWe will provide the following documentation: Inline Code Documentation, Operator Documentation, Flow design, Readme file
0c.Testing GuideWe will compose a testing guide to describe how to run tests. The code will have unit-test coverage (min. 50%) to ensure functionality and robustness
0d.Docker FileWe will provide docker file for running the workflow composerUsers can utilize the docker image as a cli tool to generate workflow wasm from the provided yaml
1a.Workflow Composer: Flow MacroWe will create three types of flows: init, pipe and terminator. The init is used to create the first task in the workflow and the terminator is used to create the last task. The pipe method is used to create other tasks that accept inputs and are dependent on other tasksUsers can utilize the flow macro that is used by the flow provider to generate the code to implement the flow of data between the tasks.
1b.staking and payout features for scs/substrate-api-clientstaking functionalities and payout example. fixed bugs in staking modules

Milestone 3 — Web API/Backend

  • Estimated Duration: 68 Working days
  • FTE: 4
  • Costs: 12800 USD
  • Estimated Delivery Date: 06/03/2023
NumberDeliverableSpecificationUser Story
0a.LicenseApache 2.0
0b.DocumentationWe will provide the following documentation: Inline Code Documentation, Operator Documentation, Flow design, Readme file
0c.Testing GuideWe will compose a testing guide to describe how to run tests. The code will have unit-test coverage (min. 50%) to ensure functionality and robustness
0d.ArticleWe will write a Medium article that explains the work done as part of the grant
1a.Web API: Workflow RegistrationWe will create an API that is Exposed as a part of workflow deployment, where workflow will be registered as an OpenWhisk action and made available to specific namespaceUsers can deploy a workflow to execute the required tasks for a particular scenario.
1b.Web API: User RegistrationWe will create an API for User to register themselvesUsers can register themselves to the system and receive a unique ID.
1c.Web API: User Workflow ManagementWe will create an API for User to select workflow and provide argument values, Pause Workflow, Delete WorkflowUsers can update the inputs of a registered workflow.
Users can pause an active workflow.
Users can unfreeze a paused workflow.
Users can delete a registered workflow.
2a.Workflow Yaml Polkadot PayoutsWe will create a YAML file that defines the workflow for claiming the Polkadot validator payouts to the provided walletUsers can create a workflow YAML to claim PolkaDot payouts using the pre-defined YAML file.
2b.Reward OpenWhisk actionsWe will create OpenWhisk actions to index the validator details to the Kafka topic that will invoke the validator payout workflowUsers can utilise the pre-deployed OpenWhisk actions to validate the validator ID and check claims for the particular validator.
2c.Claim workflow moduleWe will create a plugins to carry out the tasks of claiming the validator rewards to the provided wallet as defined in the workflowUsers can claim Polkadot payout rewards using the registered workflow.
2d.Polkadot API Derive macro for workflow taskWe will create derive macros that will provide the necessary functionalities for the task struct to perform RPC callsUsers can facilitate the execution of tasks that utilize the derive macro to perform RPC calls.
3.Video TutorialWe will create a set of video tutorials to guide the users to use this MVP

Future Plans

Immediate Plans in the timeline Includes

  • Develop full fledge system
  • Implement Custom Version of Substrate Archive
  • Add more operators
  • Add Analytics and Monitoring
  • MQTT Endpoints
  • Decentralized push notification

Our Roadmap includes

  • Decentralized Pub/Sub Network to replace Apache Kafka in Aurras
  • WASI Runtime for OpenWhisk

Additional Information ➕

How did you hear about the Grants Program?
We came across the Web3 Grants Program while perusing about the developments in the Web3 ecosystem on the Web3 Foundation Website.

Here you can also add any additional information that you think is relevant to this application but isn't part of it already, such as:

Update & Amendments


We have encountered a critical issue where we faced workflows fails due to certain Openwhisk actions timing out hence we took an alternative approach where we have made Polkadot API wasm compatible and included as a plugin to the wasm, due to this the timeline has now been exceeded by several months and has made certain components we developed obsolete.

  • Estimated Duration: 45 Working days
  • FTE: 1