This first milestone lays the groundwork for the Hetzerk project by assembling the core team and defining the system architecture with a focus on knowledge graph integration. The team will establish communication channels and outline a comprehensive project blueprint. This blueprint will detail how a knowledge graph will be used to integrate AI (including AGI-oriented algorithms) with large-scale scientific simulation data. Key activities include brainstorming requirements, identifying data sources (e.g., molecular simulation outputs, quantum chemistry results), and designing the knowledge graph schema to represent physical systems, experimental results, and algorithmic insights. By the end of this phase, the project will have a clear technical roadmap that emphasizes how scientific data will be structured and refined in the knowledge graph and how AI components will interact with this knowledge base. This milestone ensures everyone on the team understands the objectives and technical approach, setting a strong foundation for subsequent development.

A detailed Project Blueprint document and initial system setup. This blueprint will include: Architecture Specification: Diagrams and descriptions of the platform’s architecture, highlighting the knowledge graph’s role in storing and connecting scientific data with AI modules. Knowledge Graph Schema & Ontology: A defined schema or ontology outlining key entities (e.g. molecules, simulation parameters, results) and relationships for the knowledge graph, ensuring it can accommodate various scientific datasets and support reasoning tasks. Data & Integration Plan: Documentation of how diverse scientific data (from simulations, experiments, etc.) will be collected, transformed, and loaded into the knowledge graph (i.e., a plan for the data refinement pipeline). This includes identifying file formats and preliminary parsing strategies for each data type. AGI Integration Plan: A design section detailing how AI/AGI components will interface with the knowledge graph – for example, how a machine learning model or cognitive engine will query/update the graph for enhanced reasoning. Team & Resource Allocation: A roster of the project team with roles and a development timeline, ensuring that the expertise (AI, data engineering, domain science) is aligned with each component of the plan. Initial Prototype (if feasible): Optionally, a rudimentary prototype or proof-of-concept demonstrating a small subset of the knowledge graph to validate technology choices.

$10,000 USD

This milestone is successful when the project has a well-defined plan and initial setup that guides all future work. Specific success criteria include: Completed Project Blueprint: A comprehensive blueprint is finalized and reviewed by key stakeholders. It should clearly capture all major components (knowledge graph, data pipeline, AI integration) and outline how they fit together. Reviewers should agree that the design is technically sound and addresses the project goals of AGI-aligned knowledge integration and data refinement. Knowledge Graph Design Approval: The knowledge graph schema/ontology is validated through peer review or expert feedback to ensure it appropriately models the necessary scientific information and can be extended as new data types are added. Any required revisions from the review are incorporated. Infrastructure Ready: Basic project infrastructure is set up (e.g., version control repositories, development environments, perhaps a preliminary graph database instance) so that development can begin immediately in the next milestone. Milestone Review Sign-off: Project advisors or funders have reviewed the milestone outcomes (documentation and plans) and given a go-ahead to proceed, indicating confidence that the project is well-planned and ready for implementation.

In the second milestone, the project moves from planning into building the core system – integrating the computational framework with the knowledge graph and implementing the scientific data refinement pipeline. This is a development-intensive phase where the team will create the backbone of the platform. Key tasks include setting up the knowledge graph database and populating it with sample data, developing tools to ingest and refine data from various scientific computations, and integrating AI algorithms to both produce and consume knowledge graph data. For example, simulation outputs (from molecular dynamics, quantum chemistry, etc.) will be parsed by custom scripts or converters and then stored as structured entries in the knowledge graph. Simultaneously, the platform’s AI components (e.g., machine learning models or reasoning engines) will be connected so they can query the knowledge graph for insights or update it with new results. Throughout this milestone, iterative testing will be performed to ensure the system can handle complex computational tasks and data flows. By milestone completion, the Hetzerk platform’s core functionality – a loop that takes raw scientific data, refines it into a knowledge graph, and leverages it for AI-driven analysis – will be implemented and demonstrated on a pilot scale.

A prototype of the integrated platform along with associated documentation and test results including: A deployed graph database containing representative data. For instance, a running instance of the knowledge graph with several sample datasets (e.g., a molecular dataset, a materials science dataset) ingested. The schema from Milestone 1 is realized in this database, and one can query it for stored knowledge. A suite of data processing scripts or modules capable of converting raw output files from at least two types of scientific algorithms into the structured format required by the knowledge graph. Example deliverables may be parsers for molecular simulation trajectories and quantum calculation outputs that import results (e.g., energy values, molecular structures, metadata) into the graph. The data pipeline should also perform refinement steps such as cleaning inconsistent units or merging duplicate entries, so the knowledge graph information is consistent and reliable. The core computational engines (simulation or AI algorithms) integrated with the system. For instance, if a molecular dynamics simulation code is part of the project, it is configured to output results that feed directly into the knowledge graph via the pipeline. Additionally, an AI module is set up to pull data from the knowledge graph. A concrete example could be a neural network that reads simulation results from the graph to predict an outcome or suggest new simulation parameters.

$20,000 USD

This milestone is considered achieved when the integrated platform can handle real data and computations in a controlled test environment, proving out the concept of coupling a knowledge graph with scientific computing and AI. The success criteria are: The system successfully processes raw scientific data through to the knowledge graph and subsequent AI analysis without manual intervention. For instance, given a new simulation result file, the pipeline ingests it into the knowledge graph, and an AI query on the graph yields meaningful information. The graph is populated with refined data that is accurate and useful. This can be verified by checking that the ingested data in the graph matches original data sources (ground truth) and is organized correctly (e.g., units converted where necessary, linked to relevant entities without errors). A successful criterion could be that 100% of test dataset entries appear correctly in the graph and can be retrieved via queries that demonstrate the linking of related information . Basic performance benchmarks are met. For example, the system can ingest a typical dataset within a reasonable time, and query responses from the knowledge graph for test queries are within acceptable latency. Success might be defined as ingesting a dataset of size X in Y minutes, and answering a complex query (spanning multiple linked records in the graph) in Z seconds. These metrics ensure the solution is scalable to larger problems in the next phase.

The final milestone focuses on deploying the Hetzerk platform as a Minimum Viable Product (MVP) and validating its performance and integration with advanced AI (AGI) components in a real-world context. In this phase, the system transitions from a prototype in a test environment to a robust platform accessible to end-users or stakeholders (for example, via a user interface or API on a cloud or decentralized network). The knowledge graph will be fully populated with a larger volume of refined scientific data, and the pipeline will be refined for reliability. Crucially, this stage will integrate an AGI-oriented element: for instance, incorporating a cognitive engine or large language model that uses the knowledge graph to perform complex reasoning or to assist users in drawing conclusions from the data. The team will also engage initial users (such as an industrial partner or domain experts) to test the platform on real use cases – e.g., running a new molecular simulation on the platform and using the AI assistant to interpret the results via the knowledge graph. Throughout this milestone, feedback is gathered and the system is tuned for accuracy, security, and scalability. By the end, the MVP will demonstrate the platform’s value in accelerating scientific discovery through AI and knowledge graph integration, meeting both technical benchmarks and user expectations.

A deployed MVP system along with validation reports and user feedback. The key deliverables are: The Hetzerk platform is deployed in an accessible environment. This could be a cloud deployment or a decentralized network deployment. The deployment includes a front-end interface or console for users to submit computational jobs and view results, and the back-end comprising the knowledge graph database and integrated AI services. A working integration with an advanced AI system is delivered as part of the MVP. For example, an AI assistant or agent service that can be queried in natural language or logical queries about the data in the knowledge graph. This could be implemented via a large language model connected to the knowledge graph , or an OpenCog Hyperon module using the graph for reasoning. The deliverable would include the AI component itself and a description of how it connects to the knowledge graph. A demo execution using the MVP on a realistic scenario. For instance, Industrial-scale Molecular Simulation Demo: the team runs a complex molecular simulation through the platform, the data automatically populates the knowledge graph, and then the integrated AI analyzes the results to produce insights (like identifying promising compounds or explaining phenomena). The outputs of this demonstration – including the knowledge graph entries created, the AI’s conclusions or responses, and performance metrics – are compiled.

$20,000 USD

The MVP deployment and testing will be deemed successful when the platform operates in a real-world setting and delivers tangible results through its AI-enhanced knowledge graph approach. Success criteria include: The system successfully handles a substantial, real-world dataset. For instance, at least one large-scale simulation or dataset is ingested and managed in the knowledge graph. The success can be measured by data volume and complexity (multiple types of data linked in the graph). The refined knowledge graph should show that even with increased scale, data remains consistent and queryable. If applicable, integration with blockchain for data integrity (as hinted by Cardano integration) is demonstrated without performance loss. The integrated AI/AGI component provides a successful demonstration of advanced insight or decision support. A clear success criterion would be that the AI agent correctly answers complex questions or solves a problem that would be hard to do without the knowledge graph. For example, the AI might identify a non-obvious correlation in the simulation data that leads to a new hypothesis or optimization. Achieving a meaningful result like this – confirmed by domain experts or experimental validation – would illustrate the power of combining knowledge graphs with AGI techniques. Even in absence of a dramatic discovery, the criterion could be that the AI assistant can interact with users about the data as per the project requirements.