Expert Data Analysis For Anyone

Add data. Ask questions. Reveal insights — without engineers.

What We’re Building
JUXX22 is an autocoder that uses plain English to build complete data pipelines.AI agents plan, test, and construct Lego-like components that create a visual blueprint, making complex workflows intuitive. Each component is validated in isolation with synthetic data making it easy for non-technical users to customize, build upon, and explore new directions.While compatible with nearly any dataset, we intend to be the first to effectively populate your data into dynamic knowledge graphs. These graphs serve as an enhanced memory layer for reasoning and context reuse between your queries, which creates a self-improving loop in which each analysis adapts as your insights evolve.Easily analyze, quickly iterate, and discover tailored insights at scale.


How It Works (Demo)


Step By Step

  1. Enter your query in natural language.

  2. JUXX22 analyzes the problem and goal, then disassembles it into the raw elements needed to engineer a complete data pipeline solution.

  3. AI assembles these elements into modular components with specialized roles and responsibilities tailored to your goal.

  4. JUXX22 then constructs a solutions blueprint, defining how the components communicate via strictly typed input/output ports to ensure data integrity, adaptability, and scalability.

  5. JUXX22 then writes code for each component individually, optimizing the process and supporting complex workflows and diverse data sources.

  6. Each component is tested in isolation using AI-generated sample data, to ensure alignment with your goal.

  7. Errors are automatically detected and corrected at the component level, preventing cascades typical of monolithic systems.

  8. Validated components are configured into a cohesive and adaptive system.

  9. JUXX22 conducts comprehensive end-to-end testing, ensuring all components work together seamlessly; if not, it continues troubleshooting until they do.

  10. Data is populated into knowledge graphs to add an enhanced memory layer for reasoning, learning, and context reuse.

  11. JUXX22 then delivers an intuitive visual map offering full transparency into how each part functions and fits together. Including plug-and-play components that can be swapped between queries for easily customized configurations.

  12. Build, iterate, and scale data pipelines with a flexible architecture that self-improves as your insights are fed back into the knowledge graph.


Why JUXX22 Scales
Memory vs. Computation separation – The knowledge graph can grow organically, while the autocoder pipelines stay lean and task‑specific.LLM feasibility – Each step is small enough to fit into an LLM context window for code‑gen/debug.Evolvability – You can swap storage back‑ends, add new operator libraries, support MCP, or point the autocoder at a different dataset without rewiring everything.


How JUXX22 Handles Any Data Pipeline Challenge.
JUXX22 decomposes complex data flows into four modular, testable, type-safe components—avoiding the fragility of monolithic systems and enabling precise, adaptable pipelines.1. DataObject - The Structured Carriers
Hold typed data at various stages of processing, ensuring consistent input/output contracts.Example: RawDocument, ProcessedText2. Processor - The Transformers
Transforms one DataObject into another.Example: TextExtractor converts RawDocument into ProcessedText3. Router - The Intelligent Directors
Analyze DataObjects and route them to the correct downstream processors based on content or format.Example: FormatRouter sends content to either HTMLProcessor or MarkdownProcessor4. Reducer - The Result Aggregators
Merge multiple processed outputs into a unified result for downstream use.
Example: FormatCollector merges formatted outputs into a single ProcessedText DataObject


Type-Safe Communication Through Ports and Schemas
All components communicate through strictly typed input and output ports, preserving structure and validation throughout the pipeline.Runtime validation is enforced via Pyadantic schemas attached to each DataObject.Both Routers and Reducers are composable, meaning they can be nested to support more complex or recursive workflows without breaking modularity.


Advanced Workflow With Routers and Reducers
Router and Reducer components enable complex conditional paths and result aggregation, unlocking sophisticated flows without breaking modularity.

-Router (FormatRouter) splits flow between HTML/Markdown processors.
-Reducer (FormatCollector) merges their outputs.


Iterative Development With Focused Testing
Each component is tested independently using LLM-generated test data. This modularity allows:
-Rapid debugging
-Focused validation
-Multiple feedback loops for system improvement


What Differentiates Us

  • Goal-driven pipeline generation – Converts natural-language objectives into complete, testable Python pipelines built step-by-step.

  • Modular, schema-defined architecture – Workflows are composed of isolated, typed components with ports, routers, and loops.

  • LLM-compatible design – Each step fits within a language model’s context window for reliable code generation and debugging.

  • Agent-native infrastructure – Enables autonomous agents to plan, coordinate, and execute structured, auditable workflows.

  • Integrated knowledge graphs – Acts as a long-term memory layer for reasoning, context reuse, and knowledge retention.

  • Self-improving loop – Insights are written back into the graph, allowing workflows to improve with each run.

  • Visual transparency – All processes are visualized as directed graphs for easy inspection, editing, and version control.

  • Composable and flexible – Processes, data sources, MCP interfaces, and process steps can be swapped without breaking the system.

  • Unifies reasoning and execution – Combines structured memory, dynamic planning, and tested execution in a single system.

  • Blueprint-driven control layer – All logic and connections originate from a centralized declarative blueprint, making workflows easy to inspect and modify.

  • Clear roles, clean boundaries – State, logic, routing, and aggregation are separated into distinct components, making behavior predictable and reducing complexity.

  • Isolated side effects – State changes and external interactions are contained within specific modules, promoting clarity and trust.

  • Machine-readable architecture – The entire system is structured for easy parsing and understanding by both AI agents and human developers.

  • Tests in isolation – Every component is validated independently to ensure reliability, simplify debugging, and enable reuse.



Explanation Of Our Vision



Abstract Thinking: The Art Of Pruning And Knowledge Graphs (Scale-Free)



Contact Me - [email protected]
https://www.linkedin.com/in/-tyler-mills/