🧭 Foundry MVP Architecture - Comprehensive Overview

1. Executive Summary

Goal:

Develop an MVP platform that replicates the core capabilities of Palantir Foundry — transforming fragmented enterprise data into continuous operational intelligence — using open-source and cloud-native components.

Scope:

The MVP unifies three key capabilities:

1. Closed-loop analytics and feedback capture
2. A graph-based ontology modeling entities, events, and processes
3. AI-driven intelligence loops that evolve from both user and system decisions

2. Strategic Context & Business Objectives

3. Functional Overview

3.1 Foundry's Closed-Loop Operational Paradigm

Traditional analytics architectures are linear, moving from data ingestion to dashboards. Foundry redefines this as a bidirectional system that not only delivers insights but captures decisions and feeds them back into models and processes.

This closed loop ensures that every user interaction strengthens the data foundation and operational models.

3.2 The Palantir Ontology

→ View detailed Ontology Layer documentation

Together, these layers form a digital operational twin — a continuously updating model of enterprise reality.

4. Core Architecture Proposal

4.1 Layered Architecture Overview

4.1.1 Architectural Notes — DuckDB-Specific Adjustments

1. Simplified Compute & Storage Footprint:
   • DuckDB operates directly on Parquet/S3 data, removing the need for Spark clusters or separate warehouses.
   • Ideal for small-to-mid workloads and early-stage startups aiming for minimal infrastructure overhead.
2. Native Integration with dbt:
   • dbt-duckdb allows defining semantic transformations as SQL models, directly building analytical tables for ontology ingestion.
   • Supports incremental models, materialized views, and lineage tracking.
3. Semantic Layer Bridge:
   • Transformed tables in DuckDB represent the canonical "truth layer."
   • An export service (Python microservice or dbt task) syncs object relationships to the Neo4j Ontology graph.
4. Operational Efficiency:
   • Analytical queries, joins, and aggregations execute in-memory via DuckDB with vectorized execution.
   • Perfect for powering Superset dashboards or feature generation without latency typical of remote warehouses.
5. Data Scale Considerations:
   • Works best for datasets up to ~100–500 GB active footprint (per node).
   • For larger scales, integrate MotherDuck or Trino over DuckDB later, maintaining the same data model.
6. Feedback Integration:
   • Decision feedback or ML outcomes are stored in DuckDB tables and periodically merged into the training set or semantic graph, maintaining local consistency.
7. Deployment Simplicity:
   • Single binary + S3 connectivity = minimal maintenance.
   • Perfectly containerized for Kubernetes jobs or local testing pipeline.

4.1.2 Recommended Extensions (DuckDB Roadmap)

• MotherDuck Integration: scale compute elastically via cloud DuckDB.
• Materialized Ontology Views: store entity-relationship snapshots in DuckDB for analytics.
• Streaming Connectors: use DuckDB's incremental ingestion mode for Kafka topics.
• Direct Feature Engineering: compute ML features in DuckDB and publish to Feast offline store.

4.2 Data & Control Flow

1. Data Sources → ETL (Airbyte/dbt) → Semantic Graph (Neo4j)
2. Events → Kafka → Update Kinetic Layer (entity state changes)
3. Models → MLflow + Seldon → Bind predictions to entities
4. Operator or System Decisions → Feedback API → Kafka feedback topic
5. Feedback → Dagster retrains models → Updated models redeployed

4.3 Ontology Integration within the Closed Loop

• Semantic Layer: Establishes a consistent business context for all data.
• Kinetic Layer: Streams continuous operational updates.
• Dynamic Layer: Applies AI-driven optimization and captures new learnings.

These layers collectively realize a self-reinforcing operational intelligence loop.

5. Technology Stack & Open-Source Alternatives

6. Data & Workflow Model

6.1 Semantic Modeling Example

(Customer)-[:PLACED]->(Order)
(Order)-[:CONTAINS]->(Product)
(Product)-[:SUPPLIED_BY]->(Vendor)
(Asset)-[:LOCATED_AT]->(Facility)

Each node aggregates attributes from multiple source systems (CRM, ERP, IoT).

6.2 Kinetic Event Model

Event Example:

{
  "event_id": "uuid",
  "timestamp": "2025-11-12T09:32Z",
  "type": "ORDER_SHIPPED",
  "actor": "Order_789",
  "target": "Customer_123",
  "attributes": {"status": "delivered"},
  "source": "logistics_api"
}

This event updates the graph and triggers downstream logic, maintaining a real-time operational view.

6.3 Dynamic Intelligence Feedback Loop

• Models (e.g., churn prediction) are bound to entities via metadata.
• Predictions are published as events.
• Decision outcomes are captured and merged into labeled training datasets.
• Dagster orchestrates retraining workflows, completing the learning loop.

7. Functional Decomposition

8. Implementation Roadmap

Estimated MVP timeline: ~4–5 months for a 4–5 member data/ML team.

9. Risks & Mitigations

10. Competitive Insight

11. Suggested Future Extensions (Version 2.0)

1. Ontology Builder UI: Drag-and-drop schema and relationship editor
2. Temporal Graphing: Time-based versioned entity states
3. Simulation Layer: "What-if" scenario modeling
4. Causal Inference Engine: Policy optimization through causal ML
5. Domain Ontology Templates: Industry-specific accelerators
6. AI Copilot: LLM-powered schema mapping and graph querying
7. Edge & IoT Integration: Real-time feedback from devices and sensors
8. Data Provenance & Trust Scoring: Traceable, auditable decision lineage