AI Engineering Knowledge Graph Platform
An internal AI platform that converts circuit diagrams, DTC specs, and system docs into a connected, searchable knowledge graph engineers can actually query.
Start a Similar ProjectAutomotive & Embedded Systems Engineering
AI & Machine Learning / Custom Software Development / Cloud Solutions
Internal Engineering Intelligence Platform
Python, FastAPI, Neo4j, Qdrant
RESULTS THAT MOVED THE NEEDLE.
Engineering artifact types unified into one processing pipeline
Graph model for connected components, faults, and metadata
Semantic search across technical entities via local embeddings
Every run logged, archived, and packaged for export
In automotive and embedded engineering, the knowledge that actually matters is scattered across circuit diagrams, DTC specifications, system descriptions, IO mappings, and base configuration files. Each document is useful on its own, but engineers lose hours re-interpreting the same PDFs and XML files every time a question comes up, and that manual interpretation doesn't scale as ECU families and documentation sets multiply.
We built an engineering intelligence platform that ingests every one of those artifact types through one orchestrated pipeline, extracts the relationships between them, and stores the result as connected knowledge instead of static files. A graph-based workflow engine routes each document through the right specialized processor, resolves system and ECU-family context, and writes structured entities into a knowledge graph that engineers can actually query, by exact match or by meaning.
INSIDE THE PLATFORM.
Six Artifact Types, One Pipeline
Circuit diagrams, DTC specs, system descriptions, IO mappings, base configurations, and function parameters all flow through a single, repeatable ingestion process.
Graph-Orchestrated Processing
A defined execution graph routes each inference through system analysis, circuit interpretation, IO processing, exporting, logging, and archiving, extensible without rebuilding the pipeline.
Neo4j Knowledge Graph
Components, relationships, DTC links, and metadata are modeled as a graph, so engineers can trace how systems, faults, and data actually connect, not just search flat records.
Semantic Search With Qdrant
Embeddings generated locally via Ollama power similarity search across components and function groups, surfacing relevant knowledge even when naming conventions vary.
Export-Ready, Auditable Outputs
Every run produces packaged exports, archived inputs and outputs, and structured logs, so results are traceable and ready for downstream tooling, not locked inside the platform.
API & Webhook Delivery
A FastAPI layer exposes inference creation, file upload, background execution, and signed webhook callbacks, so the platform can run as a service other systems call into.
“Too much high-value engineering knowledge sits trapped in documents that are technically rich but operationally hard to reuse. Turning that into connected, searchable knowledge is the difference between teams that depend on effort and teams that benefit from systems.”