We Build Pipelines That Run Themselves

Real-time traffic data flows in from hardware sensors across multiple vendors — Bluetooth detectors, radar units, video analytics systems. Each vendor has a different API, different data format, different polling interval. We normalize all of it into a unified schema and run it through travel-time calculation, segment aggregation, and anomaly detection before it hits the database.

On top of the real-time layer, batch jobs run hourly and daily aggregations: FHWA-compliant travel time indices (TTI/PTI/BTI), near-miss safety metrics (TTC/PET), and EPA MOVES emissions estimates. Archive jobs compress and store raw data. Monitoring jobs watch the whole thing and alert when collectors go silent.

A distributed intelligence system that autonomously collects, processes, and connects information across domains. 167 collectors run on scheduled cycles — scraping, polling APIs, monitoring feeds, and ingesting structured data. Each collector normalizes its output into a common signal format tagged with source, confidence, and domain metadata.

Incoming signals feed into a multi-stage enrichment pipeline: entity extraction, relationship mapping into a Neo4j knowledge graph, vector embedding into Qdrant for semantic search, and time-series storage in TimescaleDB. LLM inference runs on local GPU hardware, producing summaries, assessments, and cross-domain correlation reports on 4-hour autonomous cycles.

A personal command center that aggregates data from multiple life domains — journal entries, habit tracking, financial transactions, goals, project status, and external feeds. Incoming data flows through validation and normalization before landing in domain-specific stores.

Scheduled analysis jobs run pattern detection across domains: correlating habits with goal progress, tracking financial trends against budgets, and surfacing tasks that are falling behind. An LLM layer generates weekly summaries and actionable recommendations based on the cross-domain analysis. Everything runs on self-hosted infrastructure with zero external data custody.

An AI-driven development pipeline that accepts build proposals and runs them through a 16-state process to produce tested code. Proposals enter a planning queue where an LLM breaks them into implementation steps. Each step flows through a generation queue that writes code on isolated Git branches, then into a validation queue that runs tests and static analysis.

Three Celery queues handle the different stages with different concurrency and priority settings — planning is serial, generation is parallel across proposals, validation runs with dedicated resources. Failed validations loop back to generation with error context. Successful builds produce tested branches ready for human review. The whole pipeline runs autonomously once a proposal is submitted.