Delivery lane
Signals and AI Automation Trend detection and agent-driven operations with engineering-grade controls.Signal intelligence
Trend Detection Signal Pipeline
One ranked view of what's rising, instead of five open tabs and a gut feeling.
- Python
- Signal scoring
- dbt
- DuckDB
Problem
Trend work is usually scattered across feeds, social dashboards, and search tools with no central scoring logic.
Solution
News, social, and search feeds get pulled into one normalized scoring layer, so rising topics surface with a number attached instead of a gut feeling.
Outcome
Content, growth, or research teams get one ranked view of momentum instead of five open tabs.
Repository scope
4 core delivery blocks A compact build with practical deliverables and visible operating behavior.Operating posture
Controlled AI and signal workflows Built with the same logging, retries, and validation you would expect from a workload running in production.What ships in this repository
- News, social, and search ingestion model
- Normalized scoring layer
- Trend-ranking analytics mart
- Alert-ready publish output
Stack and operating model
- Python
- Signal scoring
- dbt
- DuckDB
- Alert-ready outputs
- Observability
News, social, and search feeds pulled into one scoring layer, so a rising topic shows up with a number attached instead of someone's hunch.
Why buyers pick this type of build
- Frames AI and signal automation as an operations system, not a demo.
- Useful when teams want faster execution but still need logs, contracts, and rollback-safe flows.
- Helps connect inbound signals, decisions, and downstream actions in a maintainable way.
How delivery stays reliable
- Structured logs, validation steps, and predictable run behavior.
- Replay-safe processing paths designed to reduce duplicate work and broken publishes.
- Repository structure that another engineer can extend without re-learning the whole system.