Data Pipeline Engineering
Production pipelines for enrichment, normalization, and intelligence collection — with yield monitoring, failure documentation, and infrastructure you own.
What we build.
Every pipeline runs on your infrastructure. You own the code, the data, and the results.
Property & Contact Enrichment
Multi-source enrichment pipelines that process large property databases — geocoding, ownership lookup, contact enrichment — with pre-filtering to minimize paid API calls and yield monitoring that stops the pipeline if something breaks.
- →100K+ properties enriched
- →$0.05 per contact
- →Yield monitoring per batch
Multi-Source Data Normalization
Pipelines that unify data from incompatible systems into a single schema. Different identifier types, different naming conventions, different timing models — resolved into a queryable unified data layer your business can actually use.
- →5+ source types
- →Entity resolution across systems
- →Financial reconciliation
Court & Legal Intelligence
Scrapers and API connectors for public court data sources — clerk APIs, bulk subscriptions, and LLM-powered extraction from portals without structured APIs. Deposition discovery, hearing schedules, docket events, attorney data.
- →Hundreds of proceedings/day
- →75% cost reduction
- →Multi-county coverage
Global Intelligence Collection
Daily collection pipelines across 41+ official sources — conflict data, sanctions, cyber threats, government advisories, humanitarian data. Structured for decision-support rather than raw data dumps.
- →195 countries covered
- →Daily cadence
- →$0.02/day for scraping tier
How we build.
Four rules that apply to every pipeline we've ever shipped.
Cache before you spend.
Every paid API result is cached locally before writing to any remote system. If the downstream write fails, replay from cache costs $0. This rule has saved five-figure API budgets on pipelines that would otherwise have re-run paid calls.
Pre-filter in application code.
SQL deduplication is the first filter. Python set-checks are the mandatory safety net before any paid API call. Duplicates that SQL misses for naming convention reasons get caught in application code.
Monitor yield, not just errors.
A pipeline that runs without errors but produces zero new records is broken. Yield monitoring — tracking new records per batch against expected rates — catches silent failures that error monitoring misses entirely.
Document every mistake.
When a bug wastes budget, we quantify it to the dollar and present it fully. Not buried in a status update. The client who saw our 19% waste analysis expanded the engagement. Transparency competes better than silence.
Pipeline running but not producing?
The most common pattern: a pipeline that runs without errors and produces zero useful output. We audit end-to-end — data sources, endpoints, extraction logic, dedup strategy — and fix what's actually broken before building new.
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