Data Engineering
Pipelines that hold their water in a storm. Ingestion, modeling, and warehousing built to last longer than the trend cycle.
Learn the craft →Data engineering · AI / ML · Strategy
We do serious things with data.
We just refuse to be boring about it.
Engineering, machine learning, and strategy — delivered by people you'd actually want on the voyage.
Chart a course →What we're good at
AI / ML
Models that earn their keep — not science projects. We ship the boring 80% that actually moves the metric.
Learn the craft →Strategic Guidance
A map and a navigator. We help you decide what to build, what to skip, and when to change course.
Learn the craft →From the logbook
Engineered-infrastructure manufacturer
- The trouble
- Expensive CRM licensing, and no idea which of hundreds of tables actually mattered.
- Our heading
- Migrated all 548 tables (144 GB) into Snowflake with per-table validation, then kept only the 61 with real value.
- Landfall
- Zero data loss — and a clean target instead of a decade of clutter.
Electrical-equipment manufacturer
- The trouble
- Hours of manual takeoff from electrical schematics against a 200+ page spec.
- Our heading
- Hybrid symbol detection (text + vision-AI via Claude) with a citation-aware RAG pipeline.
- Landfall
- Draft BOMs in hours instead of days, every line citing its source.
Commercial real-estate investor
- The trouble
- A whole acquisition-scoring methodology trapped in a fragile spreadsheet.
- Our heading
- Custom Domo app scoring 10,000+ properties in-browser with real-time metric re-weighting.
- Landfall
- Live, re-weightable scoring tailored to each investor on the fly.
Cargo delivered
548 tables 144 GB migrated with zero data loss
~$40 → ~$0 per-customer nightly cost, re-platformed away
≥80% AI agreement with human safety assessors
hours, not days to draft a bill of materials
10,000+ properties scored in-browser, in real time
$99.4M backlog validated; stale reference caught first
We're here to make cool shit with cool people.
Start small, prove value fast, then tackle the big problems together — because the best data work happens when sharp people who like working together build something genuinely good.
Meet the crew →