◆ DATA ENGINEERING × AI / ML STARTUPS

Data engineering development for AI/ML startups

ETL, warehouses, lakehouses, real-time pipelines. We design for query cost, freshness SLOs, and analyst happiness. Tuned for ai / ml startups — From the eval harness up. We help AI startups ship past the demo and into production — with cost ceilings, observability, and a real moat.

◆ WHY MIR

Why teams pick us

AI / ML startups-grade engineering

From the eval harness up. We help AI startups ship past the demo and into production — with cost ceilings, observability, and a real moat.

Data engineering where it counts

ETL, warehouses, lakehouses, real-time pipelines. We design for query cost, freshness SLOs, and analyst happiness.

Senior team, no pyramid

9+ years building outsourced product teams. Every engineer is a senior shipping production code — not a 5-person team with one architect on top.

◆ AI / ML STARTUPS PAIN POINTS WE SOLVE

Where Data engineering meets ai / ml startups

Evals that catch regressions before users do

We've shipped this in production. We design for it from day one — not as a retrofit.

Cost ceilings on LLM calls

We've shipped this in production. We design for it from day one — not as a retrofit.

RAG pipelines that survive real document corpora

We've shipped this in production. We design for it from day one — not as a retrofit.

Production observability for non-deterministic systems

We've shipped this in production. We design for it from day one — not as a retrofit.

◆ STACK

What we build with

dbt Snowflake / BigQuery / DuckDB / Redshift Airbyte / Fivetran / custom CDC Kafka / Kinesis Dagster / Airflow Great Expectations / dbt tests
◆ PROCESS

How we ship

  1. Audit current pipelines — cost, freshness, ownership, debt.
  2. Design the warehouse layer with reverse-ETL in mind.
  3. Add data quality tests + lineage from day one.
  4. Ship with self-serve docs + a query-cost dashboard.
◆ START A PROJECT

Tell us what you're building

We respond within 24 hours. NDA available on request.