◆ MLOPS × AI / ML STARTUPS

MLOps development for AI/ML startups

Training, eval, deployment, monitoring of ML and LLM systems. Reproducible runs, drift detection, cost-aware inference. 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.

MLOps where it counts

Training, eval, deployment, monitoring of ML and LLM systems. Reproducible runs, drift detection, cost-aware inference.

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 MLOps 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

Modal / RunPod / AWS Weights & Biases / MLflow DVC / LakeFS Triton / vLLM Prometheus + custom drift checks
◆ PROCESS

How we ship

  1. Reproducibility first — frozen envs, pinned data, lineage tracking.
  2. Eval harness before any production rollout.
  3. Canary + shadow deploys for every model update.
  4. Drift + cost dashboards from day one.
◆ START A PROJECT

Tell us what you're building

We respond within 24 hours. NDA available on request.