What We Build.
Start With a Diagnostic
Before we write a line of code, we sit down with your team for a free 90-minute Data Readiness Diagnostic. We map your data sources, pipelines, and bottlenecks to understand where you are and what's blocking progress.
No commitment, no sales pitch. Just a clear-eyed look at your data landscape from people who've built these systems before.
What We Cover
Current data sources and formats. Pipeline architecture and tooling. Team capabilities and workflow bottlenecks. Regulatory and reproducibility requirements.
What You Get
A 2-3 page Data Readiness Report: current state assessment, top 3 risks to your data strategy, and prioritized recommendations you can act on immediately.
No Strings Attached
The diagnostic is free. The report is yours to keep. If we're a fit, we'll scope a project together. If not, you still walk away with a clear picture of where you stand.
Three Ways
We Work.
Data Science & Engineering
Genomic pipelines (Cell Ranger, STAR, Salmon), proteomic analysis, multi-omics integration (DESeq2, Scanpy, Seurat), and reproducible analysis environments. From sequencing data to soil sensors to clinical trials — if it's scientific data, we build the infrastructure to make it useful.
Production ML Systems
PyTorch, scikit-learn, and XGBoost models deployed on AWS/GCP with full MLOps — MLflow for experiment tracking, Docker and Kubernetes for containerization, CI/CD for automated retraining. Predictive models, anomaly detection, classification pipelines. Working code in your repo, not a slide deck.
Enablement & Training
Team workshops, pair programming sessions, AI tool integration, and hands-on support embedded in your workflow. We build your team's capability, not dependency.
What We Don't Do.
We don't stop at the report.
Reports are starting points, not deliverables. Our diagnostic gives you a roadmap — but our real output is working code committed to your repository, documented and tested. Systems, not slide decks.
We don't pretend to know your domain.
We bring deep computational biology expertise, but your scientists know your science best. We listen first, then build systems that amplify what your team already knows.
We don't create dependency.
Every engagement includes documentation, knowledge transfer, and pair programming. Our goal is to make your team self-sufficient -- and then come back because you want to, not because you have to.
How Engagements Work.
Discovery Call
A 20-minute conversation to understand your team, your data, and what you're trying to build. No prep required on your end.
Data Readiness Diagnostic
A focused 90-minute session where we map your data landscape and deliver a prioritized readiness report. Free, no commitment.
Scoped Project or Retainer
Based on the diagnostic, we define a clear scope -- deliverables, timeline, and cost. Project-based sprints or ongoing retainer, depending on what fits.
Embedded Delivery
We join your Slack, work in your repos, and ship alongside your team. The same senior people you met are the ones doing the work.
Handoff & Documentation
Full documentation, knowledge transfer sessions, and pair programming to ensure your team owns everything we built. Clean handoff, no lock-in.
What It Costs.
Data Readiness Diagnostic — Complimentary
A 90-minute assessment with a written roadmap. No commitment.
Project Sprints — $15K – $50K
Scoped, fixed-fee engagements. A 2-week pipeline audit runs ~$15K. A 6-week full-stack ML build is ~$40-50K. You get a fixed price before we start — no hourly surprises.
Ongoing Retainers — Starting at $8K/month
Embedded support for teams that need continuous data science capability. Includes Slack access, regular syncs, and priority scheduling.
Workshops & Training — $3K – $8K
Half-day or full-day hands-on sessions. AI tool integration, pipeline development, team upskilling.
Not sure what you need? Start with the free diagnostic. We'll help you figure out the right scope.