The pipeline you need,
built by someone who gets your data.

AI Research Operations · Biomedical Labs

Wet-lab context, quantitative training, and hands-on pipeline experience for biomedical labs that need code, analysis, and documentation to match their research workflow.

Book a call → See what we do

Prefer async? Email hello@laminarbio.com with your dataset, goal, timeline, and current tools.


Example result

MOVAT-stained aortic section image processing with a documented QuPath workflow.

Batch size 600–700 section images processed
Workflow time 4–5 hrs automated processing and review
Manual baseline 8–10 hrs previous manual review estimate

1

Histology and microscopy image analysis

QuPath pipelines for stained tissue sections, microscopy images, segmentation, morphometry, and batch export. Workflows are documented and reproducible, with a deployed example for MOVAT-stained aortic sections and atherosclerosis lesion quantification.

Microscopy tissue cross-section

2

Research data pipelines and statistical analysis

R and Python workflows for experimental data cleaning, joins, QC checks, statistical modeling, and reproducible tables. Common analyses include logistic regression, count models, survival analysis, and mixed-effects models for labs with repeated analysis needs.

Hands with flowing water streams

3

Lab operations databases

Structured systems for records that are currently split across spreadsheets: animal colony records, genotyping logs, cage assignments, weaning schedules, sample inventories, and shared lab trackers. Built in a stack your institution can support and your lab can maintain.

Microphone or microscopy setup

4

Workflow audit and implementation plan

A 2–3 hour working session to map where AI, automation, or conventional scripting can realistically improve a research workflow. You leave with a prioritized roadmap, expected inputs and outputs, and a clear first implementation project.

Multi-channel pipette

Research background

Cardiovascular biology at UVA's Sonkusare Lab — TRP channels, calcium signaling, atherosclerosis. Real bench experience.

Quantitative training

Neuroscience + Statistics at UVA. Logistic, Poisson, NB regression. R and Python daily.

Clinical fluency

Certified EMT. We understand clinical data, patient-facing context, and translational stakes — not just the bench.

Builder, not advisor

We ship tools that non-developers actually use. Strategy is step one, not the deliverable.


Built for real lab constraints

Laminar is designed for institutional reality: workflows can be delivered in local files, documented scripts, or institution-approved stacks your lab can actually maintain. We scope around IT restrictions, shared-drive habits, handoff to trainees, and compliance-sensitive work so the result is usable inside your lab, not just impressive in a demo.


1

30-min scoping call

You describe the problem, we ask the right questions. We figure out if there's a fit and what a scoped engagement looks like, including data inputs, expected outputs, timeline, and handoff needs.

2

Proposal + quote

Within 48 hours, you get a plain-language proposal: what we'll build, what you provide, how long it takes, what it costs. Fixed-fee for projects, hourly for retainers.

3

Delivery + handoff

Code is documented. Pipelines are annotated. You or a grad student should be able to re-run everything six months from now without calling us. That's the standard we hold.



Have a dataset or workflow that needs a reproducible path forward? Let's talk.