The AI system for
verified microscopy pipelines.

Design, validate, and govern AI-powered microscopy workflows with transparent lineage and reproducible evidence from every run.

The operating layer for verified microscopy AI.

Fovea Lab connects scientists, AI models, QC, and institutional knowledge into transparent microscopy pipelines, turning images into trusted scientific evidence.

01·Experts

Keep scientists in the loop.

Bring domain experts closer to analysis with workflows they can inspect, guide, and approve without burying decisions inside black boxes.

02·Agents

Bounded agents around your pipeline.

Agents explain results, inspect lineage, and suggest workflow changes inside sandboxed, human-approved guardrails.

03·Evidence

Validated insight needs a verified pipeline.

Lineage, QC, and reproducible execution turn image-derived results into evidence scientists can trust, review, and defend.

04·Adaptation

Adaptive pipelines for rare biology.

Handle unusual assays, rare phenotypes, and edge-case imaging with modular workflows that adapt without losing rigor.

05·Knowledge

Build microscopy on your knowledge graph.

Connect images, assays, models, lineage, and decisions to institutional knowledge so every run gains context and compounds insight.

These principles become one executable, QC-aware pipeline system.

Start a conversation

Or reach us at hello@fovealab.com

From image to insight

The pipeline behind the insight

  2. QC: manifest validation report

  3. QC: focus / illumination QC map

  4. QC: mask confidence report

  5. QC: % replicating + control summary

Model development & monitoring

The ML stages above are built and kept honest by a separate loop. Each model trains on the output of the step before the node it powers, then deploys back into it.

  1. Curate Evidence Sets
  2. Train & Stress-Test
  3. Validate & Calibrate
  4. Register & Deploy
  5. Monitor & Retrain
  • Correction / restoration model → powers correct, trains on ingest output
  • Segmentation model → powers segment, trains on correct output
  • Representation / embedding model → powers profile, trains on segment output
  • MoA / phenotype model → powers interpret, trains on validate output

Built to hold up

Engineered to hold up

A trusted result is more than a model output. It is a pipeline with controls, provenance, reproducibility, and a clear audit trail.

No black box

Built from tools your scientists already trust — CellProfiler, Cellpose, StarDist, BaSiCPy, Pycytominer — plus your own code. Every step is open to inspect or swap.

Reproduce it exactly

Each run pins its code, parameters, container, and model weights. Reproduce a result months later, or re-run the same configuration on new data.

QC at every step

Every stage emits a checkable artifact — image, mask, profile, and metadata QC. Bad runs surface early instead of contaminating the result.

Runs where data lives

One pipeline runs on a workstation, your HPC, or the cloud — no rewrite to move. Your data never has to leave its environment.

Pilot to full screen

The pilot pipeline is the production pipeline — tiled, parallel, resumable. Scale throughput without re-engineering the analysis.

Built for terabytes

Whole-slide and high-content runs hit terabyte scale. The pipeline streams and tiles the data so jobs finish and stay tracked.

Tell us about your system.

Whether you’re running a core facility, a high-content screen, or a custom-built instrument — we’d like to hear about it.

Start a conversation

Or reach us at hello@fovealab.com