Connecting your physical lab to digital analytics.

I build custom pipelines that automate instrument setup and turn fragmented wet-lab data into decisions. Six years of wet-lab R&D behind every one.

Book a free call Try the tools

Three tools you can open now

Free · runs in your browser BatchSightBioprocess digital twin Upload fed-batch data. Get kinetic fits, anomaly detection and yield models. Try with your data → Free · runs in your browser NextRunExperiment planner Enter your variables and past runs. Get the next batch from Bayesian optimisation. Plan your next run → Interactive · simulated Lab OrchestratorHuman-in-the-loop screening Design an example screen, then simulate a 72 h run with an operator in the loop. Design and simulate →

The problem

Where lab workflows break

Four patterns I see again and again, none of them solved by a new instrument.

Data wrangling

Scientists spend hours normalising and merging outputs by hand.

Configuration

Turning DoE designs into robot protocols takes manual programming and invites transcription errors.

Synchronisation

Process metrics sit in separate systems and are aligned manually, which delays decisions.

Hardware first

A new $100k instrument is the default answer when manual transfer steps are the real bottleneck.

Certified Professional

PRINCE2 Agile® Practitioner

Approach

Automation is mostly a data problem.

Digital first

Highly skilled scientists lose bandwidth to manual normalisation and cross-format alignment, not to missing robots.

Connect what you have

Bespoke pipelines link experimental design directly to machine execution, where standard software stops.

Optimise before you buy

The highest leverage is usually in the workflows between your existing assets.

As project lead for our HTS and data analysis, Lagan architected our automated digital infrastructure. She developed custom Python scripts to connect disparate hardware and software systems, significantly reducing manual interference and error rates. By implementing rigorous evaluation parameters and advanced visualization methods, she directly drove increases in our overall process yield.

— Dr. Sebastian Rakers, Co-founder & CEO, BLUU GmbH

Work

Selected work

Video walkthrough

BatchSight: Bioprocess Digital Twin & MVDA Platform

Bioprocess telemetry in; kinetic models, anomaly detection and yield forecasts out.

Interactive simulation NextRunPlan the next batch with Bayesian optimisation and test it on a simulated lab. Simulate a run → Interactive simulation Lab OrchestratorDesign an example screen and simulate it with a human in the loop. Break it and watch the recovery. Design and simulate → Open source Code on GitHubOrchestrator with 239 tests and a validation pack, NextRun planner, and BatchSight analysis. View on GitHub → Case study DoE execution on the iDOT384-well setup cut from about 6 hours to under 1 minute, with zero transcription errors. Read the PDF →

By discipline

Process

How we work together

A short diagnostic before any capital is spent.

Discovery

A 30-minute call to review your bottlenecks, assays and instruments.

Workflow audit

I map samples and data end to end to find manual redundancies and idle hardware.

Blueprint

A tailored design, from Python data-parsing scripts to native liquid-handling protocols.

Deployment

Rollout with minimal disruption, SOPs and hands-on training.

Contact

Book a free call

30 minutes, no sales pressure. Describe your most manual workflow and we will map where scripts can remove the work.

Book a free call

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