Why your batch variation isn't random
Most manufacturers assume batch-to-batch variation is noise. It rarely is. Here's how ML reveals the signal.
5 min readRead →
PhD specialists · Machine learning · Net zero
ML-powered formulation and process improvement
We turn your existing batch records, process logs, and quality data into actionable improvements — using machine learning and PhD-level formulation expertise.
Process control chartLive analysis
Process drift detected
Post-intervention
0%
process improvement = millions of tonnes CO₂ avoided
What we do
Machine learning has the most impact where the clues are already there — buried in your experimental records, process logs, and quality data.
Data analysis
ML pattern detection
We analyse your existing experimental and process data to capture patterns that are invisible to conventional analysis. Batch-to-batch variation, process drift, hidden correlations — machine learning finds them.
Optimisation
Ingredient and process optimisation
Once patterns are identified, we model the relationship between ingredients, process parameters, and output properties — then identify the fastest route to improvement.
Quality assurance
Human-reviewed AI reporting
Every AI-generated analysis is evaluated by PhD specialists for scientific accuracy and meaning. You get reports that are stable, repeatable, and grounded in well-defined operations.
Regulatory compliance
Cosmetic safety reports
We produce compliant Cosmetic Product Safety Reports (CPSRs) for UK and EU markets, combining toxicological assessment with formulation expertise. Every report is authored and signed off by a qualified safety assessor.
Net zero
Built for a net zero future
Reducing waste, improving yields, and cutting energy use are not just cost improvements — they are sustainability gains. Every optimisation we deliver moves your process closer to net zero.
Scope 1 & 2
Fix your scope — identify where energy and emissions are concentrated in your process
Existing data
Clues are hidden in your current experimental and process records — no new sensors required
Compounding gains
Small, repeatable improvements accumulate into significant long-term emissions reductions
From the lab
Practical insight on formulation, process data, and machine learning from our team.
Control charts: the most underused tool in QC
SPC has been around for decades. Yet most QC teams still use it wrong — or not at all.
4 min readRead →
What makes an AI-generated report trustworthy?
Speed is easy. Accuracy is harder. Here's the review process we use to ensure every report stands up to scrutiny.
6 min readRead →
Ready to get more from your process data?
Tell us what you're working with. We'll tell you what's possible.
PhD specialists
Every analysis reviewed by domain experts
Existing data
No new sensors or data collection required
Human evaluation
AI output checked for scientific accuracy
Net zero aligned
Process improvements that reduce carbon