AI platform for Specialty Chemicals
Your R&D
remembers everything
Mafer turns your company's technical history — formulas, lab results, regulatory records — into model-ready intelligence, embedded in your R&D workflows.
Formula name
Fragrance compound — woody floral
Raw materials
Composition
↕ Auto-order · Total 100.0%Tip: the filler auto-completes to 100% — every panel recomputes live on each dosage change.
Olfactive profile
Each accord is drawn relative to the strongest — recomputed on every dosage change.
Cost
€16.40 per kg
per application · ρ 0.98 g/mL
target €19.00 · SAP standard cost · EUR @ 1
Agents
Years of formulas, lab results and regulatory records become one governed layer — the foundation your team, and your models, run on.
Formulation
Models trained on your historical formulas shorten the path from brief to compliant candidate — without limiting expert creativity.
Regulation
Dozens of sources, percentage rules and market-specific exceptions centralized in one agentic system, applied consistently across formulas and raw materials.
Analysis
Lab and instrument data structured at source — from chromatograms to physico-chemical results — comparable, traceable and ready to train models on.
Today, decades of technical knowledge live across Excel files with macros, lab notebooks, instrument outputs, ad hoc software — and the minds of a few experts.
Mafer centralizes it into one information layer where R&D teams structure their history, train models on it, and make every past experiment count towards the next one.
Your lab runs on expensive analytical hardware tied to legacy software. Replacing it is not an option — integrating with it is.
Mafer connects directly to GC-MS, LC-MS, spectrometers, ERP platforms and legacy lab environments, structuring high-fidelity data at source without disrupting how your team works.
Designed for industries where molecular composition, analytical instrumentation, and formulation workflows define product development. From Fragrances and Flavours to Cosmetics and Personal Care, these sectors share a common foundation: complex chemical systems and highly specialized data.
Lab Data Capture
GC-MS, LC-MS and spectrometers
Structured Data
Model-ready, traceable at source
Private Models
Fine-tuned on your history
Industrial Scale
Research connected to execution
A new generation of infrastructure now allows that accumulated knowledge to become the foundation of AI-native operations. Four things have kept it locked away:
Fragmented Data
Proprietary, complex and scattered — each company's IP, locked across silos and experts.
Legacy Stacks
Core R&D workflows still run on technology built 10–15 years ago.
Process Complexity
Technical workflows, regulation and hardware keep generic software out.
Research–Industry Gap
Advances in ML for chemistry rarely make it into daily industrial practice.
Eight weeks, on your own data. We start from plain exports, so there is nothing to integrate with your ERP, and we agree on the KPIs before the pilot begins.
Data audit
We map what you have — ERP records, Excel macros, instrument outputs, scanned notebooks — and tell you exactly what's usable. No cleanup required on your side.
Build
We build the proof on your own data — structuring it, automating the workflow, or training a model on your history. Whatever your case calls for, your data stays exclusively yours.
Validation
Your team tests it on your own formulas, against the KPIs agreed upfront — before any long-term commitment.
FAQ
Real questions from R&D, IT and quality teams across our conversations — answered straight.
No. Each client gets their own models, fine-tuned exclusively on their own data. Nothing is pooled across clients, and nothing you share with us ever improves anyone else's system. What remains after training is your model — raw data is never reused beyond it.
Wherever you decide. We deploy in the region each client determines, whether that is Southern Europe or any other location, and we work with all major cloud regions. Data lives on enterprise-grade infrastructure, encrypted in transit and at rest. For companies with stricter policies we deploy inside your own cloud environment, and on-premise for the strictest ones. Our security architecture documentation is available on request.
That's the starting point for almost every client. Every engagement begins with a data audit: we map what exists — ERP records, spreadsheets with macros, instrument outputs, even scanned notebooks — and tell you exactly what's usable before any commitment. You don't need to clean anything first; structuring messy technical data is precisely what Mafer is built for.
A typical pilot runs about eight weeks: two of data audit, four of building on your data, two of validation against KPIs we agree on upfront. It runs on plain exports, with no ERP integration to start, and asks around three hours per week from your team.
Less than you'd expect. We start from base models built for your domain and fine-tune them on your history, so even small datasets move the needle. Quality beats volume: complete iteration records — trials, failures, adjustments — teach a model more than thousands of final formulas alone.