December, 2025

November, 2025

Q3–Q4 2025 (Jul–Present) - Hardening for scale

Theme: Durability over novelty

Capabilities delivered

• Performance hardening for enterprise datasets

• Improved automation and environment management

• Incremental refinements driven by production usage

Why it mattered: Ensured the platform scales with customer ambition without fragility

Q2 2025 (Apr–Jun 2025) - Mass Dynamics 3.0

Theme: Extensibility and system-level thinking

Capabilities delivered

• General availability of Workspaces

• Custom scripting and extensibility

• Peptide-level analysis

• Multiple deployment models

Why it mattered: Marked the transition from a proteomics platform to a scientific system.

Q1 2025 (Jan–Mar 2025) - Collaboration without compromise

Theme: Allowing multidisciplinary teams to work together without custom code.

Capabilities delivered

• Shared analytical context

• Reduced dependence on external scripting

• Faster iteration across roles

Why it mattered: Enabled biologists, proteomics experts, and computational scientists to work in one system.

Q4 2024 (Oct–Dec 2024) — Enterprise readiness

Theme: Reducing friction for regulated and larger organisations.

Capabilities delivered

• Enterprise security and identity foundations (Single Sign-On, SSO)

• Performance and reliability at scale

• Improved export and reporting

Why it mattered: Removed blockers for adoption beyond small teams and pilots.

Q3 2024 (Jul–Sep 2024) - From features to workflows

Theme: Supporting real experimental workflows across experiments and collaborators

Capabilities delivered

• Cross-experiment analysis (Dose–response curves and volcano plots across experiments)

• Richer visual context for decision-making

• Early collaboration patterns 

Why it mattered: Moved Mass Dynamics from isolated analysis to continuous experimental context

Q2 2024 (Apr–Jun 2024) -Stabilising analytical foundations

Theme: Trustworthy dose–response and dataset handling at scale

Capabilities delivered

• Robust dose–response analysis and visualisation

• Dataset-level tables as first-class analytical objects

• Performance and UX improvements for daily use

Why it mattered: Set a reliable analytical baseline so teams could scale usage without second-guessing outputs