We treat the lab as one system — because it already is one.
Every recommendation we make can be traced back to a defined user requirement, a measurable acceptance criterion, and a business outcome. That discipline is the difference between an automation program that scales and one that stalls at pilot.
Four phases. One thread of accountability.
Diagnose
Four to six weeks of disciplined listening. Workflow tracing, data-flow mapping, stakeholder interviews from bench scientist to board. We deliver a single document: what the system actually does today.
Design
User requirements, design inputs, and acceptance criteria — written in language engineering and regulatory both accept. Architectural options scored on reliability, serviceability, cost, and time-to-impact.
Deploy
Phased build with explicit V-Model gates. Method validation, analytical verification, and clinical validation sequenced against business and regulatory deadlines.
Defend
Operational instrumentation that protects first-pass rate, uptime, and COGS as volume scales. AI-driven predictive intervention supported by data and infrastructure monitoring, quarterly capacity reviews, and leadership cadence.
Six commitments that shape every engagement.
Systems before instruments
Choose architecture before vendors. Always.
Validation is design, not paperwork
Acceptance criteria are written first, not retrofitted.
Reliability is a revenue line
Uptime, first-pass, and serviceability are P&L items, not engineering KPIs.
Vendor neutrality is non-negotiable
We have no commercial relationship with any instrument or software vendor.
Talent is the multiplier
Most automation programs underinvest in the engineering organization that operates them.
Confidentiality is absolute
Engagements, clients, and findings are never disclosed without written consent.