IAFP 2026 Annual Meeting
Analytical Quality Assurance — Rethought. Come see our latest work on method validation, measurement uncertainty, and proficiency testing for food & feed labs.
Featured Poster
01 — What we're presenting
Connecting a validated statistics engine to a chatbot so the AI guides interpretation — but never invents the numbers.
Download this poster (PDF)02 — Why work with QuoData
A single, coherent statistical framework connecting method development, validation, proficiency testing, verification, and QC — instead of siloed tools and logic at every stage.
Method development, validation, proficiency testing, verification, and QC charting are usually run as separate activities, each with its own tools and logic. We connect them into a single, coherent framework, so evidence flows between stages instead of stopping at each silo.
Classical schemes treat proficiency testing (PT) as a laboratory-assessment tool and nothing else. With the right statistical design, the same PT data can also drive method validation, method comparison, verification, and ongoing QC monitoring, turning routine rounds into a continuous stream of evidence on method performance across your network.
When a formally validated method underperforms across many labs, the problem is rarely any single team. Well-designed PT is often the first mechanism to expose these systemic gaps, pointing to how the stages of the method life cycle are designed and linked rather than to individual performance.
A dedicated training program built on this integrated, continuous QA methodology gives laboratories and program leads a rigorous, unified framework. It tackles method-development and validation challenges at their root, connecting development, validation, PT, verification, and QC into one coherent whole.
High-dimensional data from sequencing, PCR, mass spectrometry, and spectroscopy demands statistics built for the data, not borrowed from simpler assays. We develop and validate analysis pipelines for molecular, genomic, and spectral measurements, so complex signals translate into defensible, reproducible results.
When you're screening for the unknown rather than a fixed list of analytes, classical validation logic no longer fits. We design the statistical and validation frameworks that make non-targeted approaches rigorous, from performance characterization to setting decision criteria you can stand behind.
Machine learning is only useful in regulated work if it's transparent, validated, and reproducible. We build and evaluate AI methods to the same evidentiary standard as any analytical method, so models can support scientific and regulatory decisions with documented, auditable performance.
Methodology only helps if it works day to day. We build validated, AI-agentic statistical tools that don't just compute — they run the workflow: evaluating PT rounds, flagging validation gaps, comparing methods, and monitoring QC charts, then surfacing what needs your attention. Your teams stay in control and every result stays transparent, reproducible, and audit-ready.