About Veriomics
Built by a microbiologist who codes
The problem with modern research isn’t the data, it’s the methodology behind it.
Veriomics turns published literature into defensible methodology, so every research team can ground their work in evidence, not guesswork.
Our mission
Modern biology generates more data than any single team can process. Microbiome sequencing, multi-omics integration, immunoinformatics, predictive modelling, the science is faster than the tooling that supports it. Small and mid-sized research teams are particularly underserved, because the specialist bioinformatics infrastructure available to large institutions remains out of reach for most labs.
Veriomics is built to close that gap. We integrate fragmented omics data, automate the routine analytical work, and ground every output in the published literature. The goal is simple: give every research team the analytical capability of a well-funded bioinformatics group, while keeping methodology, interpretation, and judgement firmly with the scientist.
The problem
Research tools are disconnected. Literature search, experimental design, analysis, and reporting live in separate systems, so evidence is lost between the steps and reproducibility is reconstructed after the fact, if at all.
The approach
A sequential closed loop where each capability’s output feeds the next. Every claim is grounded in citation evidence, and the decision-support boundary is built into the system itself, not added as a disclaimer.
The posture
Global and equal-footing across regions. Honest about limits, transparent in billing, and restrained in voice. We would rather under-claim and be checked than over-claim and be caught.
Founder
Bilal Shafiq
I trained as a microbiologist before I trained as a data scientist. My published work is in bifidobacterial biofilms; my day-to-day has been computational biology, antimicrobial resistance, and surveillance. Both halves of that, the bench and the analysis, taught me the same lesson, from opposite directions: the hard part of research is not generating an answer. It is being able to defend where the answer came from.
Most tools optimise for the answer. They produce a plot, a prediction, a recommendation, and leave the provenance for you to reconstruct later. I wanted the opposite: a system where the evidence is attached to the claim at every step, where “no literature evidence available” is a valid and visible output, and where the model is never permitted to act on your behalf. The artificial intelligence suggests. The human decides. That line is not marketing; in Veriomics it is enforced in the type system.
So I built it. Veriomics is deliberate in its architecture, honest in its terminology, and grounded in evidence at every step, the platform I wished existed when I was running experiments myself.
Microbiology trained
Bench experience · Microbial systems · Laboratory data
Peer-reviewed publications
Ageing biology · Immunoinformatics · Disease mechanism modelling
Production experience
Python · Machine Learning · Analytical pipelines
Built for compliance
Quality systems thinking from day one, for regulated research environments
Team
Founder-led today. Growing carefully.
Veriomics is currently founder-led, by design.
The early phase prioritises product depth and validated pilots over premature scaling, we’ll hire selectively as pilots progress.
Future role
ML Engineer
Building and scaling the ML systems behind workflow planning, methodology consensus extraction, and output verification.
Future role
Bioinformatics Specialist
Ensuring scientific robustness, multi-omics integration, and academic partnerships.
Future role
Research Partnerships Lead
Building academic and biotech partnerships, validating workflows in real research environments.