§ Practice Standards · Core Commitments
Five working principles, held on every engagement.
Held from the first line of code to the final manuscript figure. These principles define how PrimeOmicX approaches reproducibility, computational rigor, client ownership, and patient data privacy.
- § 01Principle
One team, whole stack
Eliminating the translation loss between biology and software.
Most groups do the analysis or the engineering. Here it is the same person, so the pipeline and the paper are built together and the models feed back into the analysis.
How This Operates in Practice
In traditional biotech workflows, bench biologists hand off raw data to dry-lab analysts, who in turn hand off code to platform engineers. Biological intent and edge cases are routinely lost in translation.
At PrimeOmicX, the same senior computational biologist authors the distributed Nextflow workflow, writes the statistical tests, benchmarks the deep learning model, and evaluates the biological findings.
The result is that your pipeline and your manuscript are built together, with direct accountability for every figure and every number.
- § 02Principle
Reproducible by default
Workflows that still execute identically in five years.
Every analysis ships as a versioned, containerised, tested workflow. Re-run it in a year and get the same answer.
How This Operates in Practice
Every workflow ships with pinned container images (Docker/Singularity/Apptainer), deterministic conda lockfiles, and nf-core compliance.
Input datasets and reference genomes are checksummed (SHA-256) and verified prior to process execution.
Analyses are wrapped in parameterised, self-documenting notebooks that can be re-run in an isolated environment to reproduce every statistic identically.
- § 03Principle
Private by design
Protecting proprietary IP, patient exomes, and unpublished targets.
On-device models and on-prem retrieval mean patient, sample and IP data stay on machines you control.
How This Operates in Practice
Patient data, clinical variant sets, and proprietary target biology never touch commercial closed APIs or external training corpora.
We specialise in local, on-device language models (Apple Silicon MLX, local llama.cpp, vLLM) and isolated VPC deployments where inference occurs strictly where your data resides.
All engagements are conducted under mutual NDAs, Data Processing Agreements (DPAs), and strict institutional ethics compliance.
- § 04Principle
Measured, not promised
Rigorous benchmarks and evaluation harnesses before any deployment.
Evaluation harnesses before models. Benchmarks before optimisation. Numbers in every report.
How This Operates in Practice
No model or pipeline is shipped based on subjective impressions or cherry-picked examples.
We engineer automated evaluation harnesses measuring exact sensitivity, specificity, concordance with truth sets (e.g. GIAB, synthetic spike-ins), and hardware execution budgets.
Every deliverable includes a written evaluation report containing concrete benchmark numbers and reproducible test commands.
- § 05Principle
You own everything
Zero proprietary lock-in, zero ongoing seat licenses, full IP transfer.
Code, weights, documentation and infrastructure. No lock-in and no black boxes.
How This Operates in Practice
Code, repository commits, fine-tuned model weights, orchestration configs, and documentation live in your repositories from day one.
We provide full handover runbooks and training sessions so your internal team can maintain, inspect, and extend the work without ongoing dependency.
No closed-source runtime dependencies, per-seat SaaS tolls, or recurring platform lock-in.
Explore our technical capabilities: Executive Capability Statement →
Meet the founder: Arun Boddapati →
§ Engagement Standards
Hold us to these principles on your project.
We agree on deliverables, reproducible runbooks, and fixed milestones in writing before any billing begins. Replies within two working days.
Scope an Engagement →