The whole stack, from data to production.
We take on the parts of a biotech's ML and data work that need to be reproducible, observable, and fast. Six practices, one team, scoped to whatever the engagement actually needs.
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i.
MLOps & Pipelines
Custom models, trained, evaluated, and deployed on infrastructure you can actually operate. We build reproducible pipelines that run the same way on a laptop and in production, so the model that passed review is the model that ships.
- Reproducible training pipelines
- Model registry & versioning
- CI/CD for models
- Monitoring & drift detection
- GPU & batch scheduling
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ii.
Bio & Cheminformatics
The foundational layer for natural-products-inspired ML: metagenomics, molecular property prediction, sequence and structure analysis, and computational drug discovery. This is where our research roots are, and it shows in how we handle the messy realities of scientific data.
- Metagenomic assembly & binning
- Molecular property prediction
- Sequence & structure analysis
- Cheminformatics workflows
- Data curation & QC
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iii.
Dashboards & Analytics
Interactive tools for exploration, decision-making, and publication, tuned to the shape of scientific data rather than generic business charts. Built so a scientist can answer their own next question without filing a ticket.
- Interactive data apps
- Exploratory analysis tooling
- Publication-quality figures
- Real-time & batch views
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iv.
Bespoke Software
Internal tools, web applications, CLIs, and the connective tissue between them. Development, deployment, and maintenance handled by the same people, so what we build doesn't rot the moment we leave.
- Web applications
- Internal tools & CLIs
- APIs & integrations
- Maintenance & handoff
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v.
AI & LLM Integration
Applications built on large language models where they genuinely help: retrieval over scientific corpora, structured extraction from unstructured records, and agent workflows scoped to real tasks. We are equally willing to tell you when an LLM is the wrong tool.
- Retrieval-augmented generation
- Structured extraction
- Fine-tuning & evaluation
- Agent workflows
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vi.
Infrastructure & DevOps
The layer everything else stands on: cloud infrastructure, containerization, CI/CD, and monitoring for computational workloads that need to run reliably and at scale. Usually the difference between a promising prototype and a system a team can depend on.
- Cloud infrastructure (IaC)
- Containerization
- CI/CD & automation
- Observability & cost control
Three ways to work together.
Most work starts as a scoped project and grows from there. We shape the arrangement around your problem, not a fixed template.
Build a defined thing.
A pipeline, a model, a tool, or a platform with a clear goal and endpoint. We scope it together, build it, and hand it over documented and running.
Work alongside your team.
We join an existing effort for a stretch, adding ML and infrastructure depth where your team is stretched thin, and leaving it stronger than we found it.
Get the architecture right first.
Shorter, higher-leverage work: reviewing an approach, choosing a stack, or de-risking a direction before anyone commits months to it.
Not sure which one fits?
Tell us the problem and we'll tell you honestly how we'd take it on, or whether we're the right team for it at all.