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Biology x engineering AI infrastructure for early-stage biotech

Novopharos builds the software layer that lets biotech teams use modern biological AI like a product, not a research project.

We are a life sciences consulting agency and platform company embedding AI into startup and wet-lab workflows. We turn foundation models into production-grade tools that measurably accelerate R&D.

Positioning Consulting + platform
Users Early biotech + wet labs
Mode On-demand computational team

Novopharos operator layer

Model-guided biological workflows
live system design
Workflow surface Form-driven model execution
Sequence / task configuration
Target Antibody humanization pipeline
Execution state Scored + validated
4 / 4 pipeline stages complete
Interpretability Scientist-readable outputs
Practical model library Reusable workflows across teams
Sequence configuration
Execution monitoring
Mutation scoring
Workflow composition

Think of Novopharos as your on-demand computational team: product-minded enough to ship interfaces, scientific enough to structure the work around real biological decisions.

What we do

We close the gap between powerful models and usable R&D systems.

AI

Embedded AI engineering

We scope, build, and ship production-grade internal tools for teams that need ML capability without hiring a full computational biology group upfront.

WF

Scientific workflow software

We translate messy bench-to-analysis processes into reliable interfaces, orchestration layers, and decision-support systems your team can actually operate.

MO

Biological model operations

We make foundation models usable in practice: parameter surfaces, run controls, scoring views, output interpretation, and handoff into downstream pipelines.

Platform

The platform is built around the work scientists already need to run.

We are developing a surface where running a biological foundation model is as simple as filling out a form, selecting a workflow, and reading an output that matches how teams make decisions.

That means less notebook glue, fewer custom scripts per project, and a cleaner handoff between bench scientists, operators, and technical leads.

Protein structure prediction and scoring
Variant effect and mutation analysis
DNA and protein generation interfaces
Lab-facing dashboards and workflow composers
Product surfaces

The screenshots are part of the story: this is a real operating layer, not a concept deck.

ESMFold sequence configuration dashboard

Sequence configuration

Form-based setup for biological model runs.

DNA sequence generation execution monitor

DNA generation monitor

Execution state, logs, and output preview in one view.

Protein mutation analysis dashboard

Mutation analysis

Scientist-readable scoring and interpretation surfaces.

Protein structure prediction dashboard

Structure prediction

Prediction outputs framed as an operational dashboard.

Scientific workflow composer dashboard

Workflow composer

Composable pipelines for real biological workflows.

Scientific workflow composer interface

Model library interface

Reusable workflows and components across teams.

Why teams hire us

Use cases that matter when time, headcount, and scientific clarity are tight.

DNA

Foundation models for biology, minus the friction

Give scientists a form-driven path to inference, not notebooks, GPU setup, or brittle command chains.

LAB

Wet-lab aware product design

Interfaces are structured around how experiments are planned, validated, and discussed in real biotech teams.

OPS

Consulting that turns into leverage

Engagements start hands-on and can evolve into internal platforms, reusable tooling, and durable operating systems for R&D.

Engagement model

We start with a real bottleneck and end with infrastructure your team can keep using.

01

Audit the scientific workflow

We map the current bottleneck, data shape, approval path, and failure modes before touching the interface.

02

Ship the first usable surface

We turn the highest-leverage analysis into a tightly scoped tool that scientists can run without manual model wrangling.

03

Expand into a platform layer

Successful workflows become reusable infrastructure across teams, models, and programs instead of one-off scripts.

Contact

If your team is exploring AI in biology, we can help you operationalize it.

Start a conversation