Your model works. Now make it a product you can deliver to customers or scale internally across your organization. We turn prototypes into production-scale models that meet regulatory compliance standards.
Your model performs well in the lab. It answers the right questions. The science is sound. But it lives in a Jupyter notebook, depends on the researcher who built it to run, and has no version control, no monitoring, no validation evidence, and no clear path to becoming something your organization can rely on or deliver to customers.
That is a software, data, and infrastructure problem, and it's one we solve for laboratory and biohealth organizations.
What We Build for You
We turn your proven model into production software built for regulated environments.
AI Model Integration is a custom engagement that takes your existing model and gives it the engineering foundation it needs to become a real product: deployed, supported, maintainable, and built to regulatory expectations from the start.
1
Move the model out of the notebook: Research code gets refactored as proper, versioned software that can be tested, maintained and expanded upon.
2
Deploy it at real scale: We host the model in a secure environment so it can serve users, instruments, and workloads reliably.
3
Validate continuously to account for model drift: Monitor model performance against new data over time, and the training and deployment infrastructure makes iteration seamless rather than disruptive.
4
Give your scientists a way to keep teaching the model: A structured feedback loop lets your domain experts review outputs, correct mistakes, and feed those corrections back in, so the model keeps improving.
5
Document the way regulators expect: Training data, model versions, performance evidence, and change history are captured as you go, so audits, submission, and inspections don't require starting the documentation work from scratch.
Why It Matters to You
We'll deliver what your organization doesn't have today.
Faster time to market
Our structured process moves your existing model into a deployed, supported system. Once that foundation is in place, improvements can be made quickly to adjust performance.
Built for regulated environments
Our extensive experience in biohealth informs our data integrity practices, software update processes, and model performance validation, which streamlines the regulatory audits. Because we build models modularly, there's also no need to revalidate the entire model with regulators when software updates occur.
Developed for wider use
We'll outfit your model deliverables with training pipelines and deployment automation built in. If a key person at your organization moves on, the work can continue without delays. The model becomes an organizational asset instead of one person's responsibility.
A model that improves over time
A production-ready AI tool is not a static deliverable. We build for the reality that models need to be maintained, retrained, and updated, so yours stays accurate and useful as your data and business evolves.
Which scenarios fit your organization?
These four entry points are when organizations typically need an AI expert to help continue the project forward. Often, the needs of the model and your organization are a blend of these situations.
Research Model to Commercial Product
You have customer interest, investor pressure, or both, but the model lives in a notebook and can’t be delivered as something you sell, support, and scale. We build the production system around your existing model so it becomes a commercially-viable product.
Notebook Prototype to Company-Wide Tool
An internal team built a model that works for their own analysis but isn’t running in any workflow your organization depends on. We move it from notebook to deployed, monitored production so it generates value continuously, not just when its creator runs it.
Adding Rigor to An Aging Deployed Model
A model is in production but lacks the engineering practices that make it safe to maintain. There's no version control, no monitoring, no validation evidence. Regulators are asking harder questions, the original developer has moved on, and updates are getting riskier. We add the foundation that lets the model keep evolving.
FDA-Track Model Preparation
A research model is being repositioned as a regulated medical device or a feature of a regulated product. The path requires validation documentation, change control under PCCP guidance, audit trails, and explainability evidence. We add the regulatory-grade foundation needed to enter the submission process credibly.
How We'll Work With You
The implementation is custom to your model and your environment. The process we use is streamlined, so you get faster delivery without cutting corners.
Start
Pre-Work
1–2 Weeks
We assess your model, your data, your environment, and your goals. You get a clear-eyed picture of what it takes to productize your specific model and a scoped plan for getting there.
Productization Pilot
6–12 Weeks
We refactor the model as proper software, establish version control and reproducible training, and build the initial deployment. You'll begin to see a working, maintainable system.
Productization Pilot
Production Build
10–16 Weeks
We productize the full application — monitoring, feedback loops, documentation, and deployment automation — into something your team can deliver, support, and scale.
End
Delivery
Your team can take the model from there or we can continue working together for model refinement and ongoing support.
Add Ons
Regulated-Use Configuration
For models that need regulatory approval, we add the validation documentation, change control processes, audit trails, and explainability evidence needed to enter the submission process credibly. Overlaps with Phase 2.
Operate & Refine
Retainer-based ongoing support for monitoring, retraining, model updates, and capability expansion as your science advances and your needs evolve.
Client Success Story
Challenge
An early-stage instrument analytics company had a working ML model for detecting meaningful signatures in particle data. The science was sound and customers were interested — but the model was built in a university setting as Jupyter Notebooks and Python snippets. It was manually run, with no version control and no defensible way to scale or maintain it. It was a promising research asset, but not a product they could deliver and support.
What we did
We rebuilt the model as proper, versioned software with reproducible training and evaluation built in, then productized the application around it into something the company could deliver to customers.
Outcome
A research asset became a product the company could sell and support. Model improvements that previously depended on one researcher's heroics now run as a repeatable, team-owned process — meaning the science can keep advancing without putting the business at risk every time it does.
Most teams that try discover that "deploying" a research model means rebuilding much of it. There's a significant difference between code that runs and software that can be maintained, monitored, retrained, and audited. The engineering work is substantial, and in regulated environments, getting it wrong creates compounding problems. We've done it enough times to do it faster and with fewer surprises.
That's often the best time to engage. Building the production infrastructure in parallel with ongoing scientific development means you're not rebuilding later, and the feedback loops we put in place actually accelerate the scientific iteration by making it easier to test and validate changes systematically.
Yes — that's what the Regulated-Use Configuration add-on covers. Validation documentation, PCCP-aligned change control, audit trails, and explainability evidence are built into the engagement from the start rather than retroactively applied. Starting that work early is significantly less expensive than retrofitting it later.
Discovery (pre-work) is typically scoped at $10–15K over 1–2 weeks and gives you a clear project plan before any larger commitment. The full build through production typically runs 4–7 months. We size the engagement during Discovery based on your specific model, data, and environment.
