Applied AI
Domain-specific AI and machine learning systems built around your data, scientific constraints, and real-world workflows.
TYPICAL CLIENT
You already have data. You have a problem. You need a trustworthy model.
Usually a Head of Data Science, VP of R&D, or founding CTO who already has valuable data—assay results, imaging, EHR extracts, or other domain datasets—but needs a model that domain experts can actually trust in a real decision, not just a notebook with a promising benchmark score.
Applied AI, broken into four services
Each can be scoped on its own, or combined into a single end-to-end engagement.
Model Development & Validation
Model architecture selection, training, validation, and evaluation against real domain ground truth.
View XAIExplainable AI Systems
Attribution, model cards, bias analysis, and validation evidence that make model decisions easier to inspect and defend.
View OPSMLOps for Regulated Environments
Versioned, access-controlled deployment with audit-ready tracking.
View STRAI Strategy Consulting
Feasibility, use-case prioritization, and implementation planning before significant development investment.
ViewSix concrete deliverables, not a slide deck.
This is exactly what transfers to you at the end of this Applied AI engagement.
Model architecture selection & justification
A written rationale for why a given model type fits your data and constraints, not just a benchmark score.
Training & validation pipeline
Reproducible training and validation code with dataset splits, evaluation criteria, and experiment versions documented.
Domain-validated performance report
Evaluation against real biological or operational ground truth, not just statistical metrics.
Production deployment
Deployment into your environment with the required data interfaces and operational configuration.
Monitoring & drift dashboard
A live dashboard tracking prediction drift and data drift over time.
Source code & model artifacts
Project source code, configuration, training assets, and model artifacts are delivered according to the agreed scope and licensing terms.
What this service includes
Custom model development
Classification, prediction, and generative models trained on your data and evaluated against real biological or operational ground truth—not just a single holdout metric.
Retrieval-augmented systems
LLM-based research assistants and internal search tools built over your own literature, protocols and lab notebooks.
MLOps & monitoring
Drift detection, retraining pipelines and performance dashboards that keep a model trustworthy long after launch.
Explainable & Trustworthy AI
Documentation and interpretability reporting suited to regulated or publication-bound use cases.
What this looks like in a real engagement
Three concrete examples of the kind of work this service covers.
Triage model for a diagnostics platform
A classifier that flags cases for review from lab results, with confidence information and traceable outputs that a clinician can review before taking action.
Internal research assistant over lab notebooks
A retrieval-augmented chat tool scientists query in plain language, grounded in your own protocols and prior results instead of the open internet.
Drift monitoring for a production model
A dashboard that flags when incoming data starts to look different from training data, before model performance quietly degrades.
Why generic AI tooling falls short in the life sciences.
Biological and chemical datasets often behave differently from conventional enterprise datasets—noisy assays, small sample sizes, domain-specific priors, complex experimental designs, and consequences that extend beyond a simple recommendation.
We build models that incorporate domain constraints from the start: known biology, assay limitations, and the actual decision the model needs to support — so performance holds up outside the training set.
How an engagement runs
Four stages, from initial scope to delivery.
Define the decision
We start with the decision the model needs to inform, not the algorithm — this shapes data requirements and success metrics.
Audit & prepare data
Assay design, label quality, missingness, and known confounders are mapped before any model is trained.
Train & validate
Models are benchmarked against domain ground truth, not just statistical holdout performance.
Deploy & monitor
Production monitoring, drift alerts and a retraining cadence are built in from day one.
What we build it with
The model that mattered wasn’t the most accurate one on paper — it was the one our biologists actually trusted enough to act on.OUR APPROACH TO APPLIED AI
Before you reach out
Client data is not reused to train models for other clients. Data access, retention, and model-training permissions are governed by the agreed project and data-processing terms.
We define evaluation criteria before deployment and monitor model and data performance after launch. If performance falls outside the agreed thresholds, we investigate the cause and determine whether recalibration, retraining, data review, or model changes are appropriate.
Yes. Many life-science projects begin with limited, noisy, or imperfect datasets. We assess data quality, sample size, label reliability, missingness, and potential sources of bias before selecting an appropriate modeling strategy. Where the data is not sufficient for a reliable model, we say so before development begins.
Explore related work
Have a model you can’t fully trust yet?
Send us the data and the decision it needs to support — we’ll tell you honestly whether AI is the right tool.