External Research Lab · AI/ML & Computational Modelling

From deep-tech needs to working prototypes

We work with teams across industry and academia as an external research lab building AI/ML pipelines that work with both real-world and synthetic data.

Whether your data comes from real-world measurements, or needs to be generated synthetically via modelling, we can build and validate solutions and hand it over to your team.

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What we deliver

Technical assessment — what's feasible, data you need, what it will cost to build
Working pipelines — code you can run, extend, and build on
Credible outputs your team can use for internal decisions, funding conversations, or regulatory review

Academic-industry background Knowledge transfer NDA-friendly

Services

Three structured engagement plans

Each engagement has defined deliverables, timelines, and success criteria agreed before we start.

Plan 01

Workflow Mapping

2 – 4 weeks

We map your system, audit your data, and produce a concrete build roadmap — so you know exactly what's feasible and what to build first.

Workflow diagram: data → models → decision points
Data audit + risk register
Build roadmap with milestones & compute estimates
Validation plan

Plan 02

Module Build

6 – 10 weeks

Build a working, validated computational or AI/ML component — ready to integrate into your pipeline and hand over to your team.

Physics-informed surrogate models
Data-driven modelling pipelines
Dimensionality reduction & feature extraction
Generative models for synthetic data
Reproducible, documented, tested code

Plan 03

Validation & QA

4 – 8 weeks

Typically follows a Module Build engagement. Essential where models inform high-stakes decisions or regulatory submission workflows.

Verification tests & baselines
Uncertainty quantification
Sensitivity analysis & error budgeting
Monitoring/QA metrics for deployment
Validation report (technical + non-technical)

Capabilities

From data to deployment

Domain knowledge combined with production-grade delivery.

Scientific AI/ML

Classical and deep learning for prediction, classification, and representation learning
Physics-informed ML — embedding physical constraints to reduce data requirements
Probabilistic modelling, uncertainty quantification, and calibration
Generative models for synthetic data generation and design space exploration

Computational Modelling

Multiphysics and PDE-based simulation (electromagnetic, thermal, fluid, diffusion)
Molecular dynamics and coarse-grained modelling
Surrogate and reduced-order models to replace or accelerate expensive solvers

Inverse Problems

Parameter estimation and system identification from sparse or noisy measurements
Nonlinear systems and hysteresis modelling

Data & Delivery

Reproducibility-first workflows — versioned data, experiment tracking, audit trails
Handover: documentation, training, and guidance on next steps

Case Studies

Examples of our work

Client details remain confidential under NDA. Further examples are available on request.

Materials · Inverse Modelling

Surrogate modelling for materials

Developed surrogate model tools to extract material parameters from measurements for industrial applications in magnetic nanotechnologies. Delivered a validated, reproducible system integrated with measurements, enabling rapid, reliable characterisation at scale.

Under NDA

Biomedical · Drug Discovery

AI pipeline prototyping for drug discovery

Built working AI pipeline prototypes combining molecular dynamics simulations with deep learning for a drug discovery startup. Provided technical training and strategic input on data requirements, modelling approach, and development roadmap.

Under NDA

About

How we typically work

Structured engagements with a transparent process — and knowledge that stays with you.

Scoping call

30–45 minutes. Problem framing, success criteria, and a frank assessment of feasibility.

Engagement proposal

Deliverables, milestones, assumptions, and risks — all documented before work begins.

Weekly updates

Demos, results, and decisions at regular intervals. No black boxes.

Handover

Code, documentation, training, and a clear path forward for your team.

Tensorlytix is an independent research lab founded by a researcher with a career spanning academic research and applied commercial consulting.

Our work sits at the intersection of computational physics and statistics, scientific machine learning, and applied modelling across materials, biomedical, and molecular systems, combining domain knowledge with production-grade delivery.

When a project needs additional capacity, we draw on a trusted network of software engineers and scientists. We're comfortable working alongside your internal team, external partners, or academic collaborators, and we make sure knowledge stays with you when the engagement ends.

Good first email

A short description of the problem
What data is available
Your timeline
What "success" looks like