AI development, end to end
From raw data to a deployed, usable tool: we design, train and ship the model itself, with validation and explainability engineered in from day one.
Complete AI, Built to Be Trusted
ExplainableAI Engineering.
Credio designs, builds and proves the AI behind high-stakes decisions. We deliver AI model pipelines with explainability, robustness, validation and regulatory evidence engineered in.
Built on a decade of peer-reviewed AI research.
Explainable AI
Robustness & adversarial testing
Independent validation
Regulatory evidence
Built end-to-end
Credio AI AssuranceBuilt · Explained · Validated
The methods and standards we hold the work to
Explainable AI
LIME
SHAP
Adversarial robustness
Generalisability testing
EU AI Act
MDR / IVDR
NIST AI RMF
PROBAST+AI
TRIPOD+AI
CLAIM
FDA GMLP
Confidence scoring
HPC at scale
01 What Credio does
Complete AI, engineered to be trusted.
We pair the best algorithms with the right tools and process to turn your data into decisions you can defend. Every capability below is delivered with explainability and validation built in.
Data mining & analysis
We uncover the non-obvious patterns in complex, high-dimensional data — going beyond classical analysis to the relationships that actually drive outcomes. We seek evidence and cause-and-effect, not just correlation.
Decision support
AI that helps an expert make the call — a diagnosis, a prognosis, a plan. Predicting what comes next, not just describing what happened.
Explainable AI
Established explainable-AI methods, tailored to the models you or we have built — interpretable design, then engineering and visualization, so users understand why the AI reached a decision, in comprehensible terms.
Validation & robustness
Independent, adversarial stress-testing and generalisation checks that tell genuine performance from a flattering result that won't survive the real world.
Regulatory evidence
The technical dossier and roadmap that get a model ready for MDR / IVDR and EU AI Act review — evidence, not surprises.
02 How we work
A second, expert eye — with the depth
A second, expert eye — with the depth
to use it, and to build.
Anyone can train a model. Far fewer can prove it truly works, explain it to the person who carries the decision, and build the thing in the first place. That combination is the whole of Credio.
01
Working, or just looking like it
We pressure-test a model from every angle and have the judgment to tell genuine clinical performance from a flattering result that won't survive contact with the real world — a new hospital, a different scanner, a patient population the training data barely saw.
02
Explainable to whoever is accountable
We are experts in explainable AI. We make a model clear to the clinician or engineer who owns the outcome — a tool they understand and can question, not a black box taken on faith. The accountable expert should be able to read why the model said what it said.
03
Human judgment, signed off
Hybrid intelligence: the expert stays responsible and the model becomes something they can genuinely rely on. Our work is human expertise you can cite — never an automated rubber stamp.
The accountable expert stays responsible.
Hybrid intelligence: we make the model something the clinician or engineer who owns the decision can genuinely rely on — and question.
03 Ways to work with Credio
Build the model, explain it, prove it —
Build the model, explain it, prove it —
separately, or end to end.
Bring us in to build the model itself, to make it explainable, to prove it holds up independently, or to get it regulator-ready: data → model → validation → trusted dossier.
DesignTrainDeploy
When you need the model itself, we design, train and deploy it with you, with validation and explainability engineered in from day one. In medicine we focus on the models that touch patient care — diagnosis, imaging, decision support, prognosis and treatment planning — not back-office administration.
- From raw clinical data to a deployed, usable tool
- Diagnostic, imaging, decision-support and prognostic models
- Validation and explainability built in from the first line
- Backed by HPC, with production-ready interfaces clinicians can use
InterpretClarifyTrust
We are experts in explainable AI. We engineer it into your model and make it clear to the clinicians and decision-makers who will actually rely on it.
- Explainability engineered into the model, not bolted on after
- Made comprehensible for clinicians and decision-makers, in their terms
- Trust that survives real-world use and scrutiny
TestStressProve
An independent, expert verdict on whether your model genuinely holds up — and a dossier others can trust.
- Tested on every aspect, end to end
- Adversarial and perturbation stress-tests
- Proven to generalise across new sites, devices and patient populations
- An independent validation dossier for partners, buyers or regulators
Gap analysisDossierRoadmap
Not certification — the evidence and the roadmap that get you ready for formal regulatory review, without surprises.
- Evidence gap analysis against MDR / IVDR and the EU AI Act
- A regulator-ready technical dossier
- A clear, prioritised roadmap to compliance
Not sure which fits?
Tell us about your AI project and where it stands. We'll point you to the right starting line — whether that's a plan to build a model or a verdict on one you already have.
04 The gap we close
~50%
of FDA-cleared medical-AI devices, in one review of more than 500, shipped with no published clinical-validation data.
Most AI is documented. Far less of it is genuinely validated. That gap — between a model that looks ready and one that is — is where errors hide, where trust breaks down, and where regulators now look first.
Whether we build your model or pressure-test one you already have, closing that gap is the point.
05 Inside the work
How a model earns its trust —
How a model earns its trust —
and how we build one to deserve it.
Two views of the same discipline: the path from a thinly-tested system to one that is thoroughly validated and safe to deploy, and the path from an opaque model to one the accountable expert can actually read. Whether we are validating your model or building ours, both journeys are the standard we hold the work to.
From minimally validated to thoroughly validated
A model can pass a quick internal test and still be quietly fragile. The difference between a system that looks ready and one that is shows up only when you stress it the way the real world will.
Minimally validated
Limited data sources with little diversity, checked by basic testing of limited scope. Out in the field, this is where the failures appear.
- Poor generalisation to new settings
- Robustness failures under noisy or shifted inputs
- Safety risks and no clear line of accountability
The outcome is performance no one can stand behind.
Thoroughly validated
A comprehensive validation pipeline that earns reliability rather than assuming it.
- Diverse, ethically sourced data reflecting real populations and conditions
- Generalisability testing across new sites, devices and patient groups
- Robustness and adversarial stress-testing against noise and edge cases
- An accountability and governance audit, so responsibility is clear
The result is verified performance you can deploy with confidence.
From opaque black box to explainable decision
A model that is accurate but unreadable still asks the clinician to trust it blindly. In medicine that is rarely good enough — the person who carries the decision needs to see the reasoning, not just the result.
A result without a rationale
High-volume input data goes into an opaque deep-learning model and a prediction comes out — but the complexity hides the decision process.
- An outcome that can't be verified
- A number the clinician can't interrogate
- Trust asked for, not earned
The clinician is left holding a number they can't question.
A verified, accountable outcome
Transparency is built into the system so its reasoning is visible.
- Interpretable model design, with methods such as LIME and SHAP
- Predictions paired with confidence scores, not bare outputs
- An explanation panel that shows what drove the decision
- Actionable insight the accountable expert can act on
A prediction a clinician can question, confirm and own.
Explainable. Robust. Validated.
Explainable AI
Robustness & adversarial testing
Independent validation
Regulatory evidence
Built end-to-end
Designed to the frameworks
06 Standards & compliance
We help you meet the standards your AI will be judged against.
Medical AI is now assessed against a fast-growing body of regulation and reporting standards — general-purpose AI governance and medicine-specific guidance alike. We know these frameworks, design to them from the start, and produce evidence that maps to them — turning a wall of acronyms into a clear, prioritised path to evidence.
General AI governance
Cross-sector frameworks
Medical AI & clinical evidence
Medicine-specific regulation & reporting
EU MDR — Regulation 2017/745Medical Device Regulation
EU IVDR — Regulation 2017/746In-Vitro Diagnostic Regulation
PROBAST+AI (2025)Risk-of-bias & applicability for AI prediction models
TRIPOD+AIReporting standard for clinical prediction models
CLAIMChecklist for Artificial Intelligence in Medical Imaging
STARD-AIReporting for AI diagnostic-accuracy studies
DECIDE-AIEarly clinical evaluation of AI decision-support systems
SPIRIT-AIClinical-trial protocol standard for AI interventions
FDA Good Machine Learning PracticeGMLP & transparency guiding principles
WHO guidanceEthics & governance of AI for health
We design to these frameworks from the start and produce evidence that maps to them — so compliance is a by-product of doing the work well, not a scramble at the end.
07 Research & impact
From knowledge-building research
From knowledge-building research
to real-world AI.
Credio is built on more than a decade of applied AI: funded research projects, an active peer-reviewed publication record, high-performance computing on tap, and diagnostic models validated across multiple hospitals. That depth is why an independent verdict from Credio carries weight — and why the models we build start from research-grade foundations rather than guesswork.
7+
Years in applied AI and machine learning
15
Funded research projects delivered
50+
Peer-reviewed publications
Multi-
hospital
hospital
Diagnostic models validated across multiple sites
HPC on tap
Research output by year
A decade, compounding
2015201820212024
Selected research
A sample of the peer-reviewed
A sample of the peer-reviewed
work behind our methods.
Part of The Lancet · 2025
Utility of disease probability scores to guide decision-making during screening for phaeochromocytoma and paraganglioma: a machine-learning modelling cross-sectional study
eClinicalMedicine
Read the paper
Imaging · 2025
Clinician-Centered Evaluation Framework for Explainable AI Heatmaps in OCT-Based Retinal Disease Classification
Journal of Imaging
Read the paper
Nuclear medicine · 2022
Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review
Eur. Journal of Nuclear Medicine & Molecular Imaging
Read the paper
08 Why teams choose Credio
Proven researchers and engineers —
Proven researchers and engineers —
from award-winning research to deployment.
A team with deep track records in AI and machine learning, spanning award-winning academic research and real-world deployment.
Research-driven
Every solution and every verdict is grounded in peer-reviewed methods, not vendor claims.
Explainable by design
Transparency is built into the model from the start, aligned with EU AI Act expectations — not added as an afterthought.
Domain depth where it counts
Deep experience in clinical AI — imaging, diagnosis, decision support — and the rigour to carry the same standard into any high-stakes domain.
Build and prove
The rare team that can develop the model and independently stand behind it — the whole distance, under one roof.
Partners on high-stakes AI.
From the first model to the evidence that has to stand up to scrutiny — we work alongside the teams who carry the decision.
09 Who we help
Partners on high-stakes AI — from the
Partners on high-stakes AI — from the
first model to the evidence that must hold up.
We work alongside the teams who carry the decision, as the independent partner on their AI across its whole life: building it, proving it, explaining it, and standing behind it.
Healthtech & SaMD companies
Validate and de-risk your medical-AI product before it reaches the market — or partner with us to build it, with assurance engineered in from the start.
Hospitals & health authorities
Validate decision-support systems in your own setting, keep them trustworthy after go-live, and develop new clinical models tailored to your patients and workflows.
Research groups & innovators
Bring Credio onto your AI project as the independent partner whose results hold up to scrutiny — and whose engineering can turn a promising idea into a working model.
Notified bodies
Specialist, on-demand validation capacity for overstretched conformity-assessment teams.
Investors & funders
Independent technical due diligence on a model or venture before you commit.
Beyond healthcare, next
The same rigour travels to any domain where a wrong model is costly — finance, the public sector and beyond.
From research to requirement
10 Why now
The rules are arriving.
AI is moving from optional guidance to hard requirement. In Europe, the EU AI Act sets binding obligations for high-risk systems, and in medicine those stack on top of the medical-device rules (MDR / IVDR): models must be reliable, representative, explainable and stable, with evidence that maps to standards like PROBAST+AI, TRIPOD+AI, CLAIM and the FDA's Good Machine Learning Practice. The strictest high-risk obligations land in August 2028, and the technical standards aren't fixed yet — building a model the right way, and proving it, takes time you only have if you start now.
European healthcare-AI market
$6.1B → $32B
2025202720292030
$6.1BEuropean healthcare-AI market in 2025
~39%Annual growth, heading toward $32B by 2030
Aug 2028
High-risk medical-AI obligations under the EU AI Act begin to land — and the technical standards aren't fixed yet.
Beyond
Trustworthy, explainable AI is becoming a baseline expectation across regulated and high-stakes domains — not medicine alone.
Contact
Have an AI project to build — or one that needs a second, expert eye?
Tell us about your AI project and where it stands. You'll get a straight, expert answer on whether — and how — Credio can help: building what you need, or proving what you already have.
Start a project
BuiltExplainedValidated