AI in healthcare
that survives an audit
MLR pre-check, tumour board preparation and document analysis for pharma and clinical care. Built so the evidence trail stays intact and the decision stays with a person.
References in this industry
AI Architecture Advisory in a Biopharma Environment
prioritisedUse Cases Assessed
IT Discovery and Communication Platform for a Hospital Group
14,000Employees
Member Platform and Document Pipeline for a Statutory Health Insurer
eliminatedMedia Break
AI-Powered HealthTech Analysis Platform
AutomatedData Analysis
Frontend for AI Research Platform
2 monthsDuration
B2B Health Commerce Platform
12 monthsDuration
Marketing and Sales Automation for a Wellbeing Provider
automatedScheduling
What we build for this industry
Regulatory pre-check
Assets are screened against claims, sources and visual implications before formal review, with a passage reference and a severity on every finding.
- Claims & reference matching
- Visual material review
- Findings with passage links
- Rule mapping to HWG & FSA
Clinical decision support
Structured and unstructured case data runs through an analysis pipeline into a case summary clinicians can verify and override.
- Clinical data ingestion
- Automated analysis
- Generated case summaries
- Every insight traceable
Document analysis & literature search
Questions in natural language against studies, guidelines and product information, answered with a citation rather than from model memory.
- OCR & document pipeline
- Answers with citations
- No answer without evidence
- Connects to existing filing
Article 9 architecture
Hosting, access design and logging are settled before the first line of code, not in a final acceptance review.
- EU or tenant-internal hosting
- Processing agreements settled
- Complete audit logging
- Medical device boundary
What actually gets in the way
The problems we hear in this field, before anyone mentions a technology.
Feasible is not permissible
A model can classify reliably and still be unusable. The question is who is accountable for the output.
The review is the bottleneck
Every claim and its source annex is checked by hand. Assets come back for a second and third cycle.
Hours before every board
Imaging, lab values and treatment histories are pulled together before a case can even be discussed.
No evidence trail, no use
A summary nobody can check against the record is not decision support.
Article 9 shapes the architecture
Health data is a special category. That fixes hosting, access and logging before anything is built.
The medical device boundary
Where a system influences treatment, the regime shifts. You settle that early or not at all.
What we have built here
AI Architecture Advisory in a Biopharma Environment
Assessing and designing AI-supported processes under regulatory constraints - with an upstream check layer ahead of every approval.
IT Discovery and Communication Platform for a Hospital Group
An assessment of the IT landscape of a hospital group with 14,000 employees, the action areas derived from it, and an AI-assisted platform for communicating project progress.
Member Platform and Document Pipeline for a Statutory Health Insurer
Digital application and advisory journey for a large statutory health insurer - including normalised XML delivery into the insurer core systems.
AI-Powered HealthTech Analysis Platform
Medical data analysis platform with AI-driven tumor board decision support and automated clinical workflows.
Frontend for AI Research Platform
Frontend development for an AI-powered analysis platform supporting cancer research at a HealthTech partner.
B2B Health Commerce Platform
Continuous development and performance optimization of a Shopware-based B2B2B platform in the health sector.
Marketing and Sales Automation for a Wellbeing Provider
Digital customer acquisition for a wellbeing provider: automated booking journey, lead qualification and local SEO instead of manual scheduling.
Engineering Across Borders
Camsol was founded with a clear conviction: world-class engineering doesn't need to come from a single zip code. By bridging Germany's engineering precision with Cameroon's emerging tech talent, we've built a model that delivers exceptional results - while creating real opportunity.
Our teams aren't outsourced contractors. They're integrated engineering partners who work alongside our clients daily, using AI-augmented workflows to deliver faster and better.
Every engineer on our team uses AI daily - not to replace expertise, but to amplify it. The result: faster delivery, higher quality, and solutions that scale.
Frequently Asked Questions
No, and the systems are deliberately built so it cannot. Every generated insight stays traceable to its source, and the interface is built for clinicians to verify, override or annotate it. The system does the groundwork; the decision stays with the people who carry the clinical responsibility.
Where it is allowed to sit, and we settle that before choosing a model. Health data falls under the special categories of personal data in Article 9 GDPR, so EU or tenant-internal hosting is a precondition rather than an option. For the MLR pre-check the deployment was scoped for the client own Azure tenant from the outset, because health and commercial data does not leave it.
As soon as it is intended to influence a diagnosis or treatment decision. We establish that boundary early in the design rather than working around it, because it determines how much evidence a system has to carry. In practice it often leads us to deliberately limit the system role to groundwork and leave the decisive assessment with a person.
Only if they are evidenced, and that is how we build them. Every finding points at the flagged passage in the document, names the severity and the rule it touches, such as the German HWG or the FSA code. A tool that returns a verdict without a passage reference merely moves the work rather than easing it.
A pre-check or a document analysis typically reaches a productively usable state in four to six weeks. We deliberately start with a slice, such as one asset type or one indication, and extend from there. That has a second benefit: a working state serves as the basis for funding approval, as with the MLR pre-check, which doubled as the pitch to country leadership.
No, and we say so openly. The focus is technology, information security and IT governance, alongside experience with ISO 27001, NIS2 and financial-sector regimes such as DORA and KAIT. The medical and regulatory expertise comes from your side. Our job is to translate it into systems so the evidence question does not surface at the end.
Ready to Build Something
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