Enterprise
Healthcare and life sciences
In this sector the hosting question comes before every other, and the right answer is often “on premises”. That doesn’t make the project impossible: it changes the choice of models and the sizing of the machine.
The sector’s needs
- AI and analytics served entirely inside the perimeter, on models and machines sized for data that can’t leave
- Analysis chains that regenerate every published result, versioned, reproducible and owned by the institution
- A storage and archiving architecture that absorbs the instrument volumes with integrity, tiering and a governed cost
- Data-management plans delivered as running infrastructure: repositories, access and retention live with the project
- Document pipelines that structure reports, forms and notes at production scale, with human review where it matters
Delivered across Switzerland, remote-first, with on-site days by arrangement.
Scope boundary
Swiss hosting, secure mail or a tested restore don’t by themselves demonstrate health-data compliance. Roles, purposes, access, contracts, transfers and professional duties still have to be validated in the framework that applies.
Examples of what gets built
Assistant on confidential documents
Open models served from your own machines, with directory permissions applied to every question.
Secure AI assistants →Research data pipelines
Reproducible, versioned and documented processing, owned by the institution rather than by one person’s environment.
The data foundation →Document extraction and classification
Structuring of reports, forms and notes, with human review of anything that falls outside the expected shape.
Document automation →Scientific monitoring
Tracking the literature of a field with a summary and a link to the source, as the laboratory demonstration already does.
Document search →Governance and traceability
Who has access to what, what was processed, what is kept and for how long, written down and kept current.
The operations file →Chaining administrative tasks
File preparation, reminders and transmissions chained together, with human approval before anything concerning a patient goes out.
Business agents →Instrument workstations past end of support
The machine driving an instrument follows the version its manufacturer dictates. It’s inventoried, isolated and moved before the date rather than after the incident.
Leaving end of support →Chosen hosting for each dataset
Each option gets compared across purpose, roles, contract, locations, access, subprocessors, transfers, retention and exit. Permissions are reconciled during the migration.
Migration to hosting you chose →On-premise compute for data that can’t travel
Where the constraints justify it, compute, storage and access remain on site with outbound flows, support, backup, administration and telemetry explicitly controlled.
Private servers and private cloud →Continuity of research computing
Access, repositories and exports readable without the original tool, for a platform that outlives every individual environment.
Reversibility pack →Records restored without exposing the data
The isolated copy of records and research data uses credentials separate from production, and I restore in front of you, in an isolated environment, with the duration measured.
Backup and recovery →Role-based access to the record and the laboratory
Clinicians, researchers and administration receive their rights through groups that describe their role, with the review professional secrecy requires and a verified closure at every departure.
Identity, directory and access →Mail whose operator and contract are documented
The clinic's or laboratory's tenant carries separated admin roles, controlled external sharing and a written answer on the operator, the processing locations and the subprocessors, the one a patient or a partner may ask for.
Mail and collaboration tenant →A model served on your premises for the documents that never leave them
The open model runs on a machine of the clinic or the laboratory, sized to your volumes, with latency, cost and quality measured on your own cases before the assistant reads a record.
Private AI infrastructure →
Examples of solutions
The data foundation
Your sources reconciled in a governed model, with definitions, owners and controls.
Secure AI assistants
An assistant evaluated on your documents, with sources, permissions, processing and retention written down.
Document search
Finding the right page in the whole archive, in one question.
Document automation
Reading, sorting and extracting whatever arrives by email, PDF or scanner.
Business agents
Tasks chained end to end, with a person at the points that matter.
Leaving end of support
Systems past their support date, moved before the incident rather than after.
Migration to hosting you chose
Server, files or mail moved to an evidence-based choice, with rollback and exit defined.
Private servers and private cloud
Virtualisation, file sharing and remote access on hardware that belongs to you.
The operations file
The written file that lets somebody else take over your systems tomorrow.
Reversibility pack
Everything needed to leave a provider, or to carry on the day I stop.
Identity, directory and access
One account per person, rights carried by groups, technical secrets inventoried and revocations you can verify.
Backup and recovery
An isolated copy, a restore performed and timed, then recovery objectives decided per service and rehearsed.
Mail and collaboration tenant
Microsoft 365, Google Workspace or kSuite designed, migrated and kept in order: admin roles, sharing, a protected mail domain and justified licences.
Private AI infrastructure
An open model served on your machines or with a Swiss host, or a gateway to a cloud provider's APIs, sized and measured on your documents.
Relevant experience
Assignments I delivered, described without naming anyone. No logos and no testimonials: what I did, and with what.
Systems and storage engineering at a global storage-solutions vendor
FC · iSCSI · NAS/SAN
An AI-agent environment for software development (scope review, testing, code memory), built and in daily use
Copilot · Claude Code · MCP
Bioinformatics data pipelines for a genomics research institute
Python · Linux
Why me
- Bioinformatics pipelines delivered for a genomics research institute
- Local AI treated as a concrete sizing question: which machine, which model, what real throughput
- Storage and backup sized for the instrument terabyte
- One limit stated plainly: no health data under BioMedIT without an accredited partnership
Neighbouring sectors
Banking and insurance
Document processing, internal assistants and market data, under traceability constraints.
Energy and utilities
Forecasting, integration with the operating systems, and deviations caught in the measurements.
Manufacturing
Shop-floor data in one place, maintenance triggered by condition, documentation you can query.
Test the fit: Healthcare and life sciences
Describe the context, constraints and decision you need to make. The first conversation qualifies scope, boundaries and the next useful step.
Describe the situation