Context

We worked with a clinical practice operating in a highly privacy-sensitive healthcare environment.

The organisation conducted regular one-to-one sessions with patients and was required to maintain detailed clinical notes and reports. These records were essential for continuity of care, compliance, and professional standards, but producing them placed a significant administrative burden on clinicians.

From the outset, patient data sensitivity and regulatory obligations ruled out the use of public cloud AI services. Any system processing session content needed to operate within tightly controlled data boundaries.

The Real Problem

The problem was not a lack of tools or information.

It was cognitive and administrative load.

Clinicians spent a large amount of time after sessions reconstructing conversations, writing notes, and producing structured summaries. This work was mentally demanding, reduced time available for patients, and introduced variability across records.

At the same time, trust was non-negotiable. Recordings and transcripts were deeply sensitive, hallucinations were unacceptable, and clinicians needed to remain fully responsible for final outputs. Any AI system had to support clinical work without undermining professional judgement or patient confidentiality.

Constraints That Shaped the Design

The system was shaped by strict constraints from day one.

All processing needed to occur within environments controlled by the organisation. Data residency mattered, as did auditability and predictable behaviour. The system had to function reliably in real clinical workflows, without requiring clinicians to change how they worked or rely on opaque automation.

Just as importantly, the solution needed to be adaptable. Different operational contexts required different deployment models, without weakening privacy guarantees.

What We Built

We initially designed and deployed a fully on-prem private AI system.

This system recorded sessions locally, generated transcripts on-device, and used a locally hosted language model to produce structured summaries and draft clinical notes. All data remained within the organisation’s own hardware environment, with no external data transfer.

Building on this foundation, we then introduced a hybrid private deployment model.

In this configuration, processing was handled by dedicated private servers hosted in Switzerland, operating under strict data-residency and access controls. This allowed the organisation to balance operational flexibility with regulatory requirements, while maintaining full control over where data was stored and how it was processed.

In both cases, the AI acted as an assistant. Outputs were structured, reviewable, and editable, with clinicians retaining final authority over all records.

Design Considerations

Several principles guided both deployment models.

Privacy was treated as a baseline requirement, not a feature toggle. Whether on-prem or hybrid, data handling remained tightly controlled and transparent.

Reliability and predictability were prioritised over linguistic creativity. Outputs needed to be sober, consistent, and easy to verify against source material.

Workflow fit was equally important. The system was designed to integrate naturally into existing clinical routines, reducing friction rather than adding new cognitive overhead.

Outcome

The system significantly reduced the time clinicians spent on post-session documentation.

More importantly, it reduced mental fatigue. Clinicians were able to focus more fully on patients during sessions, knowing that documentation support was in place afterwards. Records became more consistent, and the effort required to reconstruct complex conversations decreased substantially.

Trust developed gradually as clinicians saw that the system behaved predictably, respected privacy boundaries, and remained firmly under their control across both deployment models.

Why This Matters

This deployment reinforced an important lesson for privacy-critical environments.

Effective private AI systems are not defined by where they run, but by how well they respect control, data boundaries, and professional responsibility. Whether deployed fully on-prem or in a tightly governed private cloud, success depends on restraint, clarity, and trust.

When designed properly, private AI can reduce cognitive load without increasing risk.