Concept phase
A fixed fee concept phase that produces the evidence for a build, partner or stop decision.
The problem with big AI decisions
Most health AI investment decisions are made on the strength of a business case, and the business case is written before anyone has seen the thing work. The assumptions inside it stay untested until the build is underway and the budget is committed.
By then, the questions that mattered most are the expensive ones to answer. Will clinicians and patients actually use it. Does it fit the workflow it has to live inside. Is there a commercial model that anyone would sign. Does it stay clear of the regulatory boundary you thought it did.
A concept phase moves those questions to the front, where they are cheap.
What a concept phase is
It is a self-contained piece of work with a defined end point. You are not buying a system, and you are not starting something that becomes difficult to stop. You are buying the evidence and the recommendation you need to make the next decision properly.
At the end you will be in a position to proceed, refine, redirect or stop, and to explain to your board which one you chose and why.
What it produces
01
The design of the decision layer that sits between your data and your patient. Prediction creates insight; behavioural intelligence creates action. This is the work of specifying what the system should do with what it knows, grounded in established behaviour change models and techniques rather than invented from scratch.
02
Working software, built on synthetic data, that your stakeholders can use and challenge. Not a mockup and not a video. Something people can sit in front of and try to break.
Assumptions should be tested in software, not PowerPoint. A demonstrator turns an abstract internal argument into a concrete one, and it does it before anyone has committed to a build.
03
Hypothesis-led co-design with provider and ecosystem organisations, typically eight to twelve of them. The questions are whether it would be adopted, how it fits real workflows, and what commercial model would carry it.
04
Partner profiling, longlists and shortlists, engagement cases and pilot design. If the answer is to proceed, you should not be starting the route to market from a standing start.
05
A clear position on whether to proceed, refine, redirect or stop, with the evidence behind it. Written to be read by the people who hold the budget.
Why this works
Sacher AI has no platform of its own to sell you. That makes the conversations honest and it makes a stop recommendation possible. If the evidence says stop, the report says stop.
The whole concept phase runs without real patient data. Governance, information security and ethics approvals stop being the thing that delays the work.
Concept work is shaped from the start with a clear view of where the medical device boundary sits, so that what you learn stays useful when you move towards a build.
Innovation, digital and R&D teams in health and pharmaceutical organisations who have gate funding, an idea worth investigating, and a decision to defend.
Next step
A short call to understand what you are trying to decide, and whether a concept phase is the right way to decide it.
