Evidence phase
Fixed fee, eight to twelve weeks. At the end you know whether to build it, and you have the evidence to show why.
The problem
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 the people it is built for 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.
An evidence phase moves those questions to the front, where they are cheap.
What it is
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.
It ends with one of three answers. Go ahead, rework it, or stop. Whichever it is, you will be able to explain to the people holding the budget why.
What it costs
Every evidence phase is quoted individually, because what would actually be useful varies far more than the format does. The cost moves with the complexity of what you are proposing, how many workstreams run alongside each other, the specialist skills it calls for, how long it needs to run, and whether it stays on synthetic data or has to work near real patient data and the medical device boundary.
Whatever the number, it is agreed and fixed before anything starts. There is no open-ended meter running.
We quote after a conversation rather than before one. That conversation is most useful when you already know what you need to decide and have a budget to investigate it. If you are not there yet, it is usually worth waiting until you are.
What you get
There is no fixed deliverables list, because the useful work depends on what is least certain. An evidence phase usually produces some combination of the following, weighted towards whichever of them your decision actually turns on.
Software that runs on synthetic data and brings the idea to life. Not a mockup and not a video, but something people can use and react to.
It sits between a prototype and a minimum viable product, and it can be built to anywhere on that line depending on what the decision needs. That makes it useful for gathering market feedback, not only for testing the idea inside your own organisation.
How the inputs you have come together into something a clinician or a health coach could act on. This is usually the part that is hardest to specify, and the part that decides whether the rest of the idea holds up.
Structured conversations with the providers, clinicians and partner organisations whose decision it would ultimately be. Whether they would use it, how it fits what they already do, and what commercial model would carry it.
Where the boundary sits for what you are proposing, and what changes on either side of it. Found now, when it is still cheap to design around.
A position on whether to go ahead, rework it or stop, with the evidence behind it. Written to be read by people who were not in the room.
The regulatory read draws on the same standards work we bring to live systems, including MHRA and CQC expectations, Good Machine Learning Practice and ISO/IEC 42001. More on that in safety and governance.
What we bring
Most health AI work stops at prediction. It can tell you who is likely to struggle. It rarely tells you what to do about it.
The difficult work is deciding what the system should do with what it knows. That means linking clinical data, behavioural data, segmentation and phenotyping into logic a clinician or a health coach could actually act on. Prediction creates insight; behavioural intelligence creates action, and the logic in between is where most of the design work goes.
Some of what that logic does will sit inside the medical device boundary and some of it will sit outside. Knowing which is which, early, changes what you are able to build and how you will have to evidence it. We bring that governance and regulatory experience alongside the design work rather than leaving it to the end.
Then the working model puts it in front of the people whose reaction matters, clinicians, coaches and the people they support, while it is still cheap to change. How it would be validated is part of the same question, and we work across proof of concept, pilot and trial, so an evidence phase is shaped with the next stage of evidence already in view.
Most of this is work an internal team knows it should do and cannot find the time or the specialist cover to do. It is done here by clinicians, behavioural scientists and psychologists, decision architects, machine learning engineers, and behavioural AI and transformation specialists, drawn together for what a particular programme needs rather than working in sequence. You can meet the core team.
This is the ground our published work covers, including the FAST framework for evaluating conversational AI and our work on behavioural safety.
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 recommendation says stop.
The work runs without real patient data. Governance, information security and ethics approvals stop being the thing that delays it.
The work is shaped from the start with a clear view of where the medical device boundary sits, so what you learn stays useful when you move towards a build.
Innovation, digital and R&D teams in health and life sciences who have funding to investigate, an idea worth investigating, and a decision they will have to defend.
If you have already decided to build and you need someone to build it, this is not the right piece of work. If you need a supplier who will agree with the direction that has already been set, it is not that either.
What happens next
You will not be starting from a standing start. The evidence phase leaves you with partner options, a pilot design, and the internal case already made. What happens after that is your decision, and it does not have to involve us.
Next step
A short call to understand what you are trying to decide, and whether an evidence phase is the right way to decide it.
