What we do
We help teams in health and life sciences decide what is worth building, design the behavioural and decision logic that makes it work, and generate the evidence for the next investment decision. Not a generic strategy firm and not a software supplier: behavioural science, clinical practice and AI delivery in one place, with deep specialism in obesity and weight management.
01
Before you commit budget, we test whether the thing you are planning would change anything. This is the Before You Build Method, delivered as a fixed fee Evidence Phase.
02
The behavioural layer for GLP-1 and obesity care. We help teams improve adherence, retention, and long-term outcomes with personalised, evidence-based support.
03
End-to-end design and delivery of patient-facing AI agents, grounded in behaviour change science, workflow fit, and clinical and behavioural safety.
04
Test before you deploy, and evaluate after launch, with audit-ready evidence for governance, oversight, and regulatory readiness.
05
Peer-reviewed research that generates credible evidence of effectiveness, safety, and impact.
Behavioural phenotyping
Two people on the same medication can need completely different support. One sets impossibly high standards and gives up after a single slip. Another eats to manage stress. Another is pulled off course by the people around them, or by a schedule with no room for self-care.
We bring deep experience in behavioural phenotyping: identifying the psychological and behavioural drivers behind each person's eating, activity, and adherence, and turning them into tailored, evidence-based support. That draws on cognitive behavioural techniques, motivational interviewing, psychoeducation, and habit science, delivered in the tone, framing, and intensity that fit the individual.
Cognitive restructuring and self-compassion, so one slip does not end the whole effort.
Trigger and mood work, and mindful-eating prompts that separate physical hunger from emotional hunger.
Boundary-setting, assertiveness, and role-play for the real social situations that derail people.
Graded goals and immediate, non-food rewards that make steady progress feel worthwhile.
Honest expectation-setting and psychoeducation on realistic timelines, so disappointment does not drive early drop-off.
Quick-win actions, time-boxing, and stress management for busy and disrupted weeks.
Curated psychoeducation, clear next steps, and affirmation that builds belief in change.
Motivational interviewing and credible, specific evidence that meets doubt with respect.
Non-scale victories and graded exposure that lower the stakes of trying.
How we engage
Most teams do not need everything at once. These are the three most common ways teams begin working with us, and most start with the first.
Test the assumptions your business case rests on, before you commit product and engineering time. The output is a decision, not a strategy document. This is Before You Build, applied through an Evidence Phase.
See how it worksHands-on help shaping a live AI agent: conversation design, behavioural logic, workflow fit, and safer delivery.
Testing, safety, and evidence for teams close to launch, under governance pressure, or already live.
How we build
RAPID is our development lifecycle for patient-facing AI: Research-Assisted Prototyping and Iterative Development. Each idea moves through structured, auditable stages, with behavioural science, clinical thinking, and governance built in from the start rather than bolted on at the end.
We select the right subject-matter experts from our team and network, review the science and the market, and turn an ill-defined business problem into a well-defined AI problem, with a model design and success metrics.
Our engineers build the solution: choosing a foundation or custom model, engineering prompts, fine-tuning where useful, and implementing guardrails to meet your AI safety standards.
We evaluate the model with our own tools, including behavioural-science persona simulators, iterating against domain-specific metrics and safety assessments.
A software team builds an MVP web or mobile app with a simple interface, so real users can engage with the model, with cloud architecture scoped for deployment.
We put the prototype in front of small user cohorts, gather and analyse qualitative feedback, and feed it into the next iteration, with ongoing support available.
Safety and governance
A patient tells your AI they have been skipping meals and the weight is coming off faster. The AI replies: that is great, well done for staying focused. Nothing in that answer is factually wrong. But for someone with a history of disordered eating, it has just encouraged the thing your programme exists to prevent.
Right answer, wrong action. Accuracy testing will not catch this, because there is nothing inaccurate to catch.
Behavioural
Usually missing
What is the conversation doing to the person?
Clinical
Is the information accurate and in scope?
Technical
Does the system work and stay reliable?
No AI is risk-free. What we can do is design for that risk, evidence it, and keep qualified people in the loop.
AI assists with drafting, analysis, and flagging. Qualified people review and approve, and remain responsible for clinical and operational decisions.
Systems draw on approved content rather than generating free-form clinical advice, with clear escalation to a clinician when needed.
Interactions are logged so teams can review inputs, outputs, and escalation, and evidence their oversight.
We design to align with MHRA and CQC expectations, MHRA's Good Machine Learning Practice, and ISO/IEC 42001, and help teams engage regulators early where a system may be a medical device.
Next step
Book a discovery call and we will quickly work out whether the right next step is evidence, delivery, evaluation, or research.
