Everything you need to design, build, and deploy AI in digital health.
From strategic AI discovery and governance through to hands-on agent development, behavioural design, and academic-grade research — we support the full lifecycle of patient-facing AI in digital health.
Whether you are defining your AI strategy, building and testing your first use cases, navigating regulatory and governance requirements, or turning real-world outcomes into published evidence, we bring the clinical, behavioural, and technical expertise to move you forward.
- Map friction across the patient or user journey through structured stakeholder interviews spanning leadership, clinical, operational, product, and governance teams.
- Analyse existing data — support tickets, survey responses, operational records — to validate qualitative findings with quantitative evidence.
- Generate a longlist of AI and automation opportunities, then refine and prioritise based on strategic value, technical feasibility, and regulatory alignment.
- Define a phased implementation roadmap with clear sequencing, success measures, and governance requirements.
- Establish a safety and compliance framework aligned to relevant regulatory expectations, including MHRA, CQC, and ISO 42001 where applicable.
- Support investor narratives and fundraising by translating AI strategy into credible, evidenced use case roadmaps that demonstrate clinical, technical, and commercial value.
We run discovery in structured waves — exploratory interviews, data synthesis and validation, then convergence and integration — ensuring recommendations are grounded in both qualitative insight and quantitative evidence, and aligned across functions before any build begins.
A Behavioural Needs and Friction Points map, an AI Opportunity Map, a prioritised use case catalogue with phased roadmaps, an AI architecture blueprint, and a governance and safety framework. The result is clarity on where to invest, confidence in what to build first, and a defensible foundation for responsible AI adoption.
- Conduct an independent review of your current AI environment, mapping where AI systems interact with patient care, clinical workflows, diagnostics, and operational processes.
- Assess governance structures, human oversight arrangements, escalation pathways, and internal review processes against current regulatory expectations.
- Review regulatory exposure across relevant frameworks including MHRA, CQC, GPhC, ICO, NHS DTAC, and clinical safety standards such as DCB0129 and DCB0160.
- Run baseline AI safety testing — simulating patient-facing AI interactions to assess behavioural safety, clinical boundary adherence, and escalation behaviour.
- Develop a practical AI audit framework your team can use to review AI systems before deployment and as capabilities evolve.
- Support investor and board confidence by producing independent, evidenced assessments of AI safety and governance maturity.
We operate independently — without access to system prompts or proprietary architecture — bringing an outside-in perspective that internal teams and regulators find credible. Engagements are typically delivered over six to eight weeks depending on scope, and can be scoped for a single AI system or across an entire AI estate.
An AI landscape overview, governance maturity assessment, risk prioritisation summary, AI safety testing insights, practical governance recommendations, and an outline AI audit framework. The result is clear visibility over where AI introduces risk, a credible response to regulatory scrutiny, and a structured approach to ongoing oversight as your AI capabilities evolve.
- Design and develop patient-facing AI agents grounded in validated behaviour change frameworks including COM-B, CBT, ACT, and motivational interviewing.
- Build conversational AI systems — health coaches, support agents, clinical co-pilots — using advanced behaviour change techniques (BCTs) and habit formation science.
- Develop from concept to minimum viable product (MVP), including prompt engineering, evaluation framework design, and deployment support.
- Test rigorously using PromptSafe — running persona-based simulations and custom clinical and behavioural evaluators before any real-world deployment.
- Iterate in small, controlled steps — validating each component before building the next — so nothing large and untested reaches your users.
- Deliver agents that can be embedded into your existing platform or deployed as standalone tools, with full documentation and handover to your team.
We handle everything from behavioural architecture and prompt design through to build, testing, and handover. Our agents are developed using the same research-assisted, iterative lifecycle we apply to all use cases — starting with the behavioural and clinical research, defining success metrics, building in small increments, validating through PromptSafe, and returning a tested, documented product your team can deploy with confidence.
A working AI agent or health coach, a custom evaluation framework, PromptSafe testing results, prompt libraries, interaction design documentation, and a clear handover pack. The result is a deployable, evidence-informed AI agent that performs safely and effectively with your patient or user population.
- Conduct the technical, clinical, and behavioural science research needed to determine the right approach for each use case.
- Develop custom success metrics and evaluation criteria — defining what good looks like before anything is built.
- Work iteratively through micro-interventions, building and testing in small steps rather than delivering one large, high-risk build.
- Build working prototypes using a research-assisted, iterative development process.
- Validate prototypes through PromptSafe or other appropriate testing methods depending on the use case.
- Return a clear, evidenced recommendation — a tested prototype ready for MVP development, or an honest assessment of why the approach will not work.
Each use case goes through our research-assisted prototyping and iterative development process. We handle all the upfront technical, clinical, and behavioural groundwork, then build and test in small, controlled steps — so your internal team is not spending time and resource on something unproven. If it works, you get a validated prototype and a clear path forward. If it does not, you find out early — before significant cost is committed.
A tested prototype or MVP-ready specification, a custom evaluation framework, and a clear recommendation with supporting evidence. The result is faster learning, lower delivery risk, and your team's time protected for work that matters.
- Map motivation, goals, and barriers to adherence using validated frameworks including COM-B, CBT, ACT, and the MyBarriers Questionnaire (MBQ) — a validated GLP-1 phenotyping tool developed by Sacher AI.
- Define behavioural phenotypes that shape how AI agents, coaches, and support teams deliver personalised support across different patient groups.
- Design personalisation rules for tone, timing, framing, and content intensity — matched to each patient's behavioural profile and stage of treatment.
- Build adaptive support that evolves as patient signals change — from onboarding and early adherence through to plateaus, pauses, and long-term maintenance.
- Integrate behavioural personalisation into AI agents, clinical workflows, and human support teams as a cohesive layer rather than a bolt-on feature.
Behavioural personalisation at scale is a platform-level advantage — one that improves outcomes, strengthens retention, and is difficult to replicate. We design the behavioural layer from the ground up, grounded in clinical and academic evidence, and implement it across your AI and human support systems so every patient receives support matched to their actual needs.
A behavioural phenotyping model, personalisation framework, decision rules for AI and human teams, and working interaction examples across patient segments. The result is more relevant, more effective support — and measurably better engagement, adherence, and retention across your population.
- Identify what drives behaviour across your users, including motivation, barriers, confidence, and readiness.
- Review key moments across the journey where people drop off, hesitate, or need support.
- Define clear user segments that shape how support should be delivered across AI systems and human teams.
These outputs inform how AI agents, clinicians, health coaches, and support teams prioritise, respond, and deliver support across different user groups.
A segmentation model, journey friction map, and a clear plan for how support should differ across user groups, including where AI or human input is most appropriate. Where relevant, this is translated into simple prototype interactions. The result is more relevant support, better engagement, and less wasted effort on one size fits all approaches.
- Design clear onboarding journeys that guide users through what to expect and what to do.
- Create structured support across AI systems and human teams for early questions, concerns, and common issues.
- Focus on known drop off points such as confusion, side effects, and low confidence.
These designs are implemented through AI assisted interactions and team workflows, then tested and refined through small iterations rather than large, high risk builds.
End to end onboarding journeys, support flows, and interaction designs across AI and human touchpoints, with small testable prototypes. The result is better early engagement, fewer support requests, and stronger follow through in the first critical stages.
- Analyse where and why users disengage, pause, or switch providers.
- Design small, targeted interventions that can be tested and improved over time.
- Use AI where appropriate to surface useful signals and support timely follow up alongside human teams.
Interventions are deployed in a controlled way, measured, and iterated based on behavioural and operational outcomes.
A retention strategy, intervention plan, and prototype micro interventions that can be tested before scaling. The result is reduced churn, better continuity, and more efficient use of team time.
- Design consistent communication across AI systems and human teams.
- Create structured prompts, response patterns, and tone guidelines.
- Support teams with AI assisted drafting while keeping review and decision making with humans.
These frameworks are embedded into AI systems and team processes to improve consistency, reduce variation, and strengthen the overall patient experience.
Communication frameworks, prompt libraries, and prototype tools for AI assisted messaging. The result is more consistent communication, reduced team workload, and a better user experience across touchpoints.
- Review how key workflows are currently delivered across clinical, prescribing, and support teams.
- Define clear quality criteria aligned to internal SOPs and expected standards.
- Build AI tools that can automate parts of the auditing process, such as reviewing communications, decisions, or documentation.
- Use audit outputs to identify variation, gaps, and areas where additional training or support is needed.
Audit outputs feed directly into training, workflow redesign, and system improvements, and can show where further automation may be appropriate over time.
An auditing framework, clear quality criteria, and where relevant, a working auditing tool that supports ongoing review of team outputs. Insights are translated into practical actions, including process improvement and targeted training. The result is reduced manual audit burden, improved consistency, clearer performance visibility, and better use of team capacity.
- Map existing workflows across clinical, prescribing, and support teams.
- Identify where AI can reduce manual work and where human input remains essential.
- Design hybrid workflows that balance efficiency with appropriate oversight.
Workflows are redesigned to integrate AI support alongside human roles, then tested and refined before broader rollout.
A workflow map, prioritised opportunities for AI support, and a clear implementation plan. The result is reduced team workload, increased capacity, and more consistent service delivery.
- Design and run pilots using a structured lifecycle of research, small prototype, testing, and iteration.
- Focus on micro interventions rather than large, high risk builds.
- Evaluate using behavioural, clinical, and operational measures.
Successful pilots are refined and scaled, while weaker approaches are adjusted or stopped early before significant cost is committed.
A tested prototype, evaluation results, and a clear roadmap for next steps. The result is lower delivery risk, faster learning, and stronger evidence before scale.
- Define the research question and evaluation framework — what are you trying to demonstrate, and to whom.
- Write the research proposal and support ethical approval applications where required.
- Design the evaluation — whether that is a randomised controlled trial, a service evaluation, a cohort study, or a real-world outcomes analysis.
- Work alongside your internal team to set up, manage, and quality-assure the study.
- Analyse outcomes across behavioural, clinical, and operational domains using your existing platform data.
- Apply the FAST evaluation framework (Fidelity, Accuracy, Safety, Tone) — published in Frontiers in Digital Health, 2025 — where AI system evaluation is required.
- Translate findings into outputs appropriate for the audience — internal reports, white papers, conference abstracts, posters, or peer-reviewed publications.
Many digital health companies are sitting on rich data and real-world outcomes but lack the research infrastructure to turn them into credible evidence. We provide end-to-end support — from concept and design through to analysis and dissemination — working collaboratively with your team at every stage. The level of involvement is flexible: we can lead the whole process or slot in where your own capacity runs out. Academic collaborations include Imperial College London, UCL, University of Texas, Baylor College of Medicine, CDC, NIHR, and NIH.
Research proposal, ethics application support, evaluation design, data analysis, and written outputs tailored to your goals — internal stakeholder reports, regulatory submissions, conference abstracts, posters, or peer-reviewed publications. See our published research at sacher.ai/research-publications ↗. The result is credible, published evidence that strengthens your value proposition, supports regulatory conversations, and demonstrates the real-world impact of your platform.
We work through a rapid lifecycle approach combining research, prototyping, testing, and iteration, delivering small, validated use cases that can be scaled over time.
