AVTI has expanded sAÏmone, its AI workspace for Medical Affairs, medical communications and related life-sciences functions. The update brings strategic modelling, historical precedent testing and adaptive workflows into the platform’s research, evidence synthesis and planning capabilities.
The aim is to help teams examine competing courses of action, understand what their recommendations depend on and revisit the analysis when new evidence changes the picture.
What this means in practice
A Medical Affairs team might be weighing a focused scientific education programme against broader engagement across a referral network. sAÏmone can investigate the evidence, compare the approaches under different assumptions and identify what would change the preferred option. The findings can then inform a practical plan or scientific brief.
Test the assumptions behind a strategy
sAÏmone can translate selected strategic questions into structured computational models. These can examine competing options, stakeholder responses, dependencies, uncertainty and the conditions under which a preferred course of action would change.
The AI interprets the problem, proposes assumptions and assesses the implications. Where calculation is useful, numerical work passes to Python-based computation. This separates judgement from arithmetic: the agents shape and interpret the model, while computational tools execute it.
Scenario analysis, Monte Carlo simulation, sensitivity testing and actor-response modelling can support questions in launch planning, evidence generation, competitive intelligence and stakeholder strategy. Their results are conditional working estimates, informed by the evidence and assumptions used. They help a team reason through uncertainty; they do not become a primary source of evidence.
Use historical experience to challenge the thinking
The update extends the use of historical analogues beyond retrieving similar cases. sAÏmone can compare the assumptions behind a proposed strategy with relevant precedents and examine whether the observed behaviour supports or challenges the current model.
When a precedent materially challenges an assumption, the system can revise that assumption, recompute the relevant scenario and reassess the recommendation. Differences in setting, population, timing and implementation remain part of the interpretation.
Historical comparison therefore helps calibrate the analysis. It provides a reason to examine an assumption more closely, rather than treating similarity as proof that the same outcome will follow.
Let the workflow follow the problem
Specialist agents can contribute according to the question being answered. Scientific research, regulatory intelligence, strategic analysis and quantitative work can be brought together as the analysis develops.
Priscilla helps match the request to relevant capabilities and organise the work. The main agent remains responsible for the coherence of the analysis and the integrated answer. Users can steer the conversation, refine the brief or change direction as they go.
A research finding may alter an assumption. That change may lead to a new calculation, revealing a sensitivity or decision threshold worth investigating. The objective is to use each capability where it helps the work, with the depth of research translated into a readable, proportionate output.
Keep the reasoning connected as the work develops
sAÏmone can retain evidence, assumptions, unresolved questions, strategic options and dependencies within the ongoing analysis. Relevant prior work and saved results provide continuity when a question is revisited.
These capabilities sit alongside scientific and web research, Papers, internal knowledge, domain frameworks, stakeholder analysis and tools for developing briefs, documents and presentations. They support Medical Affairs strategy, evidence planning, congress preparation, scientific communications, launch preparation and lifecycle planning.
Professional judgement remains central. Teams can inspect the evidence, challenge assumptions and revise the resulting recommendations. The platform’s AI models can evolve alongside its domain frameworks, computational tools and saved work.
About sAÏmone
sAÏmone is an AI workspace developed by AVTI — Adaptative Vertical Technology International for Medical Affairs, medical communications and related life-sciences functions.
It brings specialist AI agents, scientific evidence, structured domain knowledge and analytical tools together to support research, planning and scientific communication. Its outputs support professional judgement and the review appropriate to the work.