It has no context
The model has never seen your brand plan, your evidence base, your segmentation or your rules. It answers in general, and your work is specific.
Trusted by pharma teams across Europe
ARIYA
The demo is never the problem. Every model writes well, answers fluently and impresses a room. The problem starts when someone asks where the claim came from, whether the message was approved, and who is accountable if it is wrong. Ariya is built for that second conversation.
THE PROBLEM
Most pharma teams have now run something. A chatbot on a document set, a drafting tool, an assistant in a brand team. Very few of them made it into the way the team actually works. The pattern is consistent, and it has nothing to do with model quality.
The model has never seen your brand plan, your evidence base, your segmentation or your rules. It answers in general, and your work is specific.
An answer with no source behind it cannot be used in a decision or an asset. In this industry, unattributable is unusable.
A summary is not a decision. A draft is not an approved asset. The last mile — the review, the trail, the system it has to land in — is where the value sits and where generic tools stop.
WHAT HAS TO BE TRUE
These are not features. They are the conditions under which a pharma team can put AI output in front of a regulator, a reviewer or a leadership meeting and stand behind it.
Brand plan, key messages, personas, approved claims, evidence base, market rules. Loaded in, not inferred.
Not a footnote culture. A reference on the claim, back to the document, checkable in one click.
You can see how it got there: what it weighed, what it discarded, where it is confident and where it is not.
Review, override and accountability stay with your team. The system makes the work faster, not the responsibility fuzzier.
Without confidence grading, defined logic and human override, AI does not increase velocity. It increases noise.
WHERE THE VALUE SITS
Ask a general model what happened and it will tell you. Ask it what patterns exist and it will find them. Pharma teams rarely need that. The question in the room is what to do next, and whether the recommendation will hold when someone challenges it. That gap, between an answer and a defensible next step, is what Ariya is built to close.
APPLICATIONS
Ariya is one platform with purpose-built applications on top. Same knowledge base, same governance, same referencing. Different jobs.
For commercial, insights and medical affairs teams facing a call they have to defend. Ariya works through the evidence, shows the logic, grades its confidence and puts a recommendation on the table with the reasoning attached.
Explore Ariya →For brand and marketing teams. Creates and adapts assets inside your brand context, using approved claims or pre-approved message modules, with references on every statement and MLR built into the flow.
Explore Content Wizard →For teams working across scientific, medical and commercial material. Retrieval and synthesis over your own sources and the public literature, with the source visible on every answer.
Talk to us →THE PLATFORM
Ariya is deployed as instances, not as a single shared assistant. One instance for a product, one for market research, one for a market. Same interface, different knowledge base, separated context and data. That separation is what makes it usable in an environment where who may see what is a regulatory question and not a preference.
Your data stays yours.
Access controls and deployment standards built for pharma, not adapted to it.
Context is per instance.
A brand team's instance knows that brand and nothing it should not.
The core is built once.
New instances inherit the platform and pay for their own context.
Trusted by pharma teams across Europe
HOW TEAMS START
The programme that works is narrow. Pick one workflow where the pain is recognized and the material already exists. Load the context. Run it on real inputs with the people who own the output. What you learn in twelve weeks on one workflow is worth more than a year of platform evaluation, and the context you build stays with you for the next one.
Talk through where to startThe mature use cases are content creation and localization, knowledge retrieval across internal and published material, competitive and market intelligence, and decision support for commercial and medical teams. The common thread is that all of them work on the company's own material.
By constraining what the system may answer from. Ariya works from your approved sources and attaches a reference to each claim, and it can be configured to work only from pre-approved content. Anyone claiming a model that cannot be wrong is selling something. The workable answer is that every statement is checkable and a human approves.
No. Instances are separated, context is per instance, and access controls are set to pharma deployment standards.
A chatbot retrieves and summarizes. It gives you what is there. Ariya is built to work through the evidence toward something you can act on: the reasoning, the confidence, the recommendation, or the finished asset with its trail intact.
No. You need one workflow and the material that workflow already uses. Waiting for the data to be ready is how programmes stall.
Mid-sized pharma companies and commercial-stage biotech, in Switzerland and across Europe. Commercial excellence, insights and analytics, medical affairs, brand and digital teams.
The useful conversation is not about the technology. It is about one piece of work your team does now that costs more than it should, and whether the output would survive being challenged.