Appsilon builds AI for regulated clinical, statistical, and commercial work, from a one-week proof of concept to a full AI-core system. Underneath, every path is the same: your data stays where it is, a named human signs off, and every run is on the record.
AI is not one thing, and you do not have to start big. Here are four ways to work with us, ordered by how deeply AI runs the system. Pick the shallow end or the deep end. The standard underneath does not change.
We build with AI, so you get a working app in a week instead of a quarter. Test the idea and show stakeholders something real before you commit the budget for the full build.
Learn more →One AI capability, natural-language filtering, auto-summaries, or a decision-support layer, added to the app your team already opens every day. No new project, no dedicated AI hire.
Learn more →Mediforce is our open-source platform for agentic workflows in regulated work. Map a process, decide which steps a person runs, a script runs, or an agent runs, and move one step at a time with an audit trail on every run.
Learn more →A system where AI is the engine, not a feature: agents in production, RAG over your documents and data, multi-agent architecture, built and run with you under one PMO. For work that manual effort made too expensive to attempt.
Learn more →Every path runs on the same standard. Pharma-safe means the model will not leak your data, invent an answer, act without your say-so, or reach a system it was never granted. A named person makes the final call. And every agent run is written down: the inputs, the output, the tools it called, the model version, who reviewed it, and who approved it. When QA or a regulator asks why a decision was made, the answer is already in the record.
Your data stays in your environment. Nothing executes without explicit human approval. The model cannot touch a system it was not granted.
Every agent output goes to a named reviewer, with full context, before it counts as a decision. Nothing ships on the model’s say-so.
Each run records inputs, outputs, tools called, model version, reviewer, and approver. Ready for QA or a regulator on the day they ask.
We are not new to this. Appsilon teams have shipped regulated work for most of the largest names in pharma, and we build the open-source tooling the field runs on, from Rhinoverse for R/Shiny teams to contributions across the Pharmaverse. The point is not the logos. The point is that the work held up under the same review yours has to pass.
⚠ Confirm each company is approved for public reference before publishing.
Analysts stitched together manual reports to work out what was moving Rx, TRx, and NBRx, which slowed every commercial review.
An ML engine and interactive decision trees give the same answer in one tool, on live data, in the meeting where the question gets asked.
Hundreds of R packages in the Pharmaverse ecosystem, with no systematic way to assess quality at scale.
A Mediforce agent scores each package’s health and produces a structured assessment; a human council reviews and approves or overrides it. A live example of how our AI runs in a regulated, open setting.
Expert reviewers spent most of their time on triage across five-to-seven tools instead of applying domain judgment, and blockers surfaced too late to fix cheaply.
A full AI-core system catches issues earlier, routes high-judgment cases to the right people, and gives reviewers context to interpret instead of hunt for.
Confidential engagement — kept de-identified.