The AI RWE platform
for real-world evidence studies.
An AI RWE platform uses AI agents to run real-world evidence (RWE) studies: from the research question to the protocol, cohort feasibility, data extraction, statistical analysis and the final report. Frekil is an AI RWE platform for pharma, biotech, CROs and clinical researchers. It runs on your own data, deploys inside your VPC, and keeps your experts in control of every scientific decision.
What is an AI RWE platform?
Real-world evidence is evidence about how treatments work in routine care, drawn from real-world data such as electronic health records (EHR), insurance claims, disease registries and pharmacy records. Producing it has traditionally meant months of manual work by epidemiologists, data engineers and biostatisticians for every study.
An AI RWE platform automates that work. AI agents draft the study design, write and run the data and statistical code, and assemble the results, while your experts review and approve each step. The result is RWE automation that keeps scientific rigor: the same methods your team already trusts, delivered in minutes to hours instead of months.
What Frekil's AI RWE platform automates
Study protocol
Turns a research question into a structured protocol: population, exposure, comparator, outcomes and the analysis plan, grounded in the published literature.
Cohort definition and feasibility
Builds the patient cohort from your inclusion and exclusion criteria and tells you early whether there are enough patients to answer the question.
Data extraction
Writes and runs the extraction code against your data where it already lives, with every clinical code and definition documented.
Statistical analysis
Executes the pre-specified analysis in R, Python or SAS in a sandbox, with diagnostics your biostatisticians can inspect and rerun.
Final study report
Drafts publication-ready tables, figures and the written report, with an audit trail from every number back to the code that produced it.
- RWE Protocol: Research question to study protocol
- RWE Extract: Data extraction and cohort feasibility checks
- RWE Analytics: Analysis and the final study report
Bring your own data, in any format
Frekil connects to your real-world data wherever it lives today: Databricks, Snowflake, AWS, GCP, Azure or your own data warehouse. We deploy inside your VPC and start working on your data as it is.
Your data does not need to be in a common data model (CDM) or any specific format, and we do not harmonize or convert it. If it is already in a CDM, that works too. Patient-level data never leaves your environment, and the AI never sees it.
Who uses an AI RWE platform
Any team that turns real-world data into decisions: market access, safety, medical affairs, clinical development and academic research.
What to look for in an AI RWE platform
Transparent code
Every cohort, extraction and analysis step should produce code a biostatistician can read, change and rerun.
Experts in the loop
The platform should pause for review at the protocol, the cohort, the analysis plan and the results, not just at the end.
Reproducible results
The same code on the same data snapshot should give the same answer, with a full audit trail for regulators and reviewers.
Your data stays in your environment
The platform should come to your data, inside your VPC, and the AI should never see patient-level records.
No forced data conversion
You should not have to re-model your data before the first study. Working with data as it is shortens time to first evidence.
Grounded in the literature
Design choices such as confounders, outcomes and washout windows should cite the published evidence they rest on.
Clear answers.
An AI RWE platform runs the steps of a real-world evidence study with AI agents. It turns a research question into a protocol, checks whether enough patients meet the criteria, extracts the data, runs the pre-specified statistical analysis and drafts the study report. Experts review and approve each step, so the evidence stays defensible.
Yes, with people in the loop. Frekil’s agents carry a study from research question to final report, and your epidemiologists and biostatisticians review the protocol, the cohort definitions, the analysis plan and the results before anything is final. Every step produces code and documentation your team can inspect and rerun.
No. Frekil connects to your data in whatever format it is in today, on Databricks, Snowflake, AWS, GCP, Azure or your own data warehouse, and deploys inside your VPC. We do not harmonize your data or convert it to a CDM. If your data is already in a CDM, that works too.
No. Frekil’s AI writes protocols and statistical code; it never sees, processes or trains on patient-level data. The generated code runs in a separate, sandboxed environment inside your VPC, where your data already lives.
Comparative effectiveness and safety studies, treatment patterns, disease burden and epidemiology, trial feasibility, external control arms and target trial emulations, across EHR, claims, registry and pharmacy data.
HEOR and market access teams, epidemiology and medical affairs teams, drug safety teams, clinical development teams checking feasibility, CROs delivering studies for sponsors, and academic researchers.
Run your next real-world evidence study with AI.
See Frekil's AI RWE platform on a question from your own portfolio.