AI RWE platform Frontier AI for
post-approval evidence.

Everything a drug needs after approval that an observational study can answer: post-marketing safety studies, including non-interventional PASS and other safety commitments; real-world evidence on effectiveness and treatment patterns; HEOR and comparative effectiveness for payers and HTA bodies; evidence for label expansion; and external comparators for single-arm trials. Frekil runs these studies on EHR, claims, registry and other data you already license or own.

An assistant answers a question. Frekil runs a study. The protocol and statistical analysis plan are locked before any data is touched. Confounders come from a causal graph whose edges cite the literature, and negative and positive control outcomes and sensitivity analyses such as E-values test the result. Your team signs off each stage, every change is recorded with who, when and why, and the AI writes the code, never the result, so every number comes from code you can open and rerun.

The study protocol follows the HARPER template, or your own, and is checked against the ENCePP checklist, ICH M14 principles and FDA’s RWE guidance. The final study report comes with a reporting checklist mapped to STROBE, RECORD, RECORD-PE, TARGET, ICH M14 and the EMA final study report, so a reviewer can find where each item is addressed.

No. Frekil runs observational (non-interventional) studies on data you license or own. It does not run interventional trials, sites or patient recruitment. Many post-approval commitments, such as non-interventional PASS, comparative effectiveness and external comparator studies, are observational, and those are the studies Frekil is built for.

An AI RWE platform uses AI agents to run real-world evidence (RWE) studies: turning a research question into a study protocol, checking cohort feasibility, extracting the data, running the statistical analysis and writing the final report. Frekil is an AI RWE platform for pharma, biotech, CROs and clinical researchers. Your experts review every step, and the AI never sees patient-level data.

RWE automation hands the repetitive work of a real-world evidence study to software: drafting the protocol, defining cohorts, running feasibility counts, writing data extraction and statistical code, and formatting tables, figures and reports. With Frekil, that work takes minutes to hours instead of months, while your team keeps every scientific decision.

Anything your RWE team does today, faster. Internal hypothesis testing before committing to a full study. Rapid feasibility assessments for trial design. Building complex patient cohorts across multiple data sources. Comparative effectiveness analyses for payer conversations. Post-market safety signal detection. Label expansion evidence for new indications. If it starts with a clinical question and ends with evidence, Frekil automates the pipeline in between.

Bring your own data. Frekil connects to the EHR, claims, registry and pharmacy data you already have, wherever it lives: Databricks, Snowflake, AWS, GCP, Azure or your own data warehouse, including licensed datasets such as MarketScan or Optum. Your data does not need to be in a common data model (CDM) or any specific format. If it is already in a CDM, that works too.

Frekil deploys inside your VPC and connects to your data where it already sits, so we can start working right away. We do not harmonize your data or convert it to a CDM, and no data migration is required. Frekil generates statistical code in R, Python and SQL, the same languages your biostatisticians already work in, with region-specific data residency controls.

Frekil understands medicine. It knows medical ontologies (ICD-10, SNOMED, RxNorm, ATC), resolves clinical synonyms automatically, and is fine-tuned to build patient cohorts from complex, messy clinical datasets. It knows that “Type 2 diabetes”, “T2DM” and “E11.9” are the same thing, and it generates epidemiologically sound study designs, not just SQL queries.

No. Frekil augments your team’s capacity by automating the process-heavy work (cohort building, data extraction, code generation, report formatting) so your biostatisticians focus on the scientific decisions that require their expertise. CROs use Frekil to deliver faster for their sponsors. It’s infrastructure your entire RWE ecosystem can run on.

Frekil’s AI models generate statistical code and study protocols. They never see, process, or train on clinical patient data. The generated code executes in a completely separate, sandboxed environment. This separation is a hard architectural boundary, not a configuration option, and Frekil is HIPAA and GDPR compliant.

Every time Frekil runs an analysis on your data, its agents learn the structure, quirks and patterns of your specific datasets. The next study is faster and more accurate than the last. Cohort definitions get sharper, and data extraction gets cleaner. It’s not a static tool: it’s infrastructure that compounds in value the more your team uses it.

Real-world data is messy by nature. Frekil works with your data as it is: it detects coding inconsistencies, flags data quality issues and documents every analytic decision, without converting your data to a common data model. Missing data patterns are surfaced transparently so your team can make informed analytic decisions rather than discovering problems downstream.

Yes. Medical directors, epidemiologists, and clinical scientists can define studies in natural language — describe the population, exposure, comparator, and outcome. Frekil generates the protocol and the code. Your biostatisticians can review and modify the generated code directly if they want to. Both paths produce the same transparent, reproducible output.

Once Frekil is deployed in your VPC and connected to your data, your first analysis can run in minutes. We recommend starting with an internal hypothesis test or a feasibility question to see the pipeline in action. Most teams are running production studies within the first week.

Yes. The same infrastructure handles oncology, cardiology, rare diseases, immunology — any therapeutic area where RWD exists. Frekil supports multi-country analyses with region-specific data residency and can run hundreds of parallel studies across your portfolio from a single platform.

Frekil offers flexible pricing including per-study and platform options. Contact our team for specifics.