AI RWE platform.
Real-world evidence in minutes.
Frekil is an AI platform for real-world evidence (RWE). Test hypotheses, build cohorts and run end-to-end RWE studies on your own clinical data with self-improving AI agents that help you revise, discuss and ground outputs in literature at every step.
What our users say.
“I ran a target trial emulation to compare ACE inhibitors vs ARBs for patients with heart failure. The tool generated the exact statistical code I needed to handle confounding, and gave a transparent, auditable trace from the raw data to the final survival analysis.”
“We used the pipeline to validate a new prognostic score for sepsis. The reproducibility is impressive. It generated a complete set of TFLs along with the underlying scripts used to query and structure the hospital records, giving me exactly what I needed for a publication-ready paper.”
“I needed to extract a complex patient cohort and run a sensitivity analysis on treatment sequences. The platform handled the temporal data mapping automatically and gave me the transparent statistical code required to independently verify the clinical science.”
From raw data to
publication-ready evidence.
Frekil uses a continuous, self-improving AI engine to automate the entire epidemiological workflow—while giving you full control through conversational interfaces and feedback loops.
Chat with your data
Connect your EHR, claims, or registries and initiate studies using natural language. The agent structures your inquiry and interrogates the data directly.
Refine each step
You are never out of the loop. Review causal DAGs, adjust cohort definitions, and tweak statistical plans through intuitive feedback before execution.
The 8-Stage Pipeline
Literature Review
Question Clarifier
Cohort Builder
Causal DAG Builder
SAP Generator
Data Extractor
SAP Executor
Report Writer
- RWE ProtocolResearch question to study protocol
- RWE ExtractData extraction and cohort feasibility checks
- RWE AnalyticsAnalysis and the final study report
See exactly how it works.
Dive deep into the technical architecture. Explore how we connect to your data inside your VPC, account for immortal time bias, and execute sandboxed statistical code.
No clinical data
ever touches AI.
AI generates the code. The code runs in a sandbox. The data never leaves.
We built a hard architectural wall between the models and your patient records. This isn't a policy. It's how the system is built.
Stitch the data once.
Generate evidence infinitely.
Frekil runs the same AI RWE platform across every real-world evidence use case — label expansion, post-market safety, payer value dossiers, competitive intelligence, and trial feasibility — tailored to your specific research question and output requirements. And because its agents are self-improving, every study you run makes the next one sharper.
What an AI RWE platform automates-→HEOR & Market Access
Generate comparative effectiveness evidence, cost-effectiveness analyses, and payer-ready value dossiers. Evidence your market access team needs before the formulary decision - not after.
Post-Market Safety
Run safety analyses across millions of patient records in days. Detect adverse event signals before they surface through spontaneous reporting — where the vast majority of reactions currently go undetected.
Label Expansion
Test hypotheses for new indications using real-world patient data. Drug repurposing has a 25-30% success rate compared to 10% for novel compounds - but only if you can generate the evidence fast enough.
Competitive Intelligence
Understand how your drug performs against alternatives in real-world clinical practice, across specific patient subpopulations. Get the data your commercial strategy team needs before formulary decisions are made.
Trial Feasibility
Analyze real-world patient data to assess whether your trial design is realistic before you recruit a single patient. Identify sites, estimate timelines, and test inclusion/exclusion criteria against actual populations.
External Control Arms
Replace placebo arms with synthetic controls built from historical real-world patient data. Accelerate trials for rare diseases and oncology where traditional randomization is ethically or practically difficult.
Clear answers.
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 SAS, 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.
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.
Start with a
single hypothesis.
Someone is waiting for each of these studies.
The bottleneck shouldn't be the evidence infrastructure.
Tell us a little about your work to try Frekil.