The data exists.
The evidence doesn't. Yet.
Accelerate Real-World Evidence (RWE) generation from months to minutes. Test hypotheses, build cohorts, and run studies — all from your own clinical data.
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 chaos to
scientific rigor.
Bring Your Own Data
Connect EHR, claims, or registry data. Frekil automatically maps schemas and standardizes terminologies to OMOP CDM.
Protocol Generation
AI drafts best-practice study protocols. Define cohorts, covariates, and endpoints in natural language.
Code Execution
Transparent R/Python code generation. Executed in a secure sandbox. Zero patient data exposure.
Evidence Output
Auto-generated TFLs (Tables, Figures, Listings) ready for publication, payer conversations, or internal decision-making.
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 evidence infrastructure across every RWE use case — label expansion, post-market safety, payer value dossiers, competitive intelligence, and trial feasibility — tailored to your specific research question and output requirements.
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.
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.
Frekil connects to EHR, claims, registry, and pharmacy datasets you already have access to — whether through existing data licenses (such as MarketScan, Optum, or Flatiron datasets), institutional data warehouses, or data partner relationships. We connect directly to Databricks, Snowflake, and major cloud data platforms. Your data stays where it is.
Frekil generates statistical code in R, Python, and SAS — the same languages your biostatisticians already work in. We connect natively to Databricks, Snowflake, AWS, GCP, and Azure environments with region-specific data residency controls. No data migration required.
Generic platforms like Databricks or Tableau can query and visualize data, but they don’t understand medicine. Frekil is purpose-built for RWE: it understands 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. It generates epidemiologically sound study designs — not just SQL queries.
No. Frekil augments your team’s capacity by automating the process-heavy work — data harmonization, cohort building, 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. Data mappings get cleaner. It’s not a static tool — it’s an infrastructure that compounds in value the more your team uses it.
Real-world data is messy by nature. Frekil’s harmonization engine automatically detects coding inconsistencies, maps non-standard terminologies to OMOP CDM, flags data quality issues, and documents every transformation. 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 your data is connected, 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.