Sylabot Healthcare
Studies generate more data than most teams can keep tidy. Sylabot Healthcare programmes SDTM and ADaM datasets, TFLs, and the QC trail that sits around them. We do not diagnose, treat, or replace clinical judgement. We build the data systems that study teams already run.
Street-level office addresses are not published. Reach the team through the contact form.
Clinical data programming
Programming is scoped to the study you send us. Specs, code, logs, and output stay together so a reviewer can see how a number was made.
Raw study extracts are mapped into CDISC SDTM and ADaM structures that reviewers can follow. Domains, derivations, and define-ready metadata are built against the protocol and the statistical analysis plan, not a generic template.
Tables, figures, and listings are programmed to the mock shells your biostatistics team approves. Every output traces back to the analysis datasets, so a listing is never a one-off spreadsheet.
Work is delivered in the language your environment already trusts. SAS remains the core for many regulated pipelines. R is used where the sponsor has approved it for analysis, QC support, or visualization.
A second programmer reviews code, logs, and output against the specification. Findings are written down. Nothing is marked complete until independent review agrees the result is ready.
Older studies, mixed formats, and non-standard extracts are remapped into current structures so they can sit beside live programmes without inventing a second standard.
Study acceleration
Artificial Intelligence here is used to structure documents, surface quality issues, and scale repeatable work. Approvals stay with people.
Protocol text is structured into study objects that programmers and data managers can share. The team works from one source, not a marked-up PDF that lives in someone’s inbox.
When a protocol or analysis plan changes, we isolate what the amendment actually moves. Datasets, specs, and TFLs are updated without restarting the whole programme.
Shared standards, reusable macros, and consistent naming let the same team support more than one study. The method stays stable as the portfolio grows.
Automated checks surface missingness, unexpected values, and mapping drift early. A person still decides what to fix. The model does not close a query on its own.
Clinical data strategy
We help sponsors decide what to standardise, what to automate, and what must stay under a programmer. Engagements can be a single study, a programme of work, or a review of standards and QC.
CDISC alignment, define metadata, and traceability are treated as part of the delivery, not a late add-on before a submission window.
A study can be staffed as programming support, a dedicated pod, or an advisory review. Scope is written first. Invented clinical outcomes are not published here.
Automation and document intelligence sit under operational owners. Approvals are not left to the model.
Every engagement starts with the study, the data, and the programmers who will own it. We do not publish invented clinical results.
Sylabot Healthcare is a division of Sylabot Technology, alongside Sylabot AI and Sylabot Talent Acquisition. Clindata AI is the product surface for this work.
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