Discover More, Decode Less: New data model-driven skill in Tempus One supports faster onboarding and analysis
Data professionals spend as much as 40% of their time searching for data rather than analyzing it. Tempus One now surfaces data model definitions, schema, and analytic best practices for faster analyses with fidelity.
Leveraging real-world data1 (RWD) in drug R&D requires navigating complex data models, adhering to nuanced analytical best practices, and managing the risk that a single misinterpretation sends a program down the wrong path. While general-purpose AI has transformed how scientists write and analyze code, it lacks the contextual knowledge and deterministic rules that rigorous RWD analysis demands.
The cost of that gap is real: data professionals spend as much as 40% of their time searching for data rather than analyzing it.2 Every week spent decoding an unfamiliar data structure is a week of scientific progress lost, a price that is especially hard to accept when cancer patients are waiting on the next life-saving breakthrough.
To mitigate this delay and maximize scientific bandwidth for rigorous research, Tempus One’s knowledge layer now includes skills that ground high-fidelity analyses with deep understanding of the Tempus data model, schema, and real-world data analytical best practices.
Purpose-built for Tempus data
Currently in beta, this new capability provides interactive guidance across data discovery, code generation, and analytic review. Grounded in Tempus’ internal data architecture and the tempusverse suite of analytical packages, it transforms the onboarding experience by offering the contextual support that frontier AI models cannot. General LLMs generate plausible answers but are prone to incorrect mappings, syntax errors, and hallucinations, whereas Tempus One delivers trusted, reproducible results from day one.
By grounding its responses in each client’s specific Lens environment and delivered data tables, Tempus One enables researchers to accelerate time-to-insight through three core capabilities:
Cohort identification. Researchers can ask questions in plain language — such as “How do I start my PDAC cohort?” or “How do I build a cohort of NSCLC patients with no actionable genetic markers?” — and Tempus One maps the request to the correct schemas. Tempus One then generates the logic to define the population, producing inspectable R code rather than black-box answers so users can version, validate, and reuse it.
Data and tool navigation. Rather than combing through dense data manuals to understand how to handle complex genomic or clinical metadata, users receive instant, actionable guidance on the Tempus data model and the right analytic packages, such as tempustables for cohort cutting or tempusoutcomes for survival analysis, to extract precise variables with requests like, “Pull PD-L1 status for my patients of interest” or, “Run outcomes analyses (PFS and OS) anchored to first-line start.”
Intelligent code review and spec-driven analysis. Acting as an expert reviewer, Tempus One checks user-written scripts against Tempus analytical best practices and catches errors in tempusverse function calls or cohort-cutting logic before code runs. For new projects, its Spec-Driven Analysis mode turns a project plan into a complete suite of initial analytical scripts.
Unlike generic coding assistants, Tempus One indexes available packages, accesses data snapshots in your active environment, and even custom guidelines like project-specific statistical recommendations directly into the agent, grounding every response in context and sharply limiting hallucinations. It runs inside your private Lens environment, so data and proprietary hypotheses are never seen by public LLMs. Tempus One was designed and built by scientists, the very people it is meant to serve, and supported by a forward-deployed engineering team to strengthen its rigor and usability.
Less friction, faster insights
This new skill catalog within Tempus One fundamentally changes the ROI equation for platform adoption, shifting the focus from simply accessing data to getting fast insights. By eliminating the traditional technical bottlenecks associated with proprietary schema mapping and package discovery, users can quickly confirm whether Tempus data fits their specific project and immediately start implementing solutions.
- Accelerated onboarding for new users. The time required to formulate a hypothesis, navigate the Tempus data model, and run a new trusted query drops from weeks to a matter of days.
- Massive efficiency gains for power users. Even for seasoned researchers, Tempus One accelerates the work, automating boilerplate code and delivering instant, in-line reviews against Tempus best practices so that analytical tasks that once took a week of coding and debugging now take a single afternoon.
- Reduced risk for everyone. Code review by Tempus One catches common RWD mistakes and aligns analyses with best practices before they reach a result, with automated reviews and fixes on par with manual review by Tempus’ scientists-on-site. The payoff is more than saved time; it also means avoiding a wrong go/no-go decision or an analysis that won’t hold up to critical review.
What researchers have to say
This is brilliant.
I tried the Tempus AI agent to modify the code you shared and it worked better and easier than I expected.
Can you train my whole team?
Tempus One worked better than other chatbots at understanding drug names – generic vs. brand name.
Tempus One ensures scientific teams spend their time rigorously testing hypotheses and driving research forward, not decoding data structures or troubleshooting code.
See what Tempus One in Lens can do for your program. Now in beta.
- Tempus RWD has been de-identified in accordance with HIPAA.
- McKinsey & Company. Reducing data costs without jeopardizing growth. Accessed October 8, 2026. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/reducing-data-costs-without-jeopardizing-growth
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