Millions of weekly users. Thousands of commercial customers. 1 in 5 Fortune 500.

Prove it with Posit.

Data. Code. Environment. Repeat.

AI can hand you a plausible answer in seconds. Getting the same answer twice is harder. Take your data, the code, run it in an environment you control, and publish an artifact someone else can inspect and rerun.

Prove it hero

TRUSTED BY LEADING DATA TEAMS >

Influencing decisions. Rarely verified.

Companies let AI guide real decisions. Few teams check the reasoning behind the output before they act on it.

64%

of enterprise leaders use AI to guide decisions and provide evidence*

57%

of analytical model failures stem from committing too early to an incorrect interpretation ☩

<20%

of organizations review AI-generated output before it is put to use ‡

Confidence alone won’t do when AI is used to shape strategy or customer outcomes. Teams need a repeatable way to review the output and catch flawed reasoning before it influences a decision.

Plausible is not provable

We built Posit on the belief that an analytical insight is only as good as the code behind it. Whoever produced it, whatever tool drafted it, the method has to exist as code that someone else can open and rerun to get the same result from. 

A correct analysis means the method and the numbers are right. A verifiable analysis means a manager, a board member, or an auditor can rely on it before committing money or reputation: they can see what code ran, against what data, in which environment, by whom, and whether the whole artifact reproduces the same result later. 

Specifically:

  • What code - the exact version that executed
  • Against what data - the specific inputs, as they were at the time
  • In which environment - packages pinned, versions captured
  • And rerun later - same artifact, same result, today or years from now

Ask the same question twice. Get two answers.

Large language models are built on statistics, not calculators. They're designed to guess the most likely next word, which makes them inherently unpredictable. 

The inconvenient truth is that if you ask your question again, you are likely to get a different answer. Which one is right?

That variability is a feature when you want creativity. It's a liability when you need a factual, mathematical deliverable.

"I asked the model" isn't a method a reviewer can accept, because asking again isn't a reproduction - it's a new version.

Code doesn't have this problem. Pin the environment and the same code on the same data returns the same result every time.

Generate once. Rerun forever.

Use models for what they do well: proposing the approach and drafting code. Then run that code in an environment you control, on data you own, with packages you can pin, and publish an artifact that holds all of it together.

You hand the reviewer the artifact: its data, code, and environment, all in one place. As a business user, you may never read a line of the code. The people whose job is to check it can. A data practitioner, a compliance reviewer, an external auditor, opens the same artifact and regenerates the same result.

When you re-prompt the model

  • Ask twice, get two results, with no basis for preferring either
  • Asking again is a new draw, not a reproduction
  • There's nothing to hand over except the conversation
  • Nobody can say which version informed the decision

When you rerun the artifact

  • The same result comes back, this quarter and years from now
  • A reviewer opens the exact code, data, and environment that produced it
  • The work is attributed, so "who ran this" has an answer
  • Model variability stays where it belongs, in the drafting

Analysis that held up, across every industry we serve

  • Gen Re

    Gen Re's underwriting team processes roughly 1,500 insurance submissions daily, and with Posit, what once took 30 minutes now takes 5. By unifying 230 data scientists and actuaries across 7 global offices on a single governed platform, Gen Re replaced fragmented spreadsheet workflows with scalable, auditable analytics built for a regulated global business.
    Video thumbnail of Matthew Montero, Chief Data Officer at Gen Re, discussing the company's data and AI strategy

    Trillium

    Trillium has revolutionized its proprietary trading operations by moving from static Excel workbooks to a centralized, automated reporting environment on Posit Connect. This transformation has slashed report delivery times by 50%, providing traders and managers with real-time performance insights and "Trader Wrapped" summaries that drive smarter, faster decision-making in volatile markets.
    Trillium team standing behind Nasdaq podium.

    KBRA

    Discover how KBRA’s quantitative team uses Posit Connect and Shiny to get real-time stakeholder feedback and speed up the credit model development cycle.
    World Map

  • NMDP

    NMDP is revolutionizing the transplant process by using machine learning to predict donor availability across its global registry of 42 million people. By leveraging Posit Connect and Snowflake to automate daily "Donor Readiness Scores," the organization empowers physicians to prioritize the most available matches, directly accelerating the path to life-saving cures.
    A man looking at a monitor that reads "Data Science and Insights Storefront"

    TruDiagnostic

    TruDiagnostic faced an infrastructure challenge, managing the world's largest epigenetic dataset with fragmented tools. By adopting Posit Workbench with Amazon SageMaker, they empowered their scientists to control their computational resources with greater efficiency, unifying their R and Python environments. This transformation saved them a full year of product development time, cut their cloud infrastructure costs by 60%, and increased both their AI and statistical model training performance tenfold.
    Lab data displayed on computer screens
    TruDiagnostic logo

    Novo Nordisk

    Novo Nordisk is leading a paradigm shift in pharmaceutical research by successfully delivering its first FDA submission package featuring outputs created exclusively with R. By leveraging the Posit Team suite, their researchers have built a modern statistical computing environment that bridges R and Python to streamline clinical trial reporting and accelerate the delivery of chronic disease treatments.
    A man doing analytics work on a monitor

  • DairyNZ

    DairyNZ revolutionized its research and advocacy for over 11,000 farmers by replacing siloed Excel spreadsheets with a robust, Snowflake-powered data science environment. By leveraging Posit Team, they now deliver critical economic and pasture insights 4x faster, moving from 24-month delays to real-time, forward-looking tools for sustainable farming.
    A cow is in the foreground looking at the camera and another cow is eating grass in the background. Posit, Snowflake, and DairyNZ's logos are overlayed

    NASA

    Leveraging a combination of Posit Team and Amazon Web Services (AWS), NASA's People Analytics Team provides the insight necessary for strategic workforce planning and dynamic scenario planning to assess the impact of internal and external factors on the capacity and capability of the organization’s human capital.
    Nasa Space Shuttle
    NASA

    City of Reykjavik

    The City of Reykjavík is leading a digital transformation by turning complex municipal data into intuitive "data products" that directly improve daily life for its residents. From predicting geothermal pool occupancy to automating waste collection schedules, the city's data science team uses Posit Connect to deliver rapid, transparent solutions that build public trust and enhance urban living.
    The blue lagoon in iceland

  • Suffolk

    Suffolk is one of the 25 largest general contractors in the United States. They manage some of the most complex, sophisticated projects, serving clients in every major industry sector, including healthcare, life sciences, education, gaming, transportation/aviation, federal government and public work, mission critical and commercial.
    Suffolk construction employee

    Dow

    Dow is leveraging the power of data science to tackle the ambitious goal of reaching net-zero carbon emissions by 2050. By empowering over 300 "citizen data scientists" through Posit Academy and providing a low-barrier-to-entry environment with Posit Workbench, Dow is accelerating the innovation needed to push material science to its sustainable limits.
    Video thumbnail of James Wade, Research Scientist at Dow, discussing sustainability and innovation.

    Brown-Forman

    Brown-Forman revolutionized its internal operations by empowering statisticians to become application developers and data engineers through the Posit Team suite. By automating manual reporting and financial modeling, they’ve replaced high-cost third-party SaaS contracts with custom, reproducible tools that save dozens of hours across the organization every month.
    A barrel room with text that says "Brown-Forman handcrafts our own barrels"

Verifiability is a by-product of the work

Open by design. Easy to integrate. Made to last.

Proven Enterprise Adoption

Millions of weekly users, thousands of commercial customers, about 1 in 5 Fortune 500 companies. Reduces adoption risk before we've described a single feature.

Code-first Data Science

Polyglot by design: R and Python together, from analysis to production apps. This is all we do. It isn't one slice of a bigger platform.
 

Comprehensive Governance

Governed workspaces, versioned packages, executable code, and a publishing layer that records what ran, on what, by whom. Nobody stops to assemble the evidence later, because it accumulated while the work happened.
 

Responsible AI

Posit Assistant returns inspectable code, and the audit trail covers it, including when an assistant wrote the draft. You keep the run. There's no transcript to keep. Built on zero data retention with our model partners.
 

Software & Cloud Neutrality

Multi-language, multi-IDE, multi-deployment, from air-gapped to cloud. Choice over lock-in.

Built for the Long Run

Reproducible research is named in the corporate charter. A Public Benefit Corporation with 17 years of sole focus on creating tools for the community of data practitioners.

References:

* Wharton, Human-AI Research “Accountable Acceleration: Gen AI Fast-Tracks into the Enterprise” October 2025, https://ai.wharton.upenn.edu/wp-content/uploads/2025/10/2025-Wharton-GBK-AI-Adoption-Report_Full-Report.pdf

☩ Artificial Analysis “Announcing AA-AnalystAgent: An agentic benchmark for quantitative analysis on real-world spreadsheets and documents” August, 2026 https://artificialanalysis.ai/articles/aa-analyst-agent

‡ McKinsey “The state of AI: How organizations are rewiring to capture value” March, 2025 https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-how-organizations-are-rewiring-to-capture-value