inRoot.io

inRoot.io

商务咨询服务

Our vision is to leverage data to develop a co-pilot for superior problem-solving. Seamless RCAs.

关于我们

We are building the copilot for quality & safety management. Unlock Superior Problem Solving with inRoot.io. Leverage AI, automation, and collaboration to streamline your RCA process. Identify root causes faster, reduce bias, and drive continuous improvement with ease. At inRoot.io, we’re on a mission to transform Root Cause Analysis (RCA) for the remote era. Our advanced, data-driven platform brings together structured problem-solving, team engagement, and AI-enhanced insights to help organizations quickly identify and address the true sources of operational challenges. By blending technology and human expertise, inRoot.io empowers facilitators and teams to solve complex issues effectively—anywhere, anytime. Key Features - Automated Pre-Work & Bias Detection: Through powerful pre-work surveys and bias identification tools, inRoot.io streamlines team alignment before RCA events, minimizing common pitfalls and ensuring a smoother, more productive process. - Customizable Agendas: What it Does: Generates a structured, yet flexible, agenda tailored to the specific RCA event. Why it Matters: Keeps the team on track and ensures that no critical steps are overlooked. - In-Depth Analytics & Insights: Our platform provides deep analytics and insights into team data, guiding RCA facilitators with data-backed recommendations to reach actionable outcomes more efficiently. Why Choose inRoot.io? With inRoot.io, RCA facilitators are equipped to overcome the challenges of remote and hybrid work environments, all while boosting team engagement and creating sustainable solutions. From quick identification of root causes to minimizing recurrence, our solution is designed for impact. Companies using inRoot.io report reduced operational risks, improved team alignment, and a significant boost in problem-solving agility.

网站
https://inroot.io/about
所属行业
商务咨询服务
规模
1 人
类型
私人持股
创立
2024

inRoot.io员工

动态

  • inRoot.io转发了

    查看Aaron Parr的档案,图片

    Transforming RCA for Remote Environments | Bridging Continuous Improvement & Remote Work with Data-Driven Insights

    ?? The holiday season is afoot, need quick wins in RCA? Start here. #1. Automate pre-RCA team polls to uncover initial biases. #2. Use AI tools to analyze past incident data for patterns. #3. Leverage collaboration platforms that centralize findings and insights. These aren’t just “nice-to-haves”—they’re essentials to saving time and refining accuracy. Which one could benefit your team the most? #RootCauseAnalysis #ContinuousImprovement #AI #Leadership

  • 查看inRoot.io的公司主页,图片

    12 位关注者

    The cost of not solving the problem correctly? Massive. Planning is not a checklist—it’s about uncovering insights before the RCA even starts. Here’s an idea to elevate your pre-work: Preliminary Bias Check: 1. Ask team members to independently write down the problem and its causes. 2. Compare answers. 3. Highlight patterns that could signal groupthink. This clarity builds confidence that your RCA is solving the right problem. ?? Want help getting started? Drop a ?? for a free guide!

  • inRoot.io转发了

    查看Aaron Parr的档案,图片

    Transforming RCA for Remote Environments | Bridging Continuous Improvement & Remote Work with Data-Driven Insights

    ?? AI is now accessible for every RCA facilitator – no Ph.D. required! Most people think AI in RCA is reserved for big players with deep pockets. But today, even small teams can leverage AI tools to: #1 Identify patterns in historical failure data #2 Flag human biases #3 Improve problem-solving accuracy Ready to see how AI can elevate your RCA process? Let's connect. What’s one RCA challenge you’d love to automate? #AI #RootCauseAnalysis #Innovation #ContinuousImprovement

    • Enhancing RCA with AI. Funneling historical failure data into highly accurate problem-solving.
  • inRoot.io转发了

    查看麦肯锡的公司主页,图片

    6,117,125 位关注者

    To truly transform an organization, you need more than a bold idea—you need a plan for execution and the commitment to see it through. According to our research, organizations that focus on generating employee will, building critical skills, executing with rigor, and setting a holistic aspiration are far more likely to outperform peers. Discover what it takes to create a winning transformation: mck.co/3zDJ4Kh #Transformation

    • Giving employees opportunities to build new skills—and tools to regularly apply them—boosts the odds of a transformation's success
  • 查看inRoot.io的公司主页,图片

    12 位关注者

    Ever leave an RCA session feeling like you missed the mark? Most RCA failures aren’t in the meeting—they’re in the prep. The right pre-work ensures: #1 Team alignment before day one. #2 Critical data is collected in advance. #3 Biases are identified and mitigated. Here’s one simple tip: Poll your team ahead of the RCA. Ask: #1 What’s the problem, in their words? #2 What data do they wish they had? #3 What potential causes come to mind? Collecting this upfront not only saves time but ensures your session starts with focus and momentum. ?? Want a roadmap to try this? Comment "Poll" below!

  • inRoot.io转发了

    查看Aaron Parr的档案,图片

    Transforming RCA for Remote Environments | Bridging Continuous Improvement & Remote Work with Data-Driven Insights

    ?? Root Cause Analysis (RCA) Pitfall Alert! One of the biggest mistakes I see RCA facilitators make? Jumping to conclusions without a structured data approach. The result? Millions lost because the “real” root cause remains hidden. Solution: Apply a bias-free, data-driven process. AI can automate this, helping identify patterns humans miss. Checkout this upcoming webinar announcement where we'll dive deeper into this topic. Has your team ever fallen into this trap? Share your experience below! ?? https://lnkd.in/eHEq4fm2 #RCA #RootCauseAnalysis #ContinuousImprovement #AI

    查看Enhance International Group的公司主页,图片

    984 位关注者

    Join us for Webinar 3: Digital and AI/ML Collaboration Tools for RCA Streamlining RCA: Smart, Effective, and Rewarding Solutions

    此处无法显示此内容

    在领英 APP 中访问此内容等

  • 查看inRoot.io的公司主页,图片

    12 位关注者

    When things start getting seriously complex, it's more important than ever to start with first principles. At inRoot.io, we believe in applying Six Sigma and statistical principles to AI—treating it like any other statistical model. Anthropic’s latest paper on adding error bars to LLM evaluations is a must-read for anyone serious about precision and accountability in AI.

    查看Anthropic的公司主页,图片

    593,672 位关注者

    Our new research paper: Adding Error Bars to Evals. AI model evaluations don’t usually include statistics or uncertainty. We think they should. Read the blog post: https://lnkd.in/d2jKfpyT When a new AI model is released, the accompanying model card typically reports a matrix of evaluation scores on a variety of standard evaluations, such as MMLU, GPQA, or the LSAT. But it’s unusual for these scores to include any indication of the uncertainty, or randomness, surrounding them. This omission makes it difficult to compare the evaluation scores of two models in a rigorous way. “Randomness” in language model evaluations may take a couple of forms. Any stream of output tokens from a model may be nondeterministic, and so re-evaluating the same model on the same evaluation may produce slightly different results each time. This randomness is known as measurement error. But there’s another form of randomness that’s not visible by the time an evaluation is performed. This is the sampling error; of all possible questions one could ask about a topic, we decide to include some questions in the evaluation, but not others. In our research paper, we recommend techniques for reducing measurement error and properly quantifying sampling error in model evaluations. With a simple assumption in place—that evaluation questions were randomly drawn from some underlying distribution—we develop an analytic framework for model evaluations using statistical theory. Drawing on the science of experimental design, we make a series of recommendations for performing evaluations and reporting the results in a way that maximizes the amount of information conveyed. Our paper makes five core recommendations. These recommendations will likely not surprise readers with a background in statistics or experimentation, but they are not standard in the world of model evaluations. Specifically, our paper recommends: 1. Computing standard errors using the Central Limit Theorem 2. Using clustered standard errors when questions are drawn in related groups 3. Reducing variance by resampling answers and by analyzing next-token probabilities 4. Using paired analysis when two models are tested on the same questions 5. Conducting power analysis to determine whether an evaluation can answer a specific hypothesis. For mathematical details on the theory behind each recommendation, read the full research paper here: https://lnkd.in/dBrr9zFi.

    A statistical approach to model evaluations

    A statistical approach to model evaluations

    anthropic.com

  • inRoot.io转发了

    查看Aaron Parr的档案,图片

    Transforming RCA for Remote Environments | Bridging Continuous Improvement & Remote Work with Data-Driven Insights

    Excited to join this webinar and share how AI/ML tools and collaboration strategies can transform RCA kickoff meetings—hope to see you there!

    查看Enhance International Group的公司主页,图片

    984 位关注者

    Join us for Webinar 3: Digital and AI/ML Collaboration Tools for RCA Streamlining RCA: Smart, Effective, and Rewarding Solutions

    此处无法显示此内容

    在领英 APP 中访问此内容等

  • inRoot.io转发了

    查看Aaron Parr的档案,图片

    Transforming RCA for Remote Environments | Bridging Continuous Improvement & Remote Work with Data-Driven Insights

    ?? In safety, every second counts. Imagine the power of predicting incidents before they happen. That’s the reality for teams that prioritize proactive problem-solving, whether it’s RCA or another framework. AI enhances this by bringing real-time data and insights to Root Cause Analysis (RCA)—especially when teams are pressed for time. For many, RCA has long been a reactive process, resolving issues only after they’ve already disrupted operations. Often, each RCA seems disconnected, failing to reveal recurring or deeper patterns. Today, AI is transforming this approach with multiple different technologies. Imagine an RCA system that learns from each incident, incorporating these learnings into SOP updates for user approval. All of a sudden you've enabled your in-field teams be the authors of their own SOPs, instead of a separate business silo like a training department. This can create a domino effect. Your in-field team now has an avenue to create more relevant SOPs, through an exercise that truly progresses professional development. The Training Department gets the added benefit of being freed up to run actual training sessions. ?? Identifying hidden trends and potential hazards before they escalate is already being done with computer vision. AI-powered RCA leverages vast data sets to pinpoint root causes faster, proactively detecting risk patterns and enabling swift interventions. Industries like oil and gas, manufacturing, and aviation are already witnessing this shift. AI-driven insights mean fewer incidents, safer workplaces, and a cultural shift towards prioritizing safety. It’s not just about faster problem-solving; it’s about using technology to truly protect people. ?? AI-enhanced RCA isn’t a vision for the future—it’s happening now, redefining safety. Let’s lead this shift, making workplaces not only more productive but also fundamentally safer. #RootCauseAnalysis #AI #Safety #WorkplaceSafety #Innovation

    • 该图片无替代文字

相似主页