BayesLab
From deep analysis to premium slides, agentized
732 followers
From deep analysis to premium slides, agentized
732 followers
For non-analysts seeking deep data analysis and beautiful slides. Our autonomous AI analyst handles cleaning, crunching, charting and storytelling within minutes. Then, rerun the entire analysis on new data instantly—same insights, zero effort.
As a mobile app developer, how can I get the most out of you?
BayesLab
@tahatkn I confess using AI to give me ideas here, but with human brain review/approve/edits
User Retention Analysis: Upload your daily active user data/events → Ask "Why did retention drop after version 2.3?" → identifies which user segments were affected and correlates it with specific features or crashes
Funnel Drop-off Investigation: Import your conversion funnel data/user properties → Get insights like "80% of users drop at payment screen on Android 12"
API Performance Tracking: Upload API response time csv → flags "API latency spiked 3x for users in APAC region after 3 AM"
A/B Test Evaluation: Drop in your experiment CSV with conversion rates → Ask "Is variant B significantly better?" → Get statistical significance, confidence intervals, and sample size recommendations
One more, with user behavior/properties: "which group of user is more willing to pay"
This feels less like a dashboard and more like an autonomous analyst. I uploaded data and it immediately surfaced patterns I didn’t think to ask for.
BayesLab
@hello_leo Great to hear~ For business users it's quite hard to think thoroughly as analysts. AI can help a long way here.
Just FYI, we also can pin chart to multiple dashboard if you want to😊
Congrats! How does the agent expose the logic or confidence level behind its conclusions?
BayesLab
@valeriia_kuna Yes, every code logic for computing/charting is exposed. The confidence level is not exposed right now since if it's lower than specific threshold we actually ask the agent to re-evaluate/change a way automatically. We might revisit this again when a specific domain's execution reach certain volume , so that a more nuanced confidence level can be trusted by user.
OpenClaw's v2026.1.30 already ships skill sandboxing by default, so if ClawApp pulls that in, the security gap closes a lot faster than building it from scratch. Biggest remaining gap is the permission model. A compromised agent still inherits the deploying user's full access, and ClawApp's one-click setup means less-technical users won't think to scope that down. Sandboxing plus a permission preset (safe defaults for new users, unlocked for power users) would cover most of the risk surface.
The line 'for people who need analysis but aren't analysts' is the most underrated positioning statement on this page. Because the actual friction in most companies isn't that data is unavailable — it's that the people who understand the business context don't know SQL, and the people who know SQL don't have time. BayesLab is solving a workflow bottleneck that's costing companies days of latency on every important decision.
What really stands out is the reproducibility claim — that you can rerun the same analysis on new data and get consistent results. That's not just a feature. That's a trust signal. Most AI tools feel like rolling dice. This feels like you're building systems, not just generating one-off outputs. Genuinely curious: when you talk to early users, are they more excited about the speed or about the confidence they finally have in presenting numbers they didn't personally calculate?"
Congrats on the launch! The 'waiting for someone else to run the numbers' struggle is so real. I love that you’re treating the whole pipeline as a first-class artifact rather than just a chat interface. Can’t wait to throw some raw CSVs at this and see what it cooks up
BayesLab
@sandy_liusy Thanks so much! You hit the nail on the head—we built Bayeslab specifically to end that 'waiting game' and empower decision-makers to move at the speed of thought.
We strongly believe that for AI to be a true partner, the analysis must be a verifiable and traceable artifact, not just a black-box chat response. That’s why our Deep Analysis Agent focuses on creating transparent, boardroom-ready reports where you can audit the logic behind every insight.
We can’t wait to hear what you think once you throw those CSVs at it! Our Early Bird tier is currently live, so it's the perfect time to explore the full power of the engine.
Let us know how it goes!
Minara
The idea of AI analyst helping you with all kinds of data tasks is pretty promising. There're fragmented analysis platforms and happy to see Bayes Lab comes with an all-in-one platform.
BayesLab
@frank_li13 Indeed there're so many ways to do analysis. I used many, from Excel to ChatGPT to Cursor.
Glad to see many fellow startups sharing the same goal of simplifying any/all parts of data analysis and make it democratized. Let's run ~
BayesLab
@frank_li13 Thanks for the support!
That’s exactly why we built Bayeslab as an all-in-one platform. Our goal is to bridge the gap between raw data and boardroom-ready reports in one seamless flow, making deep analysis accessible to everyone.
Glad to have you with us on this journey!