AI can produce an answer in seconds, but if your data is scattered across systems and the output hasn't been reconciled or verified, you're still spending all your cycles on verifying (data, context, accuracy etc.) before you can act. That gap, between a fast answer and a decision-ready one, is where most "chat with your data" tools break down. 📅 Join me and the @isotopes_ai team for our webinar on Thursday, 4/23 | 9:30am PDT to learn: → Why the warehouse-as-single-source-of-truth model breaks down in practice → How aidnn assembles, reconciles, and verifies data across disconnected systems → Real customer examples of true self-service analytics If your team is still spending weeks on reconciliation work that should take minutes, this one's for you.

The real bottleneck isn't getting answers faster. It's that the data needed to answer them is fragmented across systems, and it needs to be reconciled and assembled before it can be analyzed. Then the answers need to be accurate to make better decisions. In the upcoming @isotopes_ai webinar, we'll walk through why fragmented enterprise data breaks traditional analytics, why chat-with-data tools paper over the problem instead of solving it, and how a verification-first, multi-agent approach with aidnn delivers answers you can actually act on. Join us April 23rd @ 9:30am PST to learn how you can make better business decisions with verified answers before your competition. Register today to join me: isotopes.ai/webinar/verified…

Excited for @isotopes_ai to be at @HumanX 2026 in San Francisco next week! Come find us at Booth #507. We'd love to connect and give you an invite to our exclusive rooftop event. See you there! #HumanX2026 #AI #IsotopesAI #aidnn #aianalytics #analytics #agents

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The work can now speak for itself. The ability to send your boss a session and have the agent answer "Why did you assume that?" instead of pinging you back on slack required making agents play multi-player.

Most AI products give you a chatbot. You ask, it answers. But that doesn’t work for serious analytical work. Your team doesn't watch every step. They start an analysis, go to meetings, come back the next day. Colleagues need to understand what happened. Sometimes you need to rewind and try a different path. A chatbot can't do any of that. So we threw out the chatbot model and rebuilt around one idea: the event stream. Every action aidnn takes is emitted as a structured, ordered event. That single decision unlocks everything: → Teammates jump in, ask questions anchored to specific moments, and keep going → Close your laptop, come back tomorrow — zero lost progress → Branch from any checkpoint and compare results → See assumptions and reasoning before execution, not after The result feels less like querying a black box and more like working with a teammate who documents their work and can be reviewed for methods, not just results. Full breakdown on the @isotopes_ai blog: blog.isotopes.ai/building-a-…

Getting phenomenal feedback, and resonance, from the Isotopes AI "Team of Rivals" technical paper published in arXiv. Here is the summary from DAIR.AI: "The paper introduces the idea of organizational intelligence for AI: → 50 specialized agents organized into teams (planners, executors, critics, experts) → Hierarchical veto authority; critics can reject outputs entirely, triggering internal retry → Pre-declared acceptance criteria established before any work begins → Remote code execution keeps raw data separate from reasoning context → Different model providers for writers vs critics to avoid monoculture It turns out that coherence emerges from opposing forces. Planners push for clarity, executors push for pragmatism, and critics push for correctness. Their conflicting incentives create boundaries that prevent drift in the system." lnkd.in/gZ9hR3yQ It's great to see how this idea is intuitively resonating with many folks - for this is the best way to make reliably accurate, and consistent, multi-agent systems for production. Needless to say, we are excited to actually have this in production *today* unlike anyone else! Head over to isotopes.ai/signup to try it out! Meanwhile, geek out on actual production traces of multiple analysis with multiple teams of agents... each blue box is an sub-agent and the sequence diagram represents the interaction between them.

Specialized agent teams organized to achieve clear goals, utilizing a remote code executor to separate data transformations from reasoning. Agents execute code remotely, returning only relevant summaries, thereby maintaining a separation between perception and execution.

We just published our whitepaper on building production-grade agentic AI. Core thesis: analytics isn’t a chatbot problem - it’s an orchestration problem. Get the whitepaper → isotopes.ai/whitepapers/orch…

We just published our latest whitepaper "The Orchestration Imperative for AI Agents" and this one is especially interesting for leaders who want to learn about the next evolution of AI. Here's the core insight: Production-grade AI Agents aren't a chatbot problem. They are an orchestration problem. Most multi-agent AI systems fail for predictable reasons—coordination overhead scales nonlinearly, context gets contaminated and rots progressively, and debugging becomes impossible. We've watched teams spend months building "agentic" solutions only to hit these walls. This whitepaper distills the principles @isotopes_ai uses to make sophisticated AI agents work in production: structured workflows, plans as contracts, hierarchy that tames complexity, and humans owning the "what" while the system handles the "how", and more. If you're evaluating any agentic AI for your business - or building one - this is definitely worth a read! Get the whitepaper to learn more → isotopes.ai/whitepapers/orch…

This idea that "AI models will not keep scaling" was conjecture in Nov 2024 when we wrote the first prototype. Through 2025, we have built a system which does not depend on context windows growing and lossy compactions, but go faster with lots of fast flash LLMs

architecture behind aidnn: agentic teams with opposing incentives validating each other’s work. This is how we’re getting production-grade reliability for financial analysis. Read more → isotopes.ai/technology

Orchestrating a Team of Rivals We @isotopes_ai just published our in-depth technical paper on how aidnn achieves production-grade reliability! Model scaling is slowing down. But AI impact doesn't have to plateau. The path forward isn't faster clock-speeds. It's multi-core. Instead of waiting for a single smarter model, aidnn orchestrates multiple LLMs with opposing incentives. No business relies on one employee to handle critical operations. We shouldn't architect AI systems around single-agent execution either. aidnn deploys 50 specialized agents organized as specialized teams: * Planners create execution strategies * Executors perform the work * Critics validate outputs with veto authority * Remote code execution keeps data isolated from reasoning models Each of these agents are coding agents resulting in a highly repeatable & reliable agentic system. We call it "orchestrating a team of rivals." Multiple agents with different failure modes catch what individual models miss. Hierarchical veto authority prevents errors from propagating. The result: 90% error prevention through layered validation. And your data stays secure. This is the architecture behind financial close automation at Invisible Technologies and others — turning weeks of manual work into hours of validated analysis. Read the full technical paper here: isotopes.ai/technology

May you have ever more prosperous probabilities as the wave functions collapse, entropy marches forward, and we begin another elliptical journey on the spacetime curve caused by our local stellar object… Happy New Year! 🚀

Exactly! One analyst spent 3 hours on Q3 working capital analysis. With aidnn they can just share a notebook, CFO clicks explain on anything they want, VP Finance branches it to test scenarios. Done in 20 minutes...

People really get it when they see branching. You share monthly expense analysis. Someone highlights a weird AWS spike, clicks "Explain," gets the breakdown. Then thinks "what if we switched regions?" and branches to test it. That's the moment!