Rainbird Technologies

LLMs are powerful, but they’re probabilistic and built on public training data. In finance, precision isn’t optional, it’s mandatory. The ability to build world models from your knowledge, and reason over them logically, provides the guardrails that make AI reliable, delivering results that are repeatable, auditable and trusted. Read the full paper to explore how deterministic reasoning ensures safe, compliant AI: https://lnkd.in/efVKrTGp

A clear reminder that powerful AI isn’t always trustworthy AI. When models optimise for attention instead of accuracy, alignment drifts, often in ways we don’t see until it’s too late. This is why deterministic reasoning and auditability matter, especially in high-stakes environments.

A clear reminder that powerful AI isn’t always trustworthy AI. When models optimise for attention instead of accuracy, alignment drifts, often in ways we don’t see until it’s too late. This is why deterministic reasoning and auditability matter, especially in high-stakes environments.

LLMs can write, code, and converse, but they can’t reason. And that’s becoming a serious liability for enterprises. The State of AI 2025 report highlights the growing unease across regulated industries: when you can’t explain an AI decision, you can’t justify it to others. In financial crime prevention, insurance claims, or tax compliance, that lack of determinism equals exposure: commercial, legal, and reputational. Rainbird’s approach replaces probabilistic guessing with logical, traceable reasoning. It’s AI designed to meet audit and compliance standards by default, not by afterthought. Because when you’re making decisions that affect people’s lives or balance sheets, “probably” doesn’t cut it. Read the full article on Rainbird.AI: “The State of AI 2025: Why Trust Matters More Than Ever.” https://lnkd.in/eDZzHCus

Most AI automation isn’t built for regulation, it’s built for speed. And that’s exactly where the risk lies. If your decision systems rely on probabilistic models or opaque logic, you may already be out of compliance without knowing it. Rainbird shows how to spot the warning signs, and what to do about them. Read: Four Signs Your Decision Automation Is Putting You at Regulatory Risk: https://lnkd.in/eePVVbfx #AI #DecisionIntelligence #ExplainableAI #Automation #Compliance

Human oversight has limits. AI architectures that are deterministic don’t guess, they prove. When every decision follows clear, logical reasoning, trust and compliance stop being an afterthought and are baked-in by design. That’s how critical decisions in finance, banking and insurance move from being human-intensive to confidently automated. Read the article on how deterministic AI closes the trust gap: https://lnkd.in/eYV3-nPm

The Business Insider feature on Salesforce’s struggles with Agentforce captures a wider truth about the state of “agentic AI.” A year after the hype, most large-scale agent deployments remain prototypes in disguise; complex, expensive, and hard to govern. MIT reports that 95% of enterprises investing in generative AI have yet to see ROI. Gartner now predicts that over 40% of agentic AI projects will be abandoned by 2027 due to escalating costs and inadequate risk controls. This isn’t a Salesforce problem. It’s a systems problem. Agentic models built solely on large language models remain probabilistic, impressive at conversation, unreliable at reasoning. They can simulate intelligence, but they can’t guarantee precision, determinism, or auditability. At Rainbird, we believe the future of agents lies in hybrid reasoning: combining generative interfaces with deterministic, auditable decision engines. That’s how you move from experiments to production, and from “probably right” to provably right. The next phase of AI won’t be about sounding human. It will be about being right. Read the article: https://lnkd.in/dcaZu6Ut #AgenticAI #DecisionIntelligence #ExplainableAI #EnterpriseAI #TrustInAI Source: MIT: https://lnkd.in/ep7j_duD Gartner: https://lnkd.in/dXY3VENB

Financial institutions want the benefits of generative AI, without the risk of unpredictable results. Rainbird’s approach ensures that your institutional knowledge is a first-class citizen that makes every decision precise, consistent and auditable. It’s how AI-powered innovation becomes safe to deploy at scale. Read the paper to learn how graph-based inference protects against AI risk: https://lnkd.in/efVKrTGp

For years AI outpaced regulation, but The State of AI 2025 report shows regulators are no longer chasing, they’re setting the pace. From the EU AI Act to US financial guidelines, boards now ask a new question: Can you prove your AI is correct? For highly regulated industries, auditability of AI isn’t a “nice to have.” It’s survival. Banks, insurers, and financial services providers can’t afford the fallout from AI systems that produce untraceable answers or “probably correct” results. Rainbird’s deterministic reasoning engine was built for exactly this world, one where every automated decision must be logical, auditable, and regulator-ready. The message from 2025 is clear: compliance and trust are no longer peripheral conversations. Read the full article “The State of AI 2025: Why Trust Matters More Than Ever.” https://lnkd.in/eDZzHCus

Automation bias is easy to ignore, until it causes real damage. In regulated industries, a single wrong AI output can trigger a major compliance issue, fine or loss of trust. The answer isn’t hiring more people to check the work of AI. It’s designing AI that can explain itself. Our article shows why deterministic reasoning is the only dependable safeguard: https://lnkd.in/eYV3-nPm