Alternatives
Products that do what Flagright AI Forensics does
The modern standard in AML compliance through AI agents
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2019 · pepchecker.com
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I got tired of slogging through walls of legal text whenever I had to sign something important, so I built a Chrome extension that uses AI to: - Spot red flags and suspicious clauses – It automatically highlights risk areas in real-time as you read through an online contract. - Score the contract’s safety – Based on industry-standard risk parameters, it gives you an at-a-glance score so you can see how risky the document might be. - Simplify the jargon – It generates a quick summary of the key points, saving you from reading every paragraph in detail. No more signing on hunches or bets!…
2025 · aayen.org
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We’re building an open-source tool that makes it easy to expose secure, LLM-optimized APIs on top of your structured data—without manually designing endpoints or worrying about compliance. AI agents and LLM-powered applications need structured access to data, but traditional APIs and databases weren’t built with AI workloads in mind. Our tool automatically generates APIs that: - Filter out PII & sensitive data to comply with GDPR, CPRA, SOC 2, and other regulations. - Provide traceability & auditing, so AI apps aren’t black boxes, and security teams stay in control. - Optimize for AI…
2025 · github.com
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2025 · walidamamou.medium.com
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Hey HN, Over the past couple months, we, a team of Aussie legal and AI experts, have been working on building a new type of legal AI company — a company that, instead of trying to automate legal jobs, is trying to automate legal tasks. We want to make lawyers’ lives easier, not replace them. We’ve been laser-focused on building small and efficient yet still highly accurate, specialized models for some of the most time-consuming and mundane legal tasks lawyers have to perform. Stuff like running through a thousand contracts just to locate any clauses that would allow you to get out early. We…
2025 · docs.isaacus.com
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Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev
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Hi HN, We’re the team at Feather Labs, and we built Feather (https://askfeather.ai), an AI tax assistant designed to assist how professionals handle modern Tax research. General LLMs are a liability for tax work because they lack a hierarchical understanding of the law. They often conflate IRC Title 26 with non-authoritative blog posts or outdated Treasury Regulations. We built Feather to move past "plausible" prose toward audit-defensible reasoning. The Technical Challenge: Standard RAG often chokes on the tax code for a few specific reasons: 1. Hierarchical Fragmentation: Simple…
Feb 2026 · askfeather.ai
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We built PrivateClaw because the hosted OpenClaw platforms on the market today require you to trust them with plaintext. PrivateClaw removes that requirement at the hardware layer. PrivateClaw runs AI agents inside Trusted Execution Environments (TEEs), backed by AMD’s SEV-SNP standard. This means that your data is encrypted at the hardware level, enforced by the AMD Secure Processor outside the host OS trust boundary. PrivateClaw comes with inference that also runs inside TEEs, which means your prompts and completions are private as well. How it works: Each user gets a dedicated CVM…
Apr 2026 · privateclaw.dev
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