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35,643 regulatory rules. 1,994 sources. One cited answer.
Compliance teams don’t have trouble finding a rule. The hard part is finding the current version of the rule that applies to them. Those rules come from federal, state and local sources, as web pages and PDFs, and each source changes on its own schedule.
A general AI will still give an answer, but it won’t ask which jurisdiction you’re in or show where the answer came from.
So we built CARA:
→ Automated web and PDF ingestion across 1,994 sources
→ Scheduled change detection, so amended rules get picked up
→ Jurisdiction routing before retrieval
→ Plain-language answers from Claude that cite their sources
→ Stripe billing, tiered access, analytics and audit logs
The model is the last step, not the system.
#RegTech #RAG #LegalTech #AIEngineering #Compliance
The model is the last step, not the system is such a quotable line. Jurisdiction routing before retrieval is the part most AI compliance demos skip, and it is exactly the part that matters.
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After weeks of 14–16 hour workdays, late nights and countless iterations, our new website is finally live! 🚀🌙
Built using Claude Code, Codex, Claude Design, Higgsfield and Figma.
AI is incredibly powerful when used correctly. With the right expertise, creativity and attention to detail, you can create something truly unique that doesn’t look or feel AI-generated.
AI accelerated. Expert led.
I’d love your feedback! What part of the website do you like the most? 👀:
great keep it up
Project AIR is an open-source toolkit that keeps a tamper-proof record of what AI agents do. Every action an agent takes gets signed, chained to the step before it, and tied to the person who authorized it. The chain is anchored to Sigstore Rekor, a public transparency log, so anyone can check later that nothing was changed or deleted. When something goes wrong, you can show exactly what happened and who approved it, instead of just saying it happened.
It also runs 16 detectors over every record, covering all ten risks on the OWASP Top 10 for Agentic Applications, three from the OWASP list for LLM apps, and three of its own. It can stop an agent mid-action until a person signs off. It plugs into LangChain, LlamaIndex, and the OpenAI, Anthropic and Gemini SDKs with a line or two of code.
I built the Python library and command-line tool, published on PyPI as projectair (now at version 1.3.1) under the Apache 2.0 license, plus the website and dashboard in SvelteKit. We run it on our own infrastructure too: every request to vindicara.io is recorded and anchored to Rekor every minute.
Impressive work! 👏 Excellent approach to AI agent security, tamper-proof audit trails, and human oversight for more trustworthy AI systems.
CA Firm Automation Suite — Complete. 5 Stages. 9 Days.
Started with a problem. Finished with a system.
✅ Stage 1 — Client onboarding: form to folder in seconds
✅ Stage 2 — FBR & SECP alerts: 7, 3, 1 day warnings, every client
✅ Stage 3 — Invoice follow-ups: 3 escalating reminders, auto-stop on payment
✅ Stage 4 — Tax data collection: automated document requests with checklist
✅ Stage 5 — New client outreach: personalised emails based on pain point
Built by a 20-year-old CA student who knows this industry from the inside.
Zero per-month cost. Runs free on n8n + Railway.
Looking for the first CA firm to implement this. If that’s you — let’s talk.
For the tax document checklist, I'd match uploads to both the client and tax period before marking an item complete. How do you handle a client uploading last year's statement or a replacement after the checklist has already cleared?