AMA: building enterprise AI agents for regulated industries
Hey everyone 👋 Darius and the AI Hive team here. Open AMA for the next 72 hours.
Some context:
→ Enterprise AI agent platform built specifically for regulated industries
→ Deployments across banking (6-country operations), healthcare (120+ clinic network), manufacturing (including air-gapped facilities)
→ Designed for compliance with GDPR, HIPAA, Vietnam AI Law 134/2025, MAS TRM, and similar frameworks
→ Eighteen years of enterprise IT delivery underneath
Open ground for questions:
→ Product: features, roadmap, pricing, integration patterns
→ Architecture: on-premise vs hybrid, multi-LLM orchestration, MCP integration
→ Enterprise AI in practice: compliance design, governance frameworks, change management
→ APAC AI ecosystem: Vietnam, Singapore, regional regulatory landscape
→ Honest reflections: what's worked, what's failed, what would be built differently
The messier and more specific the question, the more useful the answer. Open for 72 hours or more
Replies
the air-gapped manufacturing deployment is the one I want to poke at. multi-LLM orchestration usually assumes you can hit several model APIs and route between them, but air-gapped means no outbound calls at all. are you running fully local/on-prem models in those environments, and if so how far behind the frontier models are the ones you can actually get running on-prem
@galdayan Really good poke at the air-gapped case. You're right that most multi-LLM orchestration just assumes outbound API calls are always available, and air-gapped breaks that assumption completely. In our manufacturing deployments we run fully local models, primarily Llama 3 70B and some fine-tuned variants depending on the domain, with no outbound calls after initial setup.
The honest answer on frontier gap: for structured tasks like ticket classification, anomaly flagging, and document parsing, local models at that size are close enough that it genuinely doesn't matter. For complex reasoning chains or open-ended generation, there is a gap and we're upfront about it with clients before deployment rather than letting them discover it post-go-live.
Really appreciate you opening this up Darius, love that "honest reflections" bullet. Would love to hear what you'd build differently if you were starting AI Hive today, especially around the on-premise vs hybrid decision. Feels like most teams pick one and regret it 18 months in when the other model would've scaled better. What's the signal you look for now to know which direction fits a client best?
real question on the audit surface: in most regulated shops the compliance ask isnt 'was the agent right' but 'who signed that the agent was right, and when.' how does the platform handle the human sign-off layer? single reviewer, dual-control, role-based chains? and does the sign-off get versioned with the agent output so someone can pull the record 12 months later?
@thenameisarian Great question and honestly one that trips up a lot of teams when they first spec out a compliance workflow. In AI Hive we support role-based approval chains, so you can configure single reviewer, dual-control, or multi-stage sign-off depending on what the compliance team actually requires. Each sign-off is versioned with the exact agent output the reviewer saw, so pulling that record 12 months later gives you the full picture: who approved, what they approved, and which version was live at that moment.
The "who signed off and when" framing is exactly right for regulated environments. It's not just about logging that the agent ran, it's about proving a human with the right authority stood behind that specific output at that specific time.