@mdhossainSovereign AI infrastructure with Vane Enterprise: Md Abul Hossain
Hi everyone, I'm Md Abul Hossain, SVP & Head of Strategic Partnerships at Taru Global Access, based in Finland. I'm an AMD ROCm Certified Associate, and I work on sovereign AI infrastructure and security. Through Vane Enterprise I build Vane-Guard, an open framework for enterprise RAG pipelines that aims to reduce hallucinations. It gives you source citations, confidence scoring and an auditable evidence trail. My stack includes Claude, Llama, Amazon Bedrock, Gemini, LangGraph, PyTorch, vLLM and Claude Code.
The framework is at v1.0 and the repos are public on GitHub (@AnticipatedD). I'm still working out how to get it in front of the right people and how to fund it. Right now it is supported through an Open Collective.
What I'd love from this community: an introduction to anyone deploying RAG in regulated or compliance-heavy enterprises, who would give me honest feedback on whether deterministic output controls solve a real problem for them.
Comments2
Welcome to Lounge, Md Abul. The auditable evidence trail seems like the part regulated buyers would care about most, since confidence scores alone are hard to defend to an auditor. How are you computing confidence today, and do you see compliance teams or engineering teams as the first buyer?
Hi Ryan, You hit the exact nail on the head. Regulated buyers don’t care about statistical probabilities; they care about deterministic verification. To answer your questions: 1. How We Compute Confidence Today: Instead of relying on typical soft-margin LLM token probabilities (which are highly unreliable and easily spoofed during drift), Vane-Guard utilizes a Multi-Layer Semantic Grounding & Boundary Constraint Validation framework. We process outputs through three distinct layers: • Contextual Alignment Vectoring: Cross-referencing generated outputs against an immutable, local vector datastore. • Schema Constraint Assertions: Programmatic boundary gates that reject non-conforming structures before compilation. • Deterministic Rule Mapping: Passing the response through a hard-coded regulatory checklist. If an output violates a single structural rule, the orchestrator forces an immediate, safe boundary exception rather than serving a low-confidence guess. It gives an auditor a transparent binary trail: Pass or Fail, grounded in hard data. 2. The First Buyer (Compliance vs. Engineering): Our primary go-to-market buyer is the Chief Compliance Officer (CCO) / Risk Management Lead, immediately seconded by the Enterprise Security Team. Engineering teams usually want fast deployment and high flexibility. However, Compliance and Information Security teams are the ones facing severe legal blockages trying to push AI into production within regulated spaces (Finance, Aerospace, European Legal frameworks). By giving Compliance a zero-trust audit trail they can easily defend to a regulator, we remove the internal gridlock, enabling the Engineering teams to safely deploy the stack. I’m looking forward to expanding on this architecture. We are currently preparing to open slots for strategic enterprise advisory pilots to stress-test these boundary gates. Best regards, Md Abul Hossain SVP & Head of Strategic Partnerships, Taru Global Access Lead Architect, Vane Enterprise
2 comments
Welcome to Lounge, Md Abul. The auditable evidence trail seems like the part regulated buyers would care about most, since confidence scores alone are hard to defend to an auditor. How are you computing confidence today, and do you see compliance teams or engineering teams as the first buyer?
Hi Ryan, You hit the exact nail on the head. Regulated buyers don’t care about statistical probabilities; they care about deterministic verification. To answer your questions: 1. How We Compute Confidence Today: Instead of relying on typical soft-margin LLM token probabilities (which are highly unreliable and easily spoofed during drift), Vane-Guard utilizes a Multi-Layer Semantic Grounding & Boundary Constraint Validation framework. We process outputs through three distinct layers: • Contextual Alignment Vectoring: Cross-referencing generated outputs against an immutable, local vector datastore. • Schema Constraint Assertions: Programmatic boundary gates that reject non-conforming structures before compilation. • Deterministic Rule Mapping: Passing the response through a hard-coded regulatory checklist. If an output violates a single structural rule, the orchestrator forces an immediate, safe boundary exception rather than serving a low-confidence guess. It gives an auditor a transparent binary trail: Pass or Fail, grounded in hard data. 2. The First Buyer (Compliance vs. Engineering): Our primary go-to-market buyer is the Chief Compliance Officer (CCO) / Risk Management Lead, immediately seconded by the Enterprise Security Team. Engineering teams usually want fast deployment and high flexibility. However, Compliance and Information Security teams are the ones facing severe legal blockages trying to push AI into production within regulated spaces (Finance, Aerospace, European Legal frameworks). By giving Compliance a zero-trust audit trail they can easily defend to a regulator, we remove the internal gridlock, enabling the Engineering teams to safely deploy the stack. I’m looking forward to expanding on this architecture. We are currently preparing to open slots for strategic enterprise advisory pilots to stress-test these boundary gates. Best regards, Md Abul Hossain SVP & Head of Strategic Partnerships, Taru Global Access Lead Architect, Vane Enterprise