● Live Autonomous Agentic MLOps · Phases 1–6 Complete

Neluvi Aethelgard

Autonomous Machine Learning Operations for Regulated Enterprises.

End-to-end MLOps automation: zero-knowledge data ingestion, real-time inference at 2,000+ RPS, drift detection, synthetic data generation, autonomous retraining, champion-challenger promotion, explainable AI, and plain-language audit trails.

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2,000+ RPS Inference
<100ms p99 Latency
6 Phases Complete
5 Security Profiles
1,700+ Tests Passing
The Problem

ML Models Drift, Fail Silently, and Drain Resources

Enterprises in BFSI, Healthcare, SCM, and Government deploy hundreds of predictive models. Most go stale in production without anyone noticing — until revenue drops, risk spikes, or compliance flags fire.

📉
Silent Model Drift
Data distributions shift, features decay, and accuracy drops — often detected weeks after business impact. Manual monitoring can't keep pace.
🔒
PII/PHI Exposure Risk
Training data, feature stores, and inference logs frequently contain sensitive data. Traditional pipelines expose this to LLMs and data warehouses.
⏱️
Slow Retraining Cycles
Months pass between drift detection and a redeployed model. Manual validation, compliance review, and champion-challenger testing create bottlenecks.
📋
Explainability & Audit Gaps
Regulators and risk officers demand SHAP/LIME explanations and immutable audit trails. Most ML platforms generate neither without heavy custom work.
The Core Engine

6-Phase Agentic MLOps Loop. Closed-Loop Autonomy.

From ingestion to promotion, Aethelgard runs a deterministic agent pipeline with HITL gates at retrain, deploy, and compliance override points.

📁 Zero-Knowledge Ingest
⚡ Real-Time Inference
📊 Drift Detection
🧬 Synthetic Data
🏋️ Autonomous Retrain
🏆 Champion-Challenger
✋ HITL Gate
📝 Audit Narrative
Platform Modules

Inference, Governance, and Continuous Learning

🔐
Zero-Knowledge Data Proxy
Deterministic PII/PHI masking, tokenization, and block actions before any external API call or embedding.
📁
Dynamic Schema Ingestion
Ingest CSV, FHIR, ISO 20022, and DB streams. LLM-driven schema mapping with air-gapped fallback.
⚡
Real-Time Inference Gateway
Sub-100ms predictions at 2,000+ RPS with Redis online feature store and model registry integration.
🧠
Agentic MLOps Orchestrator
LangGraph pipeline: monitor → detect drift → generate synthetic data → retrain challenger → champion-challenger promote.
🔍
Explainable AI Engine
SHAP and LIME per prediction and per model version. Global and local feature importance stored and queryable.
📋
Plain-Language Auditor
LLM-generated compliance narratives plus immutable PDF audit reports with SHA-256 artifact checksums.
Security & Compliance

Regulatory Profiles Built In

🏦
BFSI — PCI-DSS tokenization, BASEL IV model tracking, dual approval gates for production deploy.
🏥
Healthcare — HIPAA audit controls, PHI logging barriers, blocked raw PHI exports.
🏛️
Government — Air-gapped mode flag, FedRAMP posture stubs, disabled external LLM calls.
📦
Supply Chain — Data residency, supplier segregation, chain-of-custody evidence collection.
🌐
General — GDPR/CCPA, SOC 2 Type II, ISO 27001 controls with deterministic rule engine.
Aethelgard by Numbers
Phases 1–6 Done PostgreSQL 16 + pgvector Redis 7 Feature Store Qdrant Embeddings LangGraph Agents FastAPI SHAP / LIME Immutable Audit PDF
Supported Data Formats
CSV FHIR R4 ISO 20022 Parquet DB Streams
Ready to Automate MLOps?

Deploy Self-Healing Models in Regulated Environments

Book a 30-minute demo and see Aethelgard detect drift, generate synthetic data, retrain a challenger, and promote it through a champion-challenger gate — with full audit.

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