Proud member of the NVIDIA Inception Program — accelerating AI innovation with GPU-powered computing.
Nexus Automate is a unified AI platform — behavioral intelligence, predictive analytics, RAG pipelines, and workflow orchestration — purpose-built for e-commerce brands. Fully managed infrastructure — you connect your data, we handle everything behind it. Live in 30 days.
Proud member of the NVIDIA Inception Program — accelerating AI innovation with GPU-powered computing.
Powered by industry-leading technology
Python
Docker
PostgreSQL
Redis
FalkorDB
Grafana
FastAPI
Elasticsearch
Nexus Automate is a unified AI platform that combines behavioral intelligence, predictive analytics, RAG pipelines, workflow orchestration, and real-time personalization into a single system — purpose-built for e-commerce.
Unlike point solutions that address one problem at a time, Nexus Automate's 12-tower machine learning ensemble creates a universal understanding of every customer, powering personalization, churn prevention, cart recovery, and dynamic pricing from one integrated architecture.
Most e-commerce brands capture a fraction of the value hidden in their customer data. Here's why.
E-commerce platforms generate millions of behavioral signals daily. Most brands capture less than 5% of available personalization opportunities because their tools don't talk to each other.
Separate tools for recommendations, cart recovery, churn prediction, and customer support mean fragmented data, inconsistent experiences, and compounding integration costs.
Building production-grade ML systems for e-commerce requires expertise in graph neural networks, contrastive learning, and real-time inference. Nexus Automate packages this into a deployable platform.
Everything from real-time behavioral AI to enterprise RAG pipelines — integrated into a single platform that compounds value across your entire operation.
Connect with any platform, CRM, or data source. Pre-built connectors and APIs integrate seamlessly with your existing tech stack.
Enterprise-grade security with SSO, OAuth, and encrypted data handling.
Live dashboards, performance metrics, and instant anomaly detection.
Designed to handle growing workloads automatically.
Nexus Automate is a single unified AI platform — not a collection of separate tools. Every module below runs on one integrated, production-ready system with shared data, shared models, and compounding intelligence.
Automate tasks, reduce costs, and improve productivity with intelligent workflow orchestration.
AI-powered conversational agents that handle customer inquiries, product discovery, and support 24/7.
8 distributed AI agents analyze customer behavior in real-time — emotion tracking, funnel analysis, abandonment prediction, and personalized interventions in under 100ms.
Durable, fault-tolerant workflows powered by Temporal — invoice lifecycle management, order fulfillment sagas, subscription dunning, and returns processing that run for days or weeks.
13-technique Ultimate RAG system with HyDE, Multi-Query, GraphRAG, semantic caching, and vector search across PostgreSQL, FalkorDB, and Elasticsearch for context-aware AI responses.
Deploy with built-in observability, performance monitoring, and optimized resource management. We manage the infrastructure — you focus on your business.
Watch how our AI agents process requests in real-time.
Nexus Automate's core is a 12-tower heterogeneous machine learning ensemble (NX-3) that creates 2048-dimensional universal user embeddings from behavioral, transactional, and interaction data — built entirely on March 2026 cutting-edge verified research.
768d, 12-head attention with frequency-domain spectral rescaling (BSARec) and Sinkformer doubly-stochastic attention for contrastive representation learning.
512d PANTHER hybrid generative-discriminative architecture with periodic temporal encoding capturing daily and weekly behavioral cycles.
512d encoder with 8192d projector for redundancy-reduction self-supervised learning. Decorrelates embedding dimensions for maximum feature utilization.
Three capacity variants (384/512/768d) combining MinGRU gated recurrence with Mamba-2 selective state spaces for multi-scale temporal modeling.
18.7M user nodes and 1.5M item nodes with symmetric normalized message passing and degree-bias-aware edge dropout for unbiased collaborative filtering.
Frozen 360M-parameter language model with rank-16 LoRA adapters (DoRA + rsLoRA) on all attention projections, adding rich semantic understanding of product interactions.
Mamba-2 selective SSM with 64-dimensional state for efficient long-range sequence modeling with content-dependent gating and linear-time inference.
Implicit ALS matrix factorization and LightFM hybrid collaborative filtering providing complementary linear signals that neural towers cannot replicate.
Frequency-split architecture with dedicated encoders for popular and long-tail items, ensuring rare products receive specialized representation capacity.
Sparse Mixture-of-Experts with cross-tower attention and masked low-rank feature crossing. Fuses all 12 tower outputs into 2048d universal Matryoshka embeddings deployable at any dimension.
As an NVIDIA Inception member, Nexus Automate integrates GPU-accelerated tools across the entire AI lifecycle — from training to inference to safety. All open-source. All production-grade.
Native Blackwell MXFP8 training with per-block scaling. Fused CUDA kernels for RMSNorm, SwiGLU, and dropout. 18–30% training speedup across all transformer towers.
GPU-accelerated pandas for feature engineering. Zero code changes required — 20–150x faster preprocessing of behavioral and transactional data.
GPU-accelerated scikit-learn for model evaluation, clustering, and dimensionality reduction. 5–175x speedup on validation metrics computation.
Drop-in GPU replacement for PyTorch Geometric. Powers our 18.7M-node graph neural network with CuGraphSAGEConv. 57–243x speedup over CPU graph processing.
GPU Direct Storage via DMA — 18 GB sequence arrays loaded directly to GPU memory bypassing CPU entirely. 3.8x faster data pipeline throughput.
Post-training quantization and QAT calibration for FP8, INT8, and NVFP4. The critical bridge between raw PyTorch training and optimized TensorRT deployment.
Converts calibrated models into FP8-optimized inference engines on L4, delivering 485 TFLOPS — 4x throughput over FP16 with 75% memory reduction.
All 12 NX-3 towers, 6 XGBoost classifiers, and 4 NLP models served from a single L4 GPU. Dynamic batching, ensemble DAG orchestration, gRPC at <0.1ms overhead.
Forest Inference Library serving 6 XGBoost classifiers at 400K+ predictions/sec with p99 <2ms. Native Triton backend — zero additional infrastructure required.
2048-dimensional multilingual embeddings across 26 languages with 8192 token context. +22% retrieval quality over MiniLM with Matryoshka dimension flexibility.
1.2B-parameter cross-encoder for 26-language reranking with 8192-token context. Replaces ms-marco-MiniLM which was measured to degrade search accuracy.
Tri-modal hybrid search — dense + sparse + ColBERT retrieval in a single model across 100+ languages. Critical for product name matching and multilingual catalog queries.
4B-parameter model covering 22 safety categories across 140+ languages. Custom “bring your own policy” support with fast and reasoning modes for content moderation.
570M-parameter NER model detecting 55+ PII types with zero-shot custom labels. Pass domain-specific identifiers (DNI, NIE, IBAN) at inference time alongside regex patterns.
Async-first LLM safety with IORails for parallel checking. Jailbreak prevention, hallucination detection, topic control, and PII filtering via Colang 2.0 custom policies.
LLM vulnerability scanner with 37+ attack probes: prompt injection, jailbreaks, encoding bypasses, data leakage, toxicity. Generates EU AI Act compliance documentation.
KV-aware LLM serving with intelligent routing. 2–7x throughput improvement for self-hosted language models. Used by Instacart, Shopee, and Coupang in production.
Agent observability, evaluation, and security red-teaming. MCP/A2A protocol support for multi-agent coordination across our 7-agent behavioral intelligence system.
1.7B-parameter visual-language embeddings. Text + image → 2048D vectors for visual product search — customers search by photo or screenshot across the entire catalog.
GNN-based fraud detection pipeline via GraphSAGE + XGBoost + Kafka. Processes 208K events/sec for real-time payment fraud screening and anomaly detection.
Turnkey GNN + XGBoost + Triton + Shapley explainability pipeline. Production-ready fraud scoring with interpretable risk factors for compliance teams.
GPU-accelerated data quality pipeline — 20x faster deduplication, quality classification, and dataset cleaning for training data preparation.
GPU-powered document extraction — 15x faster PDF, image, and structured data processing for ingesting product catalogs, invoices, and supplier documentation.
Flask-like interface for Triton Inference Server. 90% less boilerplate for model deployment, rapid prototyping, and custom inference pipeline development.
Multilingual OCR across Spanish, German, French, and English. 885M parameters with WER 0.03–0.06 for digitizing product labels, receipts, and handwritten orders.
Synthetic data generation with Nemotron-Personas for privacy-preserving customer profiles. Solves the cold-start problem for new clients without real user data.
AI-powered checkout negotiation with merchant-controlled pricing policies. ACP/UCP protocols enable autonomous deal-making within defined business rules.
Multimodal RAG + NVClip visual search for end-to-end product discovery. Conversational shopping experience powered by catalog-aware language understanding.
VLM-powered product analysis generating multi-locale descriptions in 10 languages automatically. Transforms sparse catalog data into rich, searchable product content.
Five retrieval strategies combined with LLM-based knowledge graph construction. Advanced context management for complex multi-turn product queries.
Hierarchical Parameter Server with GPU → CPU → SSD tiered caching. 5–62x inference speedup for large embedding tables exceeding single-GPU memory.
Distributed GPU embedding storage for graph neural networks. 57x faster than DGL for multi-GPU graph training on massive user-item interaction graphs.
GPU-accelerated image, video, and audio loading for visual product understanding and multimedia catalog processing at scale.
GPU geospatial processing for location-based targeting, geofencing, and regional demand forecasting across international markets.
3D simulation and digital twin technology for virtual warehouse planning, store layout optimization, and immersive product visualization.
RLHF fine-tuning for custom e-commerce language models. Align AI behavior to brand tone, product expertise, and customer service protocols.
Voice interface for customer support — multilingual speech recognition enabling hands-free product search, voice-driven ordering, and spoken customer queries.
AI-powered video features — virtual try-on, background replacement, and eye-contact correction for video commerce and live shopping experiences.
Custom CUDA kernels via JIT compilation for bespoke GPU-accelerated operations tailored to specific client data patterns and business logic.
Training framework for 100B+ parameter domain-specific language models. Enables building proprietary e-commerce foundation models at unprecedented scale.
An autonomous agent layer powered by a knowledge graph memory system, temporal workflow orchestration, and multi-channel communication — monitoring, analyzing, and acting on your behalf 24/7.
14 search modes including multi-hop graph reasoning. Agents remember every customer interaction, strategy outcome, and business context — building a living knowledge graph that gets smarter over time.
Bi-temporal knowledge with automatic contradiction detection. When customer preferences change, the system updates its understanding while preserving full history for trend analysis.
Redis-compatible graph database powering real-time knowledge retrieval. Agents query customer relationships, product associations, and behavioral patterns in under 1ms.
Graph-based agent workflows with built-in checkpointing and human-in-the-loop approval. Every agent decision is logged, reversible, and auditable for full transparency.
Fault-tolerant orchestration ensuring no agent task is ever lost. Automatic retries, timeout handling, and saga compensation patterns for reliable autonomous operation.
Secure agent communication across WhatsApp, Telegram, Slack, and email. Container-isolated with deny-by-default security — every message channel is sandboxed.
Watches every system metric around the clock. Detects anomalies in real time — CPU spikes, memory leaks, API latency degradation — and alerts you before customers notice.
Continuously scores each client’s deployment health against SLA thresholds. Identifies at-risk accounts, trending issues, and upsell opportunities from behavioral patterns in the knowledge graph.
Every morning at 8:00 AM, a structured briefing lands on your phone — overnight alerts, client health scores, today’s calendar, pending approvals, and key business metrics synthesized from the knowledge graph.
Monitors client engagement patterns and drafts personalized communications for at-risk and upsell opportunities. Every outbound message requires your approval before sending — AI-assisted, human-controlled.
Tracks GDPR, LOPDGDD, and EU AI Act obligations with temporal deadline monitoring. Generates compliance reports, flags regulatory changes, and maintains your ROPA documentation automatically.
When a new client signs up, this agent builds their knowledge graph, configures SLA monitoring, sets up integration points, and guides them through a standardized onboarding workflow — no manual setup required.
Handles inbound client queries by searching the knowledge graph for deployment context, past issues, and product knowledge. Drafts accurate responses for your review — cutting support response time by 80%.
Every customer interaction is tracked to its outcome. The system learns which intervention strategies — discounts, social proof, cross-sells, urgency messaging — convert which customer types, and gets measurably smarter with every session.
Works immediately without GPU. Win/loss ratios per customer type and strategy, cached in Redis. New strategies discovered by AI enter a pending queue for your approval before going live.
After NX-3 training, a deep crossing network predicts conversion probability for every strategy paired with each customer’s unique 2048D embedding. Weekly fine-tuning on new outcome data keeps predictions current.
Intelligent explore/exploit — ~10% of traffic tests uncertain strategies to discover new winners while the rest uses proven top performers. Prevents the system from locking onto a local optimum.
AI-discovered strategies never go live without your approval. When a new approach crosses 50% success rate over 20+ uses, you get notified to approve, reject, or edit before it reaches customers.
More customers means more outcome data. Better data means better predictions. Better predictions mean higher conversion rates. Higher conversions attract more customers. Every client makes the system smarter for the next.
Five production-ready AI systems running in a unified pipeline — built, tested, and ready for deployment.
Every visitor action triggers a cascade of 8 parallel AI agents in under 100ms. Emotion detection reads sentiment from scroll patterns, click velocity, and session timing. Intent scoring predicts purchase probability. Abandonment risk fires personalized interventions — discount offers, exit-intent popups, cart recovery emails — all without human input.
Multi-day workflows that survive crashes and restarts without losing state. A single cart abandonment saga spans 14 days across 5 re-engagement touchpoints — email sequences, SMS, push — all coordinated automatically. Invoice lifecycles, subscription dunning campaigns, and returns processing run as fault-tolerant long-running processes with zero cron jobs.
HyDE generates hypothetical answers to expand the query before retrieval. GraphRAG traverses FalkorDB knowledge graphs to find connected context. Nemotron reranks all candidates by relevance across 26 languages. Multilingual semantic caching returns repeated queries in under 5ms. All databases searched in parallel. Result: sub-21ms recommendation latency.
Every task is automatically routed to the optimal model. Deep reasoning and long documents go to Gemini 2.5 Pro (128K context). Speed-critical inference routes to Groq LLaMA-3 for sub-second responses. Semantic embeddings use Sentence-Transformers. Reranking uses CrossEncoder. You always get the best output at the minimum cost — automatically.
Each data type gets its optimal store. Relational and vector data in PostgreSQL + pgvector. Knowledge graphs in FalkorDB with sub-millisecond traversal. Full-text search in Elasticsearch 8.x with multilingual CJK support. Sub-millisecond caching in Redis. NX-3 neural recommendations via Triton. All queried in parallel — the right engine for every query.
Deployed on AWS eu-south-2 (Spain) for GDPR data residency. GPU-accelerated inference, Multi-AZ database failover, end-to-end encryption, and sub-21ms recommendation latency — hardened across 210+ security dimensions.
Triton Inference Server with TensorRT FP8 on AWS L4. 7 models served simultaneously — NX-3 ensemble, XGBoost, Nemotron embeddings, reranking, content safety, and PII detection. Sub-8ms inference latency.
Multi-AZ deployment on AWS eu-south-2 (Spain) for GDPR data residency. RDS Multi-AZ PostgreSQL with automatic failover in under 35 seconds. TLS 1.3 everywhere. KMS encryption at rest. WAF with OWASP rules and bot protection.
Proprietary 12-tower deep learning ensemble with 24-tower teacher distillation, frequency-domain knowledge transfer, and 2048D Matryoshka embeddings. Model soups with hyperparameter diversity and XGBoost classifiers for task-specific prediction.
Dense, sparse BM25, ColBERT, GraphRAG, HyDE, multi-query, RAG-Fusion, step-back, CoVe, CRAG, Self-RAG, Agentic RAG, and speculative retrieval — with dual-threshold semantic caching and CJK support.
Durable, fault-tolerant workflow orchestration with saga compensation patterns. Cart recovery, order fulfillment, campaign automation — all with retry policies, circuit breakers, and full observability.
210+ security fixes across 4 audits. Row-level tenant isolation across 8 data stores. GDPR + LOPDGDD compliance with data blocking and right-to-erasure cascade. NeMo Guardrails, PII detection, and automated vulnerability scanning via Garak.
PhD-validated benchmarks from Forrester TEI, Klarna's IPO S-1, McKinsey, and Baymard Institute. Adjust the sliders — results update live.
Simple, predictable licensing. Every license includes guided setup, documentation, and a 30-day evaluation period.
For brands getting started with AI-powered e-commerce intelligence.
For scaling brands that need the full AI stack with advanced personalization and automation.
For large-scale operations requiring the full 40-tool NVIDIA stack and dedicated infrastructure.
From installation to production in weeks, not months.
Connect your e-commerce platform, CRM, and analytics tools through pre-built integrations or API. The setup wizard configures the data pipeline for your specific catalog and customer base.
The ML ensemble trains on your historical data — purchase patterns, browse behavior, customer segments. The software adapts its models to your vertical and business metrics.
Deploy behavioral intelligence, cart recovery, and personalization across your customer touchpoints. The platform continuously learns and improves from real-time signals.
Every license includes everything you need for production AI.
Complete API reference, webhook specs, and SDK guides for seamless integration.
Grafana dashboards with real-time metrics, alerting, and performance tracking.
Step-by-step setup guides, video tutorials, and a knowledge base to get your team up and running.
KMS encryption at rest, TLS 1.3 in transit, CloudTrail audit logging, GDPR + LOPDGDD + EU AI Act compliance, DPIA documentation, and automated right-to-erasure across all data stores.
Continuous model improvements, new features, and performance optimizations.
Priority support channel with guaranteed response times on all licenses.
See how Nexus Automate's AI platform can drive personalization, reduce churn, and recover lost revenue for your brand.
Free demo · No commitment · 30-day evaluation period on all licenses
The people behind Nexus Automate's AI software.
Software engineer and technical lead responsible for the design, development, and maintenance of Nexus Automate's AI platform — from the 12-tower ML ensemble to the production deployment pipeline.
Co-founder and strategic advisor. Supporting business development and market positioning across European markets.
Have questions? We've got answers.
Onboarding is self-service with guided setup wizards. You connect your data sources through pre-built integrations, configure the platform for your catalog, and the ML models automatically train on your historical data. Most brands can be live within 4-6 weeks. Full documentation, video tutorials, and support are included with every license.
Your license includes the full software with all active modules, automated monitoring and alerting, continuous software improvements, security updates, new feature releases, and technical support. All licenses are scoped to your usage tier.
Most brands can be live within 4-6 weeks. The platform handles data integration, model training on your historical data, and deployment automatically. It starts generating insights from day one of data ingestion.
The platform is built on FastAPI, Temporal (workflow orchestration), NVIDIA Triton Inference Server with TensorRT FP8, NeMo Guardrails (AI safety), Cognee + Graphiti (knowledge graph memory), LangGraph (agent orchestration), PostgreSQL with pgvector, Redis, FalkorDB, Elasticsearch 8.x, Prometheus & Grafana (monitoring), and Docker. Deployed on AWS eu-south-2 (Spain) with Multi-AZ PostgreSQL, GPU-accelerated inference on L4, and end-to-end encryption. NX-3 training powered by NVIDIA B200 Blackwell GPUs with MXFP8 mixed precision. 13 RAG retrieval techniques with dual-threshold multilingual semantic caching. 7 autonomous AI agents with self-learning strategy pipeline.
Not at all. The platform handles the technical complexity. The software translates your business data into production AI. Full documentation and getting-started tutorials are included with every license.
Absolutely. The platform is built to scale. You can upgrade to a higher license tier or add new modules at any time. No long-term contracts — upgrade or downgrade at each renewal.
The demo showcases the platform using your e-commerce vertical as context — showing how the behavioral intelligence engine, recommendation system, and workflow automation work for your use case. You'll also see projected ROI benchmarks based on industry data.
Every license includes a 30-day evaluation period. Experience the full software with your own data and see real results before committing. Contact us to begin your evaluation.
Building a comparable AI system in-house requires a team of ML engineers, data engineers, and DevOps specialists — typically €500K+/year in personnel costs and 12-18 months to reach production parity. Nexus Automate delivers the equivalent of what a 10-person AI team would build, as a ready-to-deploy software platform, with continuous improvements included.
Yes — the platform supports global deployments. The software supports deployment in any major region worldwide, with multi-language and multi-currency support built into the personalization engine.
Request a demo and we'll show you how the platform drives revenue for e-commerce brands like yours. We respond within 24 hours.