Batch pipelines, fragmented data silos, and multi-tier storage architectures are forcing your AI models to score yesterday's transactions, flag last night's fraud, and search vectors across stale data. Every hour of delay is risk your organisation is absorbing — invisibly. VAST Data eliminates the batch window entirely.
Your Current Infrastructure Cost
After VAST Data
The Problem
Slow AI performance is rarely a model problem. It is almost always an infrastructure problem — specifically, multi-tier fragmented architectures that force AI, BI, and security systems to pull data across latency barriers that batch processing cannot hide forever.
Your AI models are trained on and scoring against data exported in overnight batch jobs. By the time the inference pipeline runs, transactions are hours or days old. Risk decisions, credit scores, and anomaly detections are made on yesterday's reality — not the one unfolding right now.
⏱ 12–24 hr data lag — every decision cycleFraud models running on batch-export infrastructure identify suspicious patterns in end-of-day reports — after funds have already moved. The window to intercept is measured in milliseconds. Your current architecture operates in hours. Every false negative is a direct financial loss your systems never see coming.
⚠ Fraud identified retroactively — not at transaction timeData moves between object storage, data lakes, relational warehouses, and specialised analytical engines — each hop adding latency, each tier requiring transformation. BI queries that could run in seconds wait for cross-tier data movement. AI pipelines stall at the storage bottleneck before a single inference is made.
🔴 5–12× query slowdown from tier-to-tier data movementLarge language models, RAG pipelines, and AI agents rely on vector databases to retrieve contextually relevant data in milliseconds. When the vector index sits on slow or fragmented storage, retrieval latency explodes — turning sub-second AI interactions into multi-second waits that degrade every user-facing AI experience.
📊 Vector search latency >800ms on conventional storageWhy Your Architecture Is the Bottleneck
Most enterprise AI performance issues trace back to the same architectural anti-pattern: data is siloed across incompatible systems, each with its own latency profile, access protocol, and transformation requirement. Every AI workload — inference, training, fraud detection, vector search — must navigate this maze before a single result is produced.
Row-based · Slow analytics
Batch export · 12–24hr lag
Cold tier · High latency
Separate silo · Stale data
Disconnected · Batch input
Models wait hours for ETL to deliver data from source systems
Anomaly detection runs after batch export — not at transaction time
Cross-system queries traverse multiple tiers — minutes, not milliseconds
Before — Fragmented Multi-Tier Architecture
Siloed Systems + Batch ETL
Data copied nightly across 5+ systems. AI pipelines queued behind batch export jobs. No unified namespace — every workload requires a separate data movement operation before it can start processing.
After — VAST Data Unified Platform
Single Namespace · All Workloads · Real-Time
One storage platform serves every workload simultaneously — AI inference, vector search, fraud detection, BI analytics, and archival — without data movement, ETL pipelines, or batch export jobs. Data is live the moment it lands.
VAST Data's Disaggregated Shared-Everything (DASE) architecture replaces your entire fragmented storage stack with a single universal platform that serves AI inference, vector search, BI analytics, fraud detection, and cold archival simultaneously — with sub-millisecond latency across all workloads, at any scale.
Unlike traditional storage platforms that force you to choose between performance and capacity, VAST scales compute and storage independently. Your AI teams never wait for data — because data is always where they need it, in the format they need it, at the latency they require.
The VAST Data Fix
VAST Data was architectured from the ground up for the AI era — where every workload demands real-time data access, and no organisation can afford to run its intelligence layer on yesterday's exports.
VAST Data's Kafka-native streaming ingest writes data directly to the NVMe fabric as events arrive — no ETL pipeline, no intermediate landing zone, no nightly batch export job. The moment a transaction is committed, it is available to every AI, BI, and analytics workload simultaneously. Your models stop running on yesterday. They run on now.
✓ Data fresh in <100ms from source eventVAST's sub-millisecond read latency means fraud models can score every transaction against a full historical dataset before settlement is authorised. Pattern matching across billions of records executes in under 5ms — inside the transaction window. The first time you see a fraud alert is before the money moves, not in tomorrow's exception report.
✓ Fraud inference: <5ms — before settlementVAST replaces your fragmented storage stack — object storage, data lake, warehouse, archive — with a single universal namespace. Structured, unstructured, and semi-structured data co-exist in one tier. No data movement between systems. No ETL pipelines maintaining synchronisation. Every team queries the same live data simultaneously without performance conflict.
✓ Single namespace: all data, zero movementVAST's built-in columnar database engine runs analytics directly on the storage layer — eliminating the compute tier entirely for query workloads. BI dashboards that take minutes on fragmented architectures return in under a second. AI agents operate with VAST's native vector database at millisecond retrieval speed — keeping every interaction sub-second regardless of dataset size.
✓ BI queries: <1s · AI agents: <10ms retrievalRecommended Solution Architecture
IES Engineering deploys a complete VAST Data-centred architecture that replaces your fragmented multi-tier storage with a single unified platform — connecting every source system directly to every AI, analytics, and compliance workload through one live data fabric.
Core banking, CRM, ERP, compliance, and operational OLTP systems
Live OLTP EventsMillions of events per second, zero message loss, real-time delivery
Event StreamingUniversal NVMe flash fabric — structured, vector, unstructured, columnar — one namespace
⚡ Core EngineFraud detection, credit scoring, risk models — real-time, sub-5ms
Real-Time AIRAG pipelines, LLM agents, vector similarity search at millisecond speed
Vector & AgentsSub-second dashboards, regulatory reporting, audit trails — all live
Analytics & BIVAST Data Platform Capabilities — Delivered in One System
Analytics run directly on storage — no separate compute tier, no data movement, sub-second BI queries on petabytes
Millisecond similarity search at scale — RAG pipelines, AI agents, and semantic search without a separate vector DB
Sub-millisecond latency across all workloads — AI inference, streaming, archival, all sharing the same flash tier
S3, NFS, SMB, HDFS, and direct API — every protocol, one namespace, no data copies between systems
AI Agents & Vector Search
Every modern AI agent, RAG pipeline, and LLM deployment depends on a vector database that can retrieve semantically relevant context in milliseconds. When that vector index lives on fragmented or slow storage, your AI applications fail in real time — not in batch, but in the middle of a customer interaction or a fraud decision.
VAST Data's built-in vector database runs on the same NVMe fabric as your structured and unstructured data — eliminating the separate vector DB tier that most organisations bolt on as yet another fragmented system.
Vector similarity search across billions of historical transaction patterns — agent retrieves relevant fraud context in <8ms and scores the live transaction before settlement
RAG pipeline pulls the most contextually relevant customer behaviour signals from VAST's vector index — live credit decisions in under 50ms with full explainability
AI agent queries VAST's vector store for similar past regulatory cases and policies — automated SBP/SECP compliance reporting in seconds, not 3-day manual cycles
Most organisations deploy VAST Data and immediately eliminate storage latency. But the true leap — 60,000× faster inference, sub-millisecond model scoring, and 1,000+ concurrent model deployments — only happens when NVIDIA GPU acceleration is layered directly on top of VAST's NVMe fabric. This is the complete picture.
GPU Inference Pipeline — End-to-End Data Flow
Live data — zero batch windows, <1ms read latency
⚡ VAST PlatformDirect memory path to GPU — no CPU bottleneck
GPU DirectGPU-native data preprocessing — 50× faster than pandas
RAPIDS cuDF6912–16384 CUDA cores + 80GB HBM3 memory
🟢 NVIDIA GPU1,000+ model instances, dynamic batching, multi-framework
Triton ISFraud decision, credit score, or agent response delivered
✓ <1ms End-to-EndInference Architecture — Without NVIDIA vs. With NVIDIA + VAST Data
Without NVIDIA — CPU-Only Inference
VAST Data + CPU Processing
VAST eliminates storage latency — data arrives in under 1ms. But CPU cores process inference sequentially. A fraud model scoring a transaction must execute thousands of matrix multiplications one thread at a time. Even with fast storage, the compute layer becomes the new bottleneck at scale.
With NVIDIA + VAST Data — Full GPU Inference Stack
NVMe Fabric → GPU Direct → CUDA Inference
VAST feeds data directly into GPU memory via NVLink — bypassing the CPU entirely. NVIDIA RAPIDS cuDF preprocesses on-GPU. Triton Inference Server manages thousands of model instances with dynamic batching. CUDA's 6,912–16,384 parallel cores execute matrix multiplications simultaneously — turning inference from sequential to massively parallel.
Inference Latency — Same Model, Same VAST Data, CPU vs GPU
NVIDIA AI Stack — Layered on VAST Data NVMe Fabric
6,912–16,384 CUDA cores. 80GB HBM3. 2TB/s memory bandwidth. Handles millions of parallel matrix operations for inference and training simultaneously.
GPU-native DataFrame and ML library. Data preprocessing, feature engineering, and model training run entirely on GPU — 50× faster than CPU pandas pipelines on the same VAST dataset.
NVIDIA's production inference serving platform. Manages 1,000+ concurrent model instances with dynamic batching, auto-scaling, and multi-framework support (TensorRT, PyTorch, ONNX).
NVLink interconnects GPUs at 600GB/s. GPUDirect Storage reads VAST NVMe data directly into GPU memory — eliminating the CPU and RAM copy step entirely for maximum throughput.
What You Gain
Every outcome below represents a measurable operational shift — from infrastructure that forces AI to work on yesterday's data, to a platform where every workload runs on live reality.
Your teams stop waiting for overnight exports. Your fraud models stop chasing settled transactions. Your AI agents stop timing out on slow vector queries. VAST Data makes real-time intelligence the default — not the exception.
Every transaction is scored by your fraud model in under 5ms — inside the authorisation window, before settlement is approved. Retroactive fraud reporting becomes a thing of the past.
Vector retrieval latency drops from 800ms–2s to under 10ms. Every AI agent interaction feels instantaneous — because the data layer is no longer the bottleneck in your AI stack.
Five disconnected storage systems consolidate into one VAST namespace. ETL pipelines, nightly export jobs, and cross-tier synchronisation overhead disappear — along with the teams and cost that maintained them.
Regulatory reports that took 3-day manual cycles run as automated sub-60-second queries against the live columnar store. BI dashboards refresh in real time — not at the end of a batch window.
"We went from a fragmented infrastructure where AI was always a day behind, to a world where every decision our models make is based on what is happening right now. Fraud interception, credit decisions, compliance reporting — it all runs on live data. VAST Data changed what was possible for us."
— CTO, Tier-1 Financial Institution (Reference Available)Ready to Eliminate the Batch Window?
IES Engineering is VAST Data's authorised partner in Pakistan — delivering end-to-end storage transformation from architecture design through deployment and go-live. We have already done this for tier-1 banking infrastructure. Let us assess your current environment and show you exactly what your AI stack could look like with VAST Data at its core.