Gen AI Lab

Get more knowledge about Gen AI Lab Package and our brochure 

Contact US
Lab Brochure

Gen AI Lab Package for Banks

A turnkey program that lets your bank establish its own secure, internal AI environment — infrastructure, software, an operational LLM and the people skills to run it. Everything you need to start delivering Generative and Agentic AI use cases from inside the bank’s perimeter.

01
AI Platform Infrastructure
Complete setup of compact, NVIDIA GTC 2026-class AI platforms sized for the bank's workloads, user base and target model scale.
02
Software Environment
Installation and configuration of the AI runtime, vector database, orchestration and observability stack.
03
LLM Use Case Deployment
One end-to-end banking AI use case — selection, knowledge connection, deployment and handover, fully on-premise.
04
Technical Enablement
End-to-end engineering enablement: integration, security review, operations runbooks and handover.

AI Platform Capacity

CONCURRENT USERS

~225 users

Combined capacity for bank-wide AI services

MODEL CAPACITY

Up to 200B+

Parameter LLMs frontier & foundation models

AI PERFORMANCE

~21 PFLOPS

Aggregate FP4 inference throughput

WORKLOAD COVERAGE

Inference + Tuning

RAG, Agentic AI and on-device fine-tuning

SCALABILITY

Up to 4 units

Linear growth as adoption expands

Software Environment

Software installation

All required packages and dependencies installed and prepared to run the AI environment.

Environment setup

System and security settings configured to match the bank's operational standards.

Database preparation

Databases provisioned and prepared to support the AI workloads inside the bank.

Network connections

Connectivity established with the bank's internal network, fully on-premise.

LLM Use Case Deployment

RAG Use Case

Smartera 3S delivers one practical banking AI use case directly inside the bank — a Retrieval-Augmented Generation (RAG) assistant that answers natural-language questions by retrieving grounded information from the bank’s own knowledge sources. The use case is shaped around banking realities — policies, procedures, product manuals, regulatory references and operational knowledge — so the bank starts seeing value from day one rather than from a pilot that never ships.

  • Grounded answers sourced from the bank's own documents
  • Banking-tailored — policies, products, procedures, regulations
  • On-premise deployment — no data leaves the bank's perimeter
  • Operational handover with documentation and runbook

Technical Enablement

Maximizing Business Value from AI​

For executives, business owners, product and strategy teams — focused on translating AI capabilities into measurable banking outcomes.

  • AI use-case discovery & prioritization
  • ROI, KPIs and value realization
  • Operating model & governance
  • Strategic roadmap for AI adoption

Developing & Operating GenAI

For data, engineering and infrastructure teams — focused on building, customizing and operating Generative AI internally.

  • Agentic orchestration & tooling
  • MLOps, monitoring & security
  • Use-case development lifecycle