Has AI Force already been deployed to HCLTech clients?

Yes, AI Force is currently deployed and active for multiple HCLTech clients, with case studies available.

Are there prerequisites and infrastructure requirements for deploying AI Force?

AI Force has the following infrastructure requirements for its deployment:

• Hardware
  • RAM: 32 GB (recommended)
  • OS: Windows 10/ Windows Server 19+ (Linux)
  • OS Arch: 64-bit operating system
  • HDD: 100 GB (minimum), 250GB (recommended)
• Software
  • Python 3.11.0
  • Postgres 14.10.1
  • OpenSearch 2.11.1

AI Force TS:

• Hardware
  • RAM: 32 GB (recommended)
  • OS: Windows 10/ Windows Server 2022+
  • OS Arch: 64-bit operating system
  • HDD: 100 GB (minimum), 250GB (recommended)
• Software
  • Python 3.12
  • OpenSearch 2.15
  • JAVA 22
  • 1.26

What costs are associated with using AI Force?

There are three components to AI Force’s cost:

• License – Per-seat cost for AI Force usage
• Professional Services – Cost associated with customization for a specific client use case
• LLM Consumption

What deployment options are available for AI Force?

HCLTech offers four AI Force deployment/consumption models:

• Stand-alone deployment: Deployed independently as a self-contained solution
• Embedded into users' existing tools: Integrated directly into IDEs, testing tools, browsers, ticketing systems, etc.
• Through APIs ("headless" model): AI Force operates behind the scenes, providing functionality via APIs without direct user interaction
• On the edge via AI-powered PCs: Deployed on edge devices like AI-powered PCs, enabling localized processing

It is consumed as a product for enterprise-wide use or as part of our managed services. We implement AI Force from the outset for large-scale engagements, ensuring immediate cost savings and efficiency benefits for our clients.

For clients already engaged with us for managed IT services, AI Force can be integrated to enhance cost savings, productivity boost and operational efficiencies. This complement to our existing managed services allows us to offer innovative, AI-powered solutions that shift our role from basic IT services to more consultative, outcome-driven and AI-enabled engagements.

During the discovery phase, we conduct value stream mapping, including creating detailed "as-is" and "to-be" pictures and assessing the potential impact at scale. This due diligence helps clients identify the right projects where AI Force can bring the most value.

When AI Force is first deployed, it ingests data through two primary approaches: source system connectors and file uploads. The source system connectors allow AI Force to pull data directly from existing systems, including requirements, user stories, source code, coding guidelines, test plans, test cases, test scripts, API documents, knowledge base (KB) articles and tickets/bugs/defects. Alternatively, clients can upload files containing relevant datasets for ingestion.

What architecture is used for AI Force deployment on Azure Cloud?

See the image below for AI Force deployment on Azure Cloud. Note that Kubernetes handle multi-node deployments.