Overview
AI Force is HCLTech's flagship service transformation platform, leveraging GenAI to revitalize software engineering, business processes and IT operations.
At its core, AI Force is mapped to people and their workflows, using advanced machine learning algorithms and neural networks to deliver tangible benefits—from enhanced efficiency and productivity to accelerated time-to-market for products and services.
Its unique ability to seamlessly integrate with the existing IT landscape and the tooling in place ensures it's a non-disruptive force multiplier.
Fast Facts
Acceleration in software development
Acceleration in legacy application modernization
Increase in testing speed
Faster issue resolution
Reduction in MTTR
Key Features of AI Force
LLM-agnostic
Plays well with all LLMs and SLMs, both proprietary and open source, including Azure Open AI, Google Gemini, Phi, Llama, etc.
Responsible AI
Governance features include fairness, accountability, data anonymization and security measures to protect sensitive information. This emphasis on ethical AI practices is a competitive differentiator.
Multi-modal governance
Speech recognition input capability to upload voice recordings of application requirement-related content to be able to generate detailed features and user stories
Agentic tech for IT operations
Offers autonomous agents that detect, resolve and learn from IT incidents in real-time. Users can review chat histories, test agent skills and obtain ticket statuses and remediation process documentation.
Prebuilt use cases and recipes
Uses advanced automation and GenAI models to analyze historical ticket data to create resolutions, prioritize test cases, summarize/migrate code and check for security vulnerabilities.
Customization
Fully customizable and extensible, it supports creating custom connectors and developing new use cases on top of the existing platform, making it highly adaptable to unique business requirements.
Telemetry
Track metrics like active users, executed jobs, downloads, published content, tokens used by different language models and job reruns—all configured in the UI as widgets, giving users insights into overall tool usage and performance.
FinOps-friendly
OOTB parameters like prompts, total tokens consumed and dollars spent per job execution are already available and can generate reports for end users. Additional widgets can be designed for increased consumption.
Deployment and consumption models
The solution can function as a standalone system or be integrated into existing tools. It offers API functionality requiring no user interaction for smooth integration and can be deployed on AI-powered PCs for enhanced accessibility and performance in real-time applications.
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TBR met with executives from HCLTech to discuss our AI Force platform, overall business model and current AI/GenAI strategies. The HCLTech team included Apoorv Iyer, EVP and Global Lead, Generative AI Practice; Gopal Ratnam, Vice President, Product Management, Generative AI Products & Platforms; Alan Flower, EVP and Global Head, AI & Cloud Native Labs; and Rohan Kurian Varghese, Senior Vice President, Marketing. This special report reflects that discussion as well as TBR’s ongoing research on and analysis of HCLTech.
Want to know more about HCLTech AI Force? Write to Us.
Frequently Asked Questions about HCLTech AI Force
AI Force is HCLTech's flagship service transformation platform, leveraging GenAI to revitalize software engineering, business processes and IT operations. From software development to support and maintenance, our AI-driven approach reshapes processes for maximum efficiency. It also prioritizes Responsible AI adoption, integrating robust security and governance measures to foster secure innovation and growth at scale.
AI Force stands out with its advanced machine learning algorithms and customizable features tailored to specific business needs. Please read more to gain a deeper understanding of its unique capabilities.
AI Force stands apart for its ‘horses for courses' approach to LLM/SLM selection. Instead of using one model for all client scenarios—as most competitor platforms do—we take the time to understand each client's specific needs and suggest the most appropriate language model. This ensures the optimal solution for the business problem, maximizing performance and driving cost savings. In this way, the model selection is based not on technical specifications alone but on your unique business problem and the context of your data.
This client-centric approach enables us to provide tailored, efficient solutions rather than a one-size-fits-all answer.
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
AI Force excels with practical, AI-driven use cases tailored to different personas and activities, enhancing the sophistication and efficiency of various tasks. It offers a range of functionalities that cater to all stages of the software engineering lifecycle, ensuring smoother workflows and better outcomes.
Read more to know in detail about the different personas and how AI Force addresses their needs
Commercial LLMs supported:
- Azure Open AI
- Google Gemini
- Anthropic Claude on AWS Bedrock
- Meta Llama 3
- IBM Granite
- NVIDIA Nemotron
- Azure Open AI Whisper (Automatic Speech Recognition & Translation)
Open Source LLMs/SLM supported:
- Phi
- Llama
- Code Llama
- Mistral for AI PCs
- Inferencing for Hugging Face LLM
The platform employs a three-pronged data security strategy, including infrastructure, inbound and outbound. Read more about infrastructure, inbound and outbound security
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. Read more in detail.