As artificial intelligence transitions from experimental pilots to production-grade systems, enterprises face a critical challenge: how to accelerate software delivery without sacrificing quality, compliance, or security. Sanjeev Azad, CTO for APAC and Global Innovation Head at GlobalLogic, is addressing this head-on with VelocityAI—an AI-powered platform that’s achieving 20-70% faster development cycles across heavily regulated industries like healthcare, finance, and telecom. In this conversation, Azad reveals how VelocityAI’s Context-Aware Knowledge Engine breaks down traditional development silos, why “Compliance-as-Code” is becoming non-negotiable, and how the rise of agentic AI is transforming engineers from coders into “architects of intent.” With enterprises racing to operationalize AI at scale, Azad offers a technical roadmap for the next evolution of software engineering.
CTO (APAC) & Global Innovation Head
GlobalLogic
CIO&Leader: Enterprise AI is moving from pilots to production. How does VelocityAI technically accelerate the software lifecycle from design and build to test and deploy compared to traditional methods?
Sanjeev Azad: VelocityAI stands apart by transforming SDLC into a connected, persona-centric journey rather than a series of isolated steps. Each persona, from UX Designer to SRE, is empowered to think, create, and collaborate in their natural context through intelligent assistants, including various utilities and tools under the VelocityAI toolchain. These modules are seamlessly connected through the Context-Aware Knowledge Engine (CAKE.AI), ensuring that every insight, artefact, and decision flows intelligently across the lifecycle.
This orchestration creates automation with continuity, where requirements inform design, design guides development, and test results feed back into optimization. VelocityAI doesn’t just automate tasks; it connects intent to outcome, ensuring alignment, traceability, and quality at every step.
Unlike traditional siloed methods that rely on manual handoffs, VelocityAI integrates GenAI-driven reasoning, knowledge reuse, and observability into a unified ecosystem. The result is an SDLC that thinks collectively across personas, achieves 30–40% productivity gains, and continuously improves with every iteration.
In essence, VelocityAI enables enterprises to deliver better products faster, through an intelligent, connected, and self-learning SDLC that harmonizes human creativity with AI-driven automation.
CIO&Leader: With enterprises already using platforms from big tech providers, what are the core architectural or API-level advantages of VelocityAI that enable smooth integration without creating silos?
Sanjeev Azad: VelocityAI is an open, modular, and interoperable AI Studio designed to plug into existing enterprise toolchains rather than replace them. Its microservices-based architecture and standardized REST/GraphQL APIs ensure seamless integration across platforms like Jira, Confluence, Azure DevOps, Jenkins, Figma, GitHub, and Terraform, enabling enterprises to enhance existing workflows without disruption.
At the core lies CAKE.AI (Context-Aware Knowledge Engine), a dynamic layer that bridges gaps between tools and personas by maintaining context continuity across design, development, testing, and deployment. It synchronizes artefacts and insights from multiple sources, ensuring every persona from Product Owner to DevOps Engineer works with the same unified context.
VelocityAI’s BYO-LLM architecture enables enterprises to choose from OpenAI, Azure OpenAI, Anthropic, or local Ollama models, while preserving data governance and compliance. Its event-driven integration framework (using webhooks, Kafka/EventGrid) supports real-time automation across CI/CD pipelines.
This combination of API-first design, model-agnostic flexibility, and context-aware orchestration makes VelocityAI an actual enterprise-grade fabric—enhancing productivity, breaking silos, and connecting human and AI intelligence through a unified, intelligent SDLC ecosystem.
CIO&Leader: In domains like healthcare, finance, and telecom, what engineering breakthroughs or optimizations within VelocityAI have enabled 20–70% faster product development cycles?
Sanjeev Azad: VelocityAI delivers 20–70% faster product development cycles across healthcare, finance, and telecom through a fusion of engineering intelligence, automation, and compliance-driven design. Its Context-Aware Knowledge Engine (CAKE.AI) maintains regulatory and domain context across personas, reducing documentation overhead and eliminating handoffs.
Through our experience-powered tools, architecture blueprints are auto-generated and validated against domain and compliance models, while developer IDEs (integrated development environments) accelerate coding, refactoring, and modernization with built-in quality and performance optimization. Our STLC toolchain introduces zero-touch QA, automatically generating test suites from requirements and NFRs for faster, traceable validation.
A key breakthrough is the newly added Compliance-as-Code capability. VelocityAI dynamically interprets industry frameworks such as HIPAA, PCI-DSS, ISO 27001, and TMForum standards, and converts them into embedded quality and security rules that run throughout the entire SDLC. This ensures every artefact, from architecture to deployment, is continuously validated for compliance.
Together with DevSecOps intelligent pipelines and insightful dashboards for CI/CD observability and predictive quality metrics, VelocityAI creates a self-learning, adaptive engineering ecosystem. It connects GenAI, automation, and governance to help enterprises deliver faster, compliant, and higher-quality products, turning SDLC into a brilliant, regulation-aware delivery engine.
CIO&Leader: Generative and Agentic AI promise higher autonomy in enterprise systems. From an engineering standpoint, how do you embed governance, explainability, and privacy controls directly into these models?
Sanjeev Azad: VelocityAI embeds governance, explainability, and privacy-by-design through its integrated Intelli-Bridge capability—built to make AI systems transparent, compliant, and trustworthy. Intelli-Bridge automatically selects the best-fit XAI techniques (LIME, SHAP, Counterfactuals) to generate clear, contextual explanations for diverse personas—business users, auditors, and engineers, enabling them to understand how and why AI decisions are made.
The platform enforces Compliance-as-Code and Policy-as-Code, aligning with HIPAA, PCI-DSS, GDPR, and TMForum standards. Sensitive data is protected via tokenization, encryption, and redaction pipelines, while BYO-LLM ensures deployment within compliant boundaries (cloud or on-prem).
With continuous monitoring through DevSecOps intelligent pipelines and insightful dashboards, VelocityAI tracks bias, drift, and fairness metrics, ensuring real-time remediation.
Together, VelocityAI and Intelli-Bridge transform black-box AI into explainable, auditable, and ethically governed systems, ensuring every AI-driven decision remains transparent, fair, and aligned with the customer’s voice.
CIO&Leader: Looking at the next five years, which deep-tech frontiers, whether quantum-ready algorithms, digital twins for industrial systems, or LLM-driven code automation, do you believe will most disrupt enterprise engineering practices?
Sanjeev Azad: Over the next five years, LLM-driven code and systems automation will be the most transformative force in enterprise engineering. It’s already reshaping the engineer’s role, from coder to architect of intent, while introducing a new paradigm of agentic workflows, where autonomous AI agents code, test, debug, and deploy under human supervision. This evolution signals the rise of Agentic FTEs, human engineers augmented by specialized AI agents representing the skills that extend their capabilities. In parallel, AI-powered digital twins will revolutionize industrial and systems engineering, enabling predictive maintenance, virtual commissioning, and real-time optimization, with LLMs translating simulation data into human insight. Meanwhile, quantum-ready algorithms represent a foundational shift in security, forcing enterprises to migrate to post-quantum cryptography and adopt crypto-agile architectures. Together, these frontiers will drive a connected ecosystem where humans and AI agents co-engineer systems, fast.