CIO&Leader in conversation with Rahul Jha, VP of Cloud, Gen AI & Cybersecurity at Visionet Systems that how AI labs are redefining enterprise innovation by moving beyond prototypes to scalable, production-ready AI solutions tailored to real business needs.
CIO&Leader: What was the core vision behind setting up Visionet’s AI Lab, and how does it stand apart from traditional enterprise innovation labs?
Rahul: When we started the AI Lab two years back, our objective was to integrate enterprise AI into actual business challenges—doing it at scale, with governance, and generating tangible, repeatable outcomes.
Right from day one, genAI studio was imagined not just as an innovation lab, but rather a “POC-to-Production” platform—built to deliver solutions rather than just prototypes. To support this, we invested heavily in building a comprehensive genAI technology fabric. It includes agentic and RAG frameworks, LLMOps, security guardrails, orchestration layers, evaluation and benchmarking tools, and deployment templates for AWS, Azure, and GCP, enabling multi-cloud deployment. All ideas that come to life in our lab are constructed and developed at the intersection of business lines.
By Q1 2025, genAI studio had implemented over 60 genAI solutions across various industry use cases, engineering tools, and productivity platforms. It saw over 1,000 unique users from teams globally, with over 10 million tokens weekly, 8,000 queries daily, and a 90 percent positive feedback rate. These aren’t vanity metrics—they are proof that our lab delivers real, scalable, repeatable innovation, not one-off experiments.
CIO&Leader: In your experience, how can AI labs function as incubators for building customized AI solutions tailored to industry-specific challenges?
Rahul: The secret to transforming an AI Lab into a true incubator lies in domain-centric immersion. Our approach goes beyond just LLMs and APIs; we embed subject matter experts—whether from healthcare, retail, insurance, or IT—directly into the AI solutioning process. These domain experts co-ideate with data scientists and engineers to understand user personas, map real workflows, apply compliance and regulatory playbooks, and work with actual production data. This approach ensures the output is not generic experimentation but scalable, purpose-built solutions. It has helped us build over 60 genAI solutions that address real enterprise pain points.
For example, in cloud lifecycle management, our Cloud Assistant organizes technical documentation, answers infrastructure questions in plain English, and generates SOPs from videos. In healthcare, tools like the Clinical Note Generator and Medical Coding Extractor help reduce physician burnout and improve claims accuracy. In retail, our AI Stylist delivers genAI-powered personal shopping experiences, while Multi-Modal Search enables intuitive discovery through voice, image, or text.
CIO&Leader: How can enterprise AI labs be strategically used to build talent pipelines, especially in emerging fields like genAI and autonomous systems?
Rahul: We run quarterly, cohort-based training programs where analysts, architects, and engineers team up on live client use cases. These hands-on experiences are tied to structured genAI learning paths within the lab. So far, over 500 developers have completed capstone projects using our AI Lab infrastructure.
To further democratize learning, every employee—from HR to Finance to Sales—receives a sandbox account on genAI studio. Here, they can experiment freely: spinning up toy agents, testing LLMs like GPT-4o, Claude 3.7, or LLaMa 3, and building with vector databases or RAG pipelines. This open access has sparked a culture of innovation curiosity, with many non-technical team members becoming certified Citizen AI Developers in under six weeks.
We also foster continuous learning through biweekly “show-and-tell” sessions that highlight standout prototypes. Senior AI Team Leads provide real-time coaching and feedback—whether it’s refining a prompt or improving a RAG setup. High-performing individuals earn the opportunity to present at major external forums like IDC Summits, Microsoft events, and other industry conferences.
Within just a year, over 100 production-grade genAI engineers have emerged from our Lab. We’ve significantly reduced the need for external hiring, and more importantly, built a self-sustaining talent pipeline.
CIO&Leader: How is Visionet using its AI Lab to support the broader developer community, both internally and externally?
Rahul: We believe innovation should be shared—not siloed. Our AI Lab is designed to empower not just our internal teams but the wider developer community. We see ourselves as part of a larger ecosystem, and our goal is to ensure that the rising tide of genAI lifts all boats.
Internally, we’ve built the genAI Studio Portal—a one-click hub that gives developers access to over 100 reusable blueprints, including agents, prompts, UI components, and evaluation scripts. With this, developers can fork a blueprint, tailor it to their specific domain, and deploy it within hours.
Externally, our focus is on community enablement. Through monthly webinars, podcasts, and live workshops, we’ve engaged over 1,000 engineers across North America, Europe, and APAC. We also actively collaborate with startups, academic institutions, and industry consortia on co-creating solutions that address real-world challenges. One such initiative is our partnership with academia—where Visionet introduced a professional elective on Large Language Models (LLMs) for 7th-semester engineering students, bridging the gap between classroom theory and enterprise AI practice.
CIO&Leader: With genAI evolving rapidly, how does Visionet’s AI Lab approach bias mitigation, ethical guardrails, and responsible AI deployment?
Rahul: While many focus solely on building bigger models, we believe the foundations—clean data, fairness checks, and audit trails—are non-negotiable. At Visionet, Responsible AI isn’t a nice-to-have; it’s a must-have.
Our AI Lab is built around an embedded Responsible AI Framework. Every model and query runs through bias detection pipelines designed to flag representational (gender, ethnicity, region) and statistical (exposure, polarity) biases. To tackle hallucinations, we use Retrieval-Augmented Generation (RAG) and curated knowledge-base connectors to ground responses in factual, domain-specific content—particularly critical in sensitive sectors like healthcare and finance.
We’ve implemented multi-layered guardrails to keep genAI deployment safe. Our integrated policy engine automatically flags high-risk prompts and triggers safe completion routines or human-in-the-loop escalation. Before any genAI application goes live, we conduct AI red teaming exercises to uncover potential vulnerabilities and stress-test safeguards.
LLMOps observability dashboards provide ongoing monitoring, tracking token usage, user satisfaction, and quality benchmarks like F1 accuracy. Drift alarms alert us to semantic inconsistencies, prompting timely retraining. Production agents are equipped with escalation protocols, so if a query triggers a red flag—be it bias, toxicity, or an unknown domain—it’s routed to a domain expert or a security reviewer.
Before deployment, every solution undergoes a rigorous, multi-stage review process. A Technical Safety Audit ensures infrastructure isolation, PII redaction, and secure authentication. Domain experts validate business relevance and regulatory alignment. Finally, our Ethical Review Board, which includes legal and data privacy stakeholders, assesses the risk of harm or compliance conflicts.
CIO&Leader: What governance frameworks or checks do you recommend for AI labs aiming to maintain transparency and compliance in fast-moving projects?
Rahul: Our governance model rests on three key pillars.
The first is a comprehensive design and security review. During the Ideation & Screening stage, we assess data sources, misuse risks, and red flags via a lightweight intake document. Next is Prototype & Testing, where our CoE teams build a Minimal Viable Agent (MVA) for closed-loop testing. And the final stage is Pre-Production Audit & Go-Live where a full report is presented to the Governance Council.
The second is continuous LLMOps and observability. This layer ensures continuous monitoring post-deployment. A unified dashboard tracks token usage, latency, hallucination rates, and user satisfaction in real time. Drift alerts are automated—for example, if semantic drift exceeds 5% over a 7-day window, it prompts a review and potential retraining. Every model iteration and user feedback loop is captured via immutable version control logs, enabling full auditability.
And the final pillar is Scalable Governance with Policy engine. We’ve embedded a context-aware policy engine into our genAI studio to ensure governance scales with innovation. This tailors oversight to domain-specific risks, automates enforcement via MLOps pipelines, and supports thousands of use cases without creating bottlenecks.
CIO&Leader: As AI rapidly advances toward multimodal systems and autonomous intelligence, how is Visionet preparing for these paradigm shifts through its lab?
Rahul: The shift from text-only to multimodal AI—and ultimately to autonomous, self-driving intelligence—is not a distant horizon; it’s happening now. We’re actively building for this new paradigm, developing intelligent agents that can see, listen, interpret, and act with increasing independence.
We’re creating multimodal pipelines to power real-world use cases. For instance, in retail, we’ve built vision-language agents that analyze in-store camera feeds and optimize shelf layouts using LLMs. In healthcare, our Medical Imaging Summarizer processes radiology scans, extracts pathology findings, and drafts preliminary notes for clinical review. In contact centers, we’re prototyping Live Transcription + Intent Agents that transcribe audio calls in real time and proactively suggest troubleshooting actions—before the agent even asks.
On the autonomy front, we’re investing in orchestration frameworks that enable intelligent task chaining. We’ve also developed our in-house Visionet AgentVerse which is an enterprise-grade agentic ecosystem with client-server protocols, agent collaboration, and adaptive A2A (agent-to-agent) communication for solving complex, distributed problems.
CIO&Leader: What advice would you offer to CIOs and tech leaders trying to future-proof their AI strategies in this age of accelerated innovation?
Rahul: For CIOs and tech leaders navigating today’s fast-moving AI landscape, the goal shouldn’t be to adopt every shiny new model—it should be to build an adaptive ecosystem. Here are a few principles:
- Build a Composable Platform—Not Point Solutions
- Embed Responsible AI & Governance Early
- Invest in People—Not Just Platforms
- Treat AI as a Living Product
In short, future-proofing AI isn’t about predicting which model will win tomorrow; it’s about creating a platform-and-people ecosystem that can adapt, govern, and scale any innovation wave.