Tarun sat down with Antoine Tardif to talk all things about Enterprise AI. Read the original interview here.
Tarun Raisoni is the CEO and Co-Founder of Gruve Inc., a company specializing in services and software development in AI, machine learning, and data.
Previously, he co-founded and led Rahi Systems from 2012 until its acquisition by Wesco, where he later served as SVP of Data Center Solutions. Under his leadership, Rahi scaled to over $500 million in annual revenue without external funding. Raisoni was recognized multiple times by Comparably as one of the Top 100 CEOs.
With over two decades of experience in the data center industry, he has introduced emerging technologies across infrastructure, networking, and security. He is also an active seed-stage investor, supporting early startups with funding, strategic guidance, and access to a global network.
Gruve is an enterprise AI services platform that helps businesses move from AI experimentation to full-scale deployment. Its five-step framework covers strategy, data readiness, model development, security, and governance. By using AI agents to automate tasks like CRM migration and security monitoring, Gruve streamlines operations and integrates directly into client systems to deliver scalable, engineering-driven solutions with software-level efficiency.
After leading multiple successful ventures, including Rahi Systems, what motivated you to start Gruve.ai? What specific challenges in enterprise AI did you see that convinced you it was the right time to build again?
After Rahi Systems and other ventures, I took a step back to study AI, sustainability, and enterprise systems at Stanford. What I saw was a repeated pattern: enterprises were investing heavily in AI strategies, but execution was falling short. Everyone wanted to “do AI,” but few could get it into production in a meaningful way. The tooling was immature, integration was expensive, and most efforts stalled in pilot purgatory. It became clear that companies didn’t need more roadmaps or strategy decks; they needed a partner who could roll up their sleeves, embed with internal teams, and deliver real outcomes. Gruve was born to solve for that missing piece: the “how” of enterprise AI.
Most enterprise AI projects struggle to reach production. What are the key reasons behind this widespread failure, and how does Gruve help organizations overcome these challenges?
It comes down to three core blockers: poor data hygiene, disconnected infrastructure, and a lack of accountable execution. Enterprises often don’t have data that’s ready for AI, don’t know how to scale it securely, and don’t have a partner to help them go the distance. Gruve’s five-step framework addresses all of this, from data readiness to governance, and our embedded model ensures we’re building alongside the customer, not handing off a spec sheet and walking away. We don’t get paid unless we deliver impact, and that alignment changes everything.
Many enterprises find themselves stuck in ‘pilot purgatory.’ How is Gruve flipping the script and helping companies go from experimentation to execution?
We flip the script by designing for ROI from day one. Every engagement starts with a well-defined use case and prebuilt AI agents that deliver immediate value, whether it’s automating invoice processing, onboarding employees, or enhancing cybersecurity. From there, we scale iteratively based on what’s working. Because we embed engineers directly into enterprise teams, we’re not just advising, we’re building, deploying, and operationalizing AI in real-world environments. That’s how we move companies from ideas to outcomes in weeks, not quarters.
Gruve takes a hands-on approach by embedding directly with enterprise teams. Why do you believe this execution-first model is more effective than traditional consulting or software delivery?
We’ve found that the best results come from working side by side with enterprise teams. By embedding directly, our engineers can navigate the real-world complexity of enterprise systems, whether that’s managing infrastructure, tuning models, or integrating AI into existing workflows. This approach helps ensure quality, security, and scalability because we’re there through the full lifecycle of the project, not just at the planning stage. It’s a model built on accountability, and it’s what allows us to move from strategy to execution effectively.
What are the key elements of Gruve’s five-step delivery framework, and why is each phase—from data readiness to governance—critical for successful AI implementation?
Our five-step framework includes:
- AI Strategy & Workshop: Align stakeholders, identify opportunities, and set a roadmap for implementation.
- Data Readiness: Assess and prepare enterprise data sources to support effective AI use.
- AI Architecture & Integration: Design scalable infrastructure and integrate with existing systems and vendor ecosystems.
- AI Model Development & Fine-Tuning: Build and optimize models to maximize impact on your business priorities.
- Security, Compliance & Governance: Ensure deployments are enterprise-grade, meeting HIPAA, GDPR, SOX, and FISMA requirements from day one.
Each phase addresses a frequent point of failure. Without data, your models are blind. Without infrastructure, they can’t scale. Without governance, you can’t trust or audit them. Success means treating AI as a full-stack system, not a standalone experiment.
From compute and networking to AI model deployment, Gruve provides full-stack infrastructure services. How do you ensure scalability, performance, and security across this diverse technical landscape?
We built Gruve with scale in mind. Our team has decades of experience managing complex global infrastructure, and we bring that rigor to every AI deployment. From optimizing compute on Cisco Nexus and ACI, to implementing containerized AI environments on Kubernetes, to ensuring zero-trust architectures, our approach is both modular and secure. Every deployment is performance-tested, security-hardened, and ready to scale across hybrid environments.
How does Gruve’s AI infrastructure integrate with legacy systems and modern platforms like Cisco, Snowflake, and Databricks to create a seamless enterprise experience?
Gruve was built with the reality of modern enterprise complexity in mind. Most companies can’t afford to rip out and rebuild core systems, so instead of forcing change, we focus on integration. Our engineering teams specialize in bridging legacy infrastructure with modern platforms like Cisco, Snowflake, and Databricks. We’ve developed prebuilt connectors and automation frameworks that allow AI agents to operate within existing CRMs, ERPs, and data pipelines. This approach helps teams move faster without disrupting what’s already working.
What role do AI agents, co-pilots, and LLM-powered assistants play in Gruve’s offerings, and how are they being applied in real-world enterprise workflows today?
AI agents are a core part of Gruve’s offering. We’ve built 35+ domain-specific agents that plug directly into enterprise systems, handling tasks like insurance verification, password resets, contract parsing, and employee onboarding. These aren’t generic chatbots. They’re tuned, integrated, and governed for real use cases. We also support fine-tuning, RAG pipelines, and ongoing monitoring to ensure agents stay relevant and secure over time.
You’ve called Gruve the “how” layer of enterprise AI. Can you elaborate on that, and how you see your role evolving as AI adoption matures across industries?
Everyone’s talking about the “what” of AI, but few are solving for the “how.” Gruve is that missing layer: the hands-on partner that makes AI work in real business environments. As AI adoption matures, we’ll become the default execution layer for the enterprise—embedding with teams, managing infrastructure, deploying agents, and ensuring governance. It’s not flashy, but it’s essential, and that’s where we thrive.
As both an operator and investor, how has your experience funding and mentoring other startups shaped your leadership at Gruve?
I’ve enjoyed the opportunity to help other entrepreneurs reach their potential and develop new, innovative products and applications for many years, and it’s provided me perspective on my companies and my own lessons learned may help others. Mentorship is a privilege and I strive to provide solid advice and perspective to startups large and small on how to establish their business value and navigate complex growth challenges.
Looking ahead, what’s your long-term vision for Gruve? Do you see it becoming a foundational layer for enterprise AI—much like AWS became for cloud infrastructure?
AI is a massive market, and the services we provide today will continue to evolve as companies are faced with the increasing complexity of agent connectivity, multi-modality and AI governance. Just like AWS provided flexibility to customers who needed to grow through cloud services, Gruve believes our flexible outcome-based approach on services will help enterprises grow their AI capabilities and greatly improve the services they offer to their own customers.
