Interview: Vishal Sharma, CTO of SearchUnify

Why It Matters

By turning knowledge bases into actionable agents, enterprises can cut support costs, improve resolution speed, and mitigate AI‑related compliance risks, accelerating true AI value beyond pilots.

Key Takeaways

• Agentic AI Suite acts as a central nervous system for support workflows
• FRAG retrieves from multiple repositories, improving answer relevance and speed
• Production‑ready connectors, permissions, and audit trails replace retrofitted pilots
• Configurable guardrails let customers set escalation thresholds and tone
• Continuous knowledge‑gap detection turns stale data into automated updates

Pulse Analysis

Enterprises have long struggled with the gap between finding information and acting on it. SearchUnify’s Agentic AI Suite bridges that divide by deploying autonomous agents that not only locate relevant content but also execute multi‑step tasks such as ticket triage, escalation, and resolution. Leveraging a federated retrieval‑augmented generation (FRAG) model, the platform pulls snippets from dozens of siloed systems—CRM, ticketing, knowledge bases—before feeding them to large language models, ensuring answers are both fast and factually grounded. This architecture eliminates the single‑index limitation of traditional RAG, delivering context‑aware responses that reduce escalations and improve first‑contact resolution rates.

The real differentiator lies in SearchUnify’s production‑grade infrastructure. Rather than treating data connectors, permission enforcement, and audit logging as afterthoughts, the suite embeds native role‑based access controls and real‑time data masking at the retrieval layer, guaranteeing that agents only surface content users are authorized to see. Comprehensive audit trails and SOC 2, ISO 27001, GDPR, and HIPAA certifications provide the compliance backbone needed for regulated sectors like finance and healthcare. Configurable guardrails let organizations fine‑tune model confidence thresholds, tone, and escalation rules, turning AI governance from a checkbox exercise into an operational discipline.

For leaders, the interview underscores that AI success is less about model brilliance and more about governance, data readiness, and cultural alignment. Deploying autonomous agents should start with the most friction‑prone processes—knowledge‑gap detection and content creation—so the system itself drives data hygiene. Human‑in‑the‑loop handoffs remain essential for complex or emotionally charged cases, preserving trust while scaling routine interactions. By embedding these practices, enterprises can move beyond pilots, achieve measurable cost savings, and unlock the true potential of AI‑driven support.