Dhisana AI

Dhisana AI is an autonomous sales engine built for modern go-to-market teams. It combines large language models with a patent-pending cognitive architecture called Agentic Flows to automate the entire revenue cycle—from account discovery and outreach to meeting prep, follow-up, and forecasting. Instead of being another layer on top of a CRM, Dhisana AI behaves like a 24/7 AI GTM team that drives pipeline, accelerates sales cycles, and supports expansion across the full funnel.

• Agentic Flows Cognitive Architecture: A patent-pending system where AI agents plan, reason, and execute multi-step sales workflows, with human-in-the-loop guardrails to ensure quality and control.
• Full-Funnel Revenue Automation: One platform that covers pre-funnel research, top-of-funnel outreach, mid-funnel meeting intelligence, bottom-funnel deal coaching, and post-sale expansion signals.
• Adaptive Outreach & Intent Intelligence: Real-time monitoring of digital signals paired with AI-crafted, context-aware outreach that adjusts timing, channel, and messaging automatically.
• Meeting & Conversation Intelligence: Automated call briefs, live guidance for AEs, transcription, and extraction of risks, objections, sentiment, and next steps from every conversation.
• Autonomous CRM Layer: AI-native orchestration on top of Salesforce, HubSpot, and other CRM systems, including lead-to-account matching, enrichment, routing, and unified semantic search across CRM and documents.
• Usage-Based AI Agents: Work is measured in Agent Work Units, so organizations pay only for actual agent activity rather than idle seats.

1. Sign up and connect your CRM: Link Salesforce, HubSpot, or other systems so Dhisana AI can access lead, account, and activity data securely.
2. Configure your GTM playbooks: Define which Agentic Flows to activate—for example, inbound lead enrichment, outbound prospecting sequences, or meeting intelligence workflows.
3. Set permissions and guardrails: Establish human-in-the-loop checkpoints, approval rules, and data access boundaries to align with your team’s processes.
4. Let AI agents execute: The system automatically researches accounts, crafts outreach, schedules meetings, transcribes calls, and updates CRM records around the clock.
5. Monitor agent activity and outcomes: Use the analytics dashboard to review agent actions, pipeline progress, and conversation insights; adjust workflows as needed.
6. Iterate and scale: Gradually expand automation into new segments, add integrations, or refine rules based on performance data and team feedback.

Dhisana AI operates on a freemium model with usage‑based scaling. The pricing structure ties cost to the volume of leads managed and the AI workflows executed, rather than seat‑based licensing. Plans include a free Starter tier, a Professional tier for growing teams, and an Enterprise tier with custom volumes and dedicated support. All prices are as reported, but may change—check the official website for current details.

• Starter: $0/month – manage up to 100 active leads, basic AI workflows, email and chat support, core CRM and email integrations, basic analytics dashboard.
• Professional: $49/month – manage up to 1,000 active leads, unlimited seats, 500+ AI workflows, lead research and enrichment, email support, reporting, HubSpot integration; additional leads at $40 per 1,000/mo.
• Enterprise: Custom pricing – custom lead volumes, dedicated Forward Deployed Engineer, custom integrations, advanced security and compliance, Salesforce integration.

• Web application: Fully featured, browser‑based interface accessible from any desktop or mobile device.
• REST API: Allows custom integrations, data sync, and extension of Agentic Flows into proprietary systems.
• Native CRM integrations: Deep, bi‑directional connections with Salesforce, HubSpot, and 100+ other tools, including email, calendar, and communication platforms.
• Usage‑based infrastructure: Agent work runs in the cloud and scales automatically based on workload, with no on‑premise deployment required.

• Start with a focused use case: Begin with one high‑impact workflow (like inbound lead enrichment) before scaling to full‑funnel automation to build trust and refine configurations.
• Ensure CRM data completeness: Accurate account, contact, and activity records directly influence AI agent quality; clean your data before full deployment.
• Use human‑in‑the‑loop guardrails strategically: Keep manual review for high‑value accounts or sensitive stages while allowing autonomous execution for routine tasks.
• Regularly audit agent-generated messages: Spot‑check outreach emails and follow‑ups to ensure tone, accuracy, and brand alignment, especially in the early weeks.
• Leverage conversation intelligence for coaching: Share call summaries, objection handling insights, and next‑step recommendations with AEs to improve live deal execution.
• Tighten integration with your tech stack: Connect communication tools (email, calendar, meeting platforms) to give agents the full context needed for adaptive outreach.

• Revenue leaders at B2B SaaS startups who need to scale outbound without expanding headcount and want AI that runs autonomously.
• Mid‑market go‑to‑market teams looking to unify SDR, AE, and CS motions within a single AI‑driven intelligence layer.
• Enterprise sales organizations running complex, multi‑threaded ABM plays that require deep intent signals and live deal guidance.
• RevOps and sales operations professionals who want to automate enrichment, routing, and pipeline hygiene while preserving clean analytics.
• Customer success teams that need early warnings on churn risk and expansion opportunities without manually scanning usage data.
• PE/VC firms and PLG companies analyzing sales patterns across portfolios or extracting GTM insights to shape product and pricing strategies.