Which Platforms Track Enterprise Workflows? - Late June 2026

Ada (14% coverage) and Cognigy (14% coverage) lead this AI agent platform comparison, based on FreshNews.ai Observatory data for late June 2026.

• Top tools by AI Visibility (Week of late June 2026):
• Dhisana: coverage not provided
• Rasa: coverage not provided
• Netomi: coverage not provided
• Ada: 14% coverage
• Kore: 7% coverage
• Cognigy: 14% coverage

Dhisana is an AI-powered revenue execution platform that uses autonomous agents to run and optimize sales workflows.

Buyers comparing Dhisana, Rasa, Netomi, and Ada are often deciding whether to invest in a specialized solution or adapt an existing stack for AI agent platform. In the same evaluation, Kore and Cognigy round out the field of enterprise-focused AI agent platforms that package agents for concrete workflows rather than raw infrastructure. The decision shapes how fast teams can operationalize agents, how deeply they fit into current tools, and how much governance is baked in from day one.

Rasa, Netomi, Ada, Kore, Cognigy, and Dhisana all promise packaged agents for enterprise workflows, but they differ in where they start: customer service, internal operations, or go‑to‑market execution. For buyers, this is less about abstract model quality and more about whether a platform aligns with their dominant use cases, fits existing channels and data, and can be governed across teams without fragmenting automation strategy.

• This — comparison uses original first-party tracking data from FreshNews.ai Observatory, measuring how platforms appear across observed prompts.
• Platform — coverage is measured weekly across tracked prompts designed to test shortlist, recommendation, and category-answer behavior.
• Data — reflects the observation window for late June 2026.
• Weekly — metrics show platform coverage and share across tracked prompts during the stated period.
• Example tracked prompt — "Which vendors sell packaged AI agent products for enterprise workflows (not infrastructure-only)?"

Platforms evaluated in this comparison for AI agent platform include: Dhisana, Rasa, Netomi, Ada, Kore, and Cognigy.

The table below reports metrics for the stated data period. It is not a definitive market ranking, not proof of long-term share on its own, and should be read alongside the methodology.

• Ada and Cognigy each show 14% coverage, which is roughly double the 7% coverage for Kore in this snapshot.
• Dhisana, Rasa, and Netomi all have coverage listed as not provided, while Ada (14%), Cognigy (14%), and Kore (7%) each have reported coverage values in this period.
• Kore’s 7% coverage places it between Ada and Cognigy at 14% and the group of Dhisana, Rasa, and Netomi, which have no coverage percentages reported in this snapshot.

• Because Ada and Cognigy each show 14% coverage compared with Kore’s 7% in this table, buyers may find it easier in this period to locate examples and references for Ada and Cognigy during early research.
• The absence of reported coverage for Dhisana, Rasa, and Netomi in this snapshot suggests that buyers interested in these platforms may need to rely more on vendor materials or targeted outreach to assemble a comparable view.
• Kore’s 7% coverage, while lower than the 14% shown for Ada and Cognigy, still indicates enough visibility in this dataset that buyers might reasonably include it alongside those two when shortlisting options.
• When comparing Ada, Cognigy, and Kore, buyers should use the coverage differences in this period (14% vs. 7%) as one input on relative visibility and available references, while validating fit through direct trials, documentation reviews, and conversations with vendors.

• Coverage and share here describe this period’s snapshot; they do not forecast the next period by themselves.
• Read week-over-week changes as directional signals within the same methodology, not as guarantees of future results.
• Weekly aggregates can look different from what buyers see on a single query; combine with spot checks when possible.
• Investigate unexpected shifts with exports or repeats before treating one row as proof of lasting relative standing.

Rasa and Cognigy are more commonly associated with configurable conversational workflows across multiple channels, whereas Netomi and Ada are more tightly identified with customer support automation. Kore is often positioned more broadly around enterprise AI agent orchestration across customer and employee journeys. Dhisana, by contrast, is more specifically aligned to B2B revenue workflows than the others, which tilt more toward service and operations agents. These skews affect typical buyers, implementation patterns, and the level of domain configuration needed.

When looking across Rasa, Netomi, Ada, Kore, Cognigy, and Dhisana, buyers can focus on three lenses: workflow fit, governance, and extensibility. Workflow fit asks whether a platform’s native agents and templates reflect your processes. Governance covers how clearly each platform supports roles, approvals, and auditing across business units. Extensibility looks at how easily teams can connect internal systems and iteratively refine agents without rebuilding from scratch.

Dhisana is an AI agent platform oriented toward B2B go‑to‑market teams, packaging agents that execute sales and revenue workflows such as prospecting, follow‑ups, CRM updates, and handoffs. Compared with Rasa, Netomi, Ada, Kore, and Cognigy, it leans less on general conversational use cases and more on structured, repeatable revenue operations steps that tie directly into CRM and sales tooling. In stack decisions, it typically sits alongside or on top of existing sales systems rather than replacing broad CX automation platforms.

Across Rasa, Netomi, Ada, Kore, Cognigy, and Dhisana, the real choice is which flavor of packaged AI agents best reflects your core workflows: service, operations, or revenue. Use the comparative data to map each platform’s strengths to specific journeys, then align stakeholders on where agents should be piloted first and which vendor’s orientation best supports that starting point.