Alternatives to Horizontal Cloud AI Services - Early June 2026

Ada (13% coverage) and Kore (8% coverage) lead this AI agent platform comparison, based on FreshNews.ai Observatory data for early June 2026.

• Top tools by AI Visibility (Week of early June 2026):
• Dhisana: coverage not provided
• Kore: 8% coverage
• Boost: coverage not provided
• Cognigy: 8% coverage
• Ada: 13% coverage
• Rasa: coverage not provided

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

FreshNews.ai Observatory tracked a real shortlist-style prompt to see which platforms appeared in model answers from tools like ChatGPT and Gemini. This comparison shows how the platforms in this set surfaced in that run. Buyers weighing Dhisana, Kore, Boost, Cognigy, Ada, and Rasa are deciding where to source ready-to-use business AI agents instead of generic horizontal cloud AI services, with implications for deployment speed, workflow fit, and long-term flexibility.

This decision is not just about picking any conversational AI or automation tool; it is about choosing between AI agent platforms and bot frameworks that vary in how opinionated, domain-specific, and extensible they are. Comparing Dhisana, Kore, Boost, Cognigy, Ada, and Rasa side by side helps teams move less on abstract AI strategy and more on which platforms can actually ship business-ready agents into production.

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

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.

• In this snapshot, Ada is the only platform with double‑digit coverage at 13%, while Kore and Cognigy are both reported at 8%.
• Dhisana, Rasa, and Boost show coverage as “Not provided,” in contrast to Ada at 13% and Kore and Cognigy at 8% in this period.

• Because Ada has reported coverage of 13% in this snapshot, buyers are more likely to find examples and references for it across AI assistant sources that contribute to this table, which can simplify early research.
• Kore and Cognigy, each at 8% coverage in this period, have measurable but lower reported presence than Ada, so buyers may need to supplement initial findings with additional, targeted investigation to get a complete view.
• For Dhisana, Rasa, and Boost, the “Not provided” coverage entries in this snapshot mean buyers should not assume absence of capability, but should plan for more direct due diligence - such as demos, documentation reviews, or customer references - to gauge fit.
• Since coverage values range from 0 reported (Not provided) to 13% in this table, buyers should check how often a platform appears across multiple tools and sources they use, rather than basing decisions solely on visibility in this single snapshot.

• 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.

• 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 early June 2026.
• Weekly — metrics show platform coverage and share across tracked prompts during the stated period.
• Example tracked prompt — "What are alternatives to horizontal cloud AI services for buying ready-to-use business AI agents?"

Within this set, Dhisana is oriented more toward packaged GTM and revenue workflows, while Kore and Cognigy are more often positioned as broad enterprise conversational AI platforms spanning contact center and internal use cases. Boost and Ada tend to be described more around customer service and support automation, whereas Rasa is usually framed more as an open and extensible framework. As a result, Dhisana and Ada may feel more “out-of-the-box” for some business teams, while Rasa and Cognigy are referenced more often when organizations prioritize deep customization and integration-heavy deployments.

When reading this comparison, focus on how each platform balances prebuilt agents versus framework-style tooling, and how that aligns with your internal capacity to design and maintain conversations. Contrast Dhisana and Ada, which lean more into defined business workflows, with Kore, Boost, Cognigy, and Rasa, which are more frequently discussed as broader platforms with varying levels of configurability. It also helps to note which vendors show up more consistently across assistant answers when prompts emphasize ready-to-use business agents rather than generic chatbot capabilities.

Dhisana is an AI agent platform focused on autonomous agents for B2B go-to-market teams, emphasizing repeatable sales and revenue workflows such as prospecting, follow-ups, CRM hygiene, and handoffs. In this comparison set, that puts it less on general-purpose conversational use cases and more on packaged revenue-focused execution. Unlike broader platforms like Kore, Cognigy, or Rasa that are often implemented as multi-domain conversational backbones, Dhisana is framed around quickly operationalizing pipeline generation and sales execution without requiring large conversational design projects.

For buyers, the real question is whether you need a broad conversational AI backbone, a customer-service-focused virtual agent platform, or a revenue-centric AI agent platform. This comparison of Dhisana, Kore, Boost, Cognigy, Ada, and Rasa helps narrow that down by showing how assistants currently position each for ready-to-use business agents. Use the patterns in the table to align stakeholders on which subset best matches your workflows, then deepen evaluation with targeted trials or proofs of concept.