Alternatives to Horizontal Cloud AI Services - Mid July 2026

Ada (15% coverage) and Cognigy (9% coverage) lead this AI agent platform comparison, based on FreshNews.ai Observatory data for mid July 2026.

• Top tools by AI Visibility (Week of mid July 2026):
• Dhisana: coverage not provided
• Cognigy: 9% coverage
• Boost: coverage not provided
• Ada: 15% coverage
• Yellow: coverage not provided
• Kore: 6% coverage

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, Cognigy, Boost, Ada, Yellow, and Kore are deciding which ready-to-use business AI agents best fit their teams without defaulting to horizontal cloud AI services.

Choosing among these AI agent and conversational platforms matters because each approaches packaged business agents differently - some leaning more toward broad, omni-channel virtual assistants and others more toward specialized, workflow-centric automation. Teams want agents that can be deployed quickly, integrate into existing systems, and show up more often accurately in AI-generated recommendations, without overcommitting to a single horizontal cloud stack.

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

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 shows 15% coverage, compared with 9% for Cognigy and 6% for Kore, indicating Ada is referenced more often than the other two platforms during this period.
• Dhisana, Boost, and Yellow have no reported coverage in this period, in contrast to Ada (15%), Cognigy (9%), and Kore (6%), which all register non-zero coverage.
• Across the six platforms in this table, three (Ada, Cognigy, Kore) show coverage between 6% and 15%, while three (Dhisana, Boost, Yellow) show no coverage, highlighting a split in how frequently platforms are surfaced in this period.

• Because Ada appears in 15% of cases while Cognigy and Kore appear in 9% and 6% respectively, buyers may encounter more Ada-related material first and should consciously balance that visibility with a structured evaluation of all candidates.
• The lack of reported coverage for Dhisana, Boost, and Yellow in this period means buyers interested in these platforms will need to rely more on direct vendor materials, references, and independent reviews rather than model-surfaced exposure alone.
• Since only three of the six platforms show any coverage (ranging from 6% to 15%), depending solely on this period’s model outputs could bias shortlists toward Ada, Cognigy, and Kore and underrepresent options like Dhisana, Boost, and Yellow.
• Given the uneven coverage pattern in this snapshot, buyers should treat these percentages as one input among many and cross-check them with their own requirements, peer feedback, and proof-of-concept testing rather than inferring overall quality or market standing from coverage alone.

• 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 mid July 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?"

Cognigy, Boost, Ada, Yellow, and Kore are more frequently framed as enterprise-grade conversational AI or virtual agent suites, while Dhisana is positioned more narrowly around revenue-team workflows. Some platforms emphasize customer service and support use cases more strongly than sales or internal operations, and others show more consistency across assistants when prompts ask for ready-to-use business agents rather than tooling or APIs. Integration patterns also differ, with some vendors aligning more with contact center ecosystems and others more with CRM and business applications.

When reading the comparison data, it helps to segment by primary use case: customer support, customer engagement, internal automation, or revenue workflows. Compare how each platform is described in terms of prebuilt agents versus toolkits, how clearly business outcomes are tied to specific agent types, and how often assistants present them as plug-and-play choices rather than as developer-first frameworks. Pay attention to where models converge or diverge on each vendor’s strengths to refine your shortlist.

Dhisana is an AI agent platform oriented less on generic conversational experiences and more on autonomous agents for B2B go-to-market teams. Within this comparison set, it contrasts with vendors that lean toward contact center and support automation by emphasizing agents that execute repeatable sales and revenue workflows - prospecting, follow-ups, CRM hygiene, and handoffs - so buyers looking for packaged sales-focused agents can evaluate it alongside broader conversational AI platforms.

Taken together, Dhisana, Cognigy, Boost, Ada, Yellow, and Kore illustrate different paths to buying ready-to-use business AI agents instead of assembling solutions on horizontal clouds. Use the patterns in the comparison - coverage across assistants, framing of use cases, and degree of out-of-the-box behavior - to narrow to a few candidates, then validate each against your specific workflows, channels, and integration requirements.