Real-time AI agent assist plus conversation intelligence for enterprise contact centers.
The strongest real-time agent-assist platform in the enterprise contact-center category โ but only worth evaluating if you already operate a CCaaS-grade contact center and have the data + change-management muscle for a multi-quarter rollout.
Last verified: April 2026
Sweet spot: a Fortune-1000 contact center with >200 agents, a clear playbook the team wants enforced, and an operations leader who has authority to drive a multi-quarter AI rollout. At that scale, the real-time intervention layer pays back via shorter handle time, higher conversion, and offloaded QA โ and Cresta's custom-tuned models genuinely outperform generic LLM wrappers because they've been fitted to your own corpus. Failure modes to know about. First, expectations: Cresta is not a magic install โ model quality climbs over months as the system ingests your conversation data, and teams that expect ChatGPT-style results in week one will be disappointed. Second, change management eats deployments alive: agents resist whisper prompts unless coaching, incentives, and QA are aligned around using them. Third, integration depth varies โ Salesforce + Genesys customers get a smooth path; teams on niche or homegrown CCaaS stacks should budget significant services time. Fourth, real-time AI hallucination risk is real on knowledge-grounded answers; treat the Knowledge Agent as a first-draft tool with human verification, not a source of truth. What to pilot before committing. Run a 90-day pilot on one queue (ideally inbound sales, where intervention has the clearest revenue signal). Measure handle time, conversion rate, QA score variance, and rep adoption of suggestions โ that last one is the leading indicator. If reps voluntarily use suggestions >40% of the time and conversion moves >5%, the technology is working in your environment. If adoption stays under 20%, the issue is almost always change management, not the AI โ fix that before scaling.
Cresta is a contact-center AI platform built for sales and customer-experience teams at enterprise scale. The core product is a layered stack: real-time Agent Assist that whispers next-best actions and approved language to a live agent mid-call, a Knowledge Agent that surfaces grounded answers from internal documentation, an autonomous AI Agent for full deflection, and a Conversation Intelligence layer that mines every interaction for coaching signals, win-loss patterns, and quality scores. Where Gong and Chorus focus on post-call analytics for B2B sales reps, Cresta focuses on in-call intervention for high-volume operations โ airlines, telco, BPOs, large insurance and lending desks. Its differentiator is the proprietary Cresta-tuned models trained on a customer's own conversation corpus, so the suggestions reflect the playbook the team actually wants reps to follow rather than generic large-model output. Customers include United, Alaska Airlines, Marriott, Hilton, LendingClub, and Propel Holdings. Deployments are typically multi-quarter โ the platform sits inside the contact-center stack alongside Genesys, NICE, or Five9, and integrates with the CRM (Salesforce, MS Dynamics) plus internal knowledge sources. This is firmly an enterprise tool. There is no self-serve tier, pricing is six-figure-and-up annually, and rollout requires conversation data, change-management buy-in, and ongoing model tuning. For organizations that have those resources, the real-time intervention is materially different from after-the-fact call review โ and it's the part of the AI-coaching market that traditional call-recording vendors haven't cracked.
Sales-gated pricing with six-figure floor โ no transparency for SMB buyers. English-first; non-English language coverage exists but is uneven. Custom model tuning requires substantial conversation data (typically months of recordings) before suggestion quality matures. Real-time accuracy varies by domain โ highly scripted flows perform best, complex consultative sales worst. Salesforce + Genesys integrations are deepest; smaller CCaaS stacks may need more services work.
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