Cresta

Cresta is an AI platform for contact centers that provides real-time guidance to agents during live calls, automated quality scoring, and post-call analytics. Founded in 2017 by Zayd Enam and Tim Shi (who did his PhD at Stanford studying human-AI collaboration), Cresta has raised over $200M and counts enterprise customers in financial services, telecom, and retail. The core product addresses two problems: helping agents perform better during calls through real-time suggestions, and helping contact center management understand performance patterns through automated analytics. Pricing is enterprise-only with multi-year contracts.

Cresta was founded in 2017 by Stanford PhD students who were researching human-AI collaboration in professional settings. The founding insight was that contact centers were spending enormous money training agents, writing playbooks, and running QA programs, and that AI could augment those investments by putting the playbook in the agent's ear in real time rather than making it something they had to memorize.

The company took the product to market in 2019 and has raised over $200M from investors including Sequoia and Greylock. CEO Zayd Enam has built it into one of the better-regarded specialized AI platforms in the contact center space, competing with Oracle, Salesforce Service Cloud's AI features, and a set of newer AI-native competitors.

Real-time guidance during calls

The live agent assist is Cresta's most distinctive feature. During a call, the agent sees a side panel with real-time suggestions alongside their existing interface. The suggestions come from a pipeline that:

Transcribes the call in real time as both parties speak, runs the transcript through models trained on your call data and company-specific scripts, detects specific conversational moments that warrant a suggestion (objection, compliance requirement, upsell opportunity, customer frustration), and surfaces relevant content at the right moment with low enough latency that it is actionable during the call.

The suggestions are not generic. They are trained on your company's actual calls, your actual scripts, and your actual performance criteria. A financial services company will have different suggestion content than a retail company. The initial training and calibration process is one of the longer parts of a Cresta deployment, typically taking several months of call data to reach accuracy that agents find genuinely useful rather than distracting.

When it works well, agents report that the suggestions surface content they would have thought of given more time, at the moment they need it. The effect is similar to having a highly experienced colleague whispering useful things in your ear. When it is poorly calibrated, suggestions are irrelevant or appear at the wrong moment, which is actively disruptive. The quality of calibration depends heavily on the quality of the implementation and the volume of good training data.

Automated quality scoring

Traditional contact center QA involves sampling a small percentage of calls (typically 2-5%) and having human reviewers score them against a rubric. The process is slow, coverage is thin, and feedback comes days or weeks after the call.

Cresta's QA automation scores every call against whatever framework you define. You specify the criteria: did the agent follow the compliance script? Did they offer the retention promotion when the customer signaled churn intent? Did they properly capture the issue before jumping to resolution? Cresta evaluates each criterion on 100% of call volume and generates a score automatically.

The coverage improvement is significant. Going from 3% to 100% call scoring changes what you can see as a manager. You can track individual agent performance trends over time with statistical significance rather than anecdotal sampling. You can identify specific skill gaps rather than general impressions. You can measure the impact of training interventions with before-and-after data that actually represents the whole call population.

Post-call automation

After-call work is a meaningful cost in contact centers. Agents spend time writing call summaries, updating CRM records, and categorizing call outcomes. This time adds up: if an agent spends 3-5 minutes on after-call work per call, and handles 40 calls per day, after-call work is 15-30% of their shift.

Cresta automates the after-call workflow: the call summary is generated automatically, CRM fields are populated, call outcomes are categorized, and the agent reviews and approves rather than writing from scratch. The time savings are real and measurable in production deployments.

What Cresta costs and who should buy it

Cresta does not publish pricing. Enterprise-only deals, multi-year contracts, pricing structured around seat count and feature bundle. Typical first-year total commitment for a meaningful deployment, including implementation, is likely in the $500K-$2M range for large contact centers. That is a significant number, and the ROI case needs to be built carefully.

The ROI model for Cresta typically includes: reduced agent handle time from better first-call resolution, reduced after-call work time from automation, reduced QA labor from automated scoring, and improvement in key performance metrics like CSAT, retention rate, and conversion rate. The time to see measurable ROI is typically 6-12 months after go-live, once the models are well-calibrated and the operational changes driven by new performance data have taken hold.

Organizations evaluating Cresta should build the ROI model with their own call volume, labor costs, and current performance metrics, then test the projections against Cresta's customer case studies, which are representative of real deployments rather than cherry-picked outliers.

Cresta vs the alternatives

The contact center AI market has several credible players in 2026. Observe.AI focuses more on QA and conversation intelligence with strong coaching workflow features. Talkdesk has AI features built into its CCaaS platform. NICE has its own AI layer built into NICE CXone. Salesforce Service Cloud Einstein covers some of the same territory for Salesforce-based operations.

Cresta's differentiator is the real-time guidance depth. Some competitors do one or the other well; real-time guidance during live calls and post-call analytics in a single platform is Cresta's positioning. The other differentiator is model specialization: Cresta's models are specifically trained on contact center conversations rather than general language models fine-tuned for the use case. Whether that specialization matters enough to justify the premium over lighter-weight tools depends on the complexity of your call types and the scale of your operation.

For organizations already on a major CCaaS platform with built-in AI, the question is whether Cresta's depth justifies its cost and implementation complexity over the AI features they already have. For organizations with no AI investment in their contact center and significant scale, Cresta is a credible option that should be evaluated alongside Observe.AI and the AI features of their telephony platform.

Key features

• Real-time agent assist showing next-best-action suggestions during live calls
• Automatic call scoring against custom quality frameworks
• Coach-on-call feature that flags coaching moments to supervisors in real time
• Conversation intelligence with topic tracking and sentiment analysis
• Automated after-call work including call summaries and CRM note generation
• Specialized models trained on contact center conversation data
• Agent performance analytics with individual and team-level coaching dashboards

Pros and cons

Who is Cresta for?

• Large contact centers improving rep performance and consistency at scale
• Financial services contact centers with strict compliance and script requirements
• Retail and e-commerce service organizations handling high-volume order and returns calls

Alternatives to Cresta

If Cresta isn't quite the right fit, the closest alternatives are observe-ai , talkdesk-ai , ada-cx , sierra-ai , and decagon-ai . See our full Cresta alternatives page for side-by-side comparisons.