Full-Time
AI Voice Designer
Updated on 9/11/2026
Sierra
501-1,000 employees
AI agents for real-time customer support
No salary listed
London, UK
In Person
 Creative Production (1) |
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- Professional experience in audio, sound, or voice design, such as sound design, audio design, voice or user-experience audio design, or music/audio production.
- Hands-on experience recording and editing speech and audio, directing voice talent, and working in professional studio settings.
- Fluency with audio tooling and digital audio workstations such as Pro Tools, Logic, Ableton, Audition, or iZotope RX.
- Experience with speech synthesis, text-to-speech, voice cloning, or conversational artificial-intelligence voice tooling such as ElevenLabs.
- A portfolio or body of work demonstrating craft and taste in voices, earcons or user-interface sounds, sonic branding, character voices, or similar work.
- Comfort with technical, command-line-adjacent tooling, including writing basic scripts, running command-line interface commands, making configuration or code-level changes, submitting pull requests, and using artificial-intelligence coding agents.
- Comfort working directly with clients to deliver against expectations and timelines.
- Design and implement natural-sounding, production-ready voices for European locales and key customers from initial concept through launch.
- Own the end-to-end experience of how a voice sounds, including deliberate choices about accent, pacing, personality, and prosody.
- Tune voices using Sierra's platform tooling by running command-line prompts and submitting pull requests.
- Source, direct, and record voice actors.
- Cut, clean, and prepare audio files for the voice pipeline while ensuring recordings meet the quality bar for synthesis.
- Work directly with customers to understand locale coverage, latency, telephony constraints, compliance, brand personality, tone, warmth, and register requirements.
- Translate customer and brand requirements into concrete, buildable voice specifications.
- Help define evaluation criteria for voice naturalness, brand fit, intelligibility, and consistency across a conversation.
- Build customer and contractor feedback loops that improve voices over time.
- Employ and manage language contractors with native-language skills who tune voices, review language nuances, and assess quality improvements.
- Build repeatable processes for evaluating voice quality across locales.
- Collate customer feedback and help scope tools, controls, and workflows for the voice platform team to build, launch, and manage voices at scale.
- Partner with the voice platform and Platform teams to implement and tune voices, build self-serve development tools, and inform the platform roadmap.
Sierra.ai builds and deploys conversational AI agents for customer service that handle real-time interactions and take actionable steps within a client’s systems (e.g., CRM, order management) to resolve issues. The agents are always-on, empathetic, and aligned with a client’s brand voice, following strict policies and security procedures. They operate deterministically, with built-in quality assurance that reveals the reasoning behind each interaction, ensuring transparency. The platform is subscription-based and scales to large client ecosystems, serving brands like Sonos, SiriusXM, and WeightWatchers, with a reported CSAT of 4.6/5 and a 70% resolution rate. Sierra.ai emphasizes data governance, using client data only to train models and securing it with industry-standard practices. Its goal is to improve customer experience by delivering real-time, automated support that can understand, resolve, and learn from interactions while remaining controllable and auditable.
Company Size
501-1,000
Company Stage
Series E
Total Funding
$1.6B
Headquarters
San Francisco, California
Founded
2023
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Simplify's Take
What believers are saying
- Sierra raised $950 million on May 4, 2026, valuing it near $15 billion.
- CarMax said August 6, 2026, Sierra improved call resolution and reduced unresolved calls.
- SoftBank made Sierra its exclusive Japan partner on July 13, 2026.
What critics are saying
- Salesforce Agentforce and Intercom Fin pressure Sierra’s enterprise budgets and CIO attention.
- Hyper-τ-bench showed automated builders at 23.9%, exposing fragile agent creation economics.
- If customers demand cheaper per-resolution pricing, Sierra’s outcome model compresses margins fast.
What makes Sierra unique
- Sierra’s Horizon agents handle long-running customer workflows across weeks, not chats.
- Sierra prices on outcomes, aligning revenue with customer results and adoption.
- Sierra’s 2026 Persona and Voice stack tightly controls brand tone across markets.
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Benefits
Unlimited Paid Time Off
Medical, Dental, and Vision benefits for you and your family
Life Insurance
Disability Insurance
401(k) Company Match
Parental Leave
Fertility Treatment Support
Discretionary Benefit Stipend
Growth & Insights and Company News
6 month growth
↑ 20%1 year growth
↑ 4%2 year growth
↑ 20%Figure Technology Solutions has partnered with Sierra to deploy AI agents that help recover abandoned home equity loan applications. The collaboration uses Sierra's Horizon platform to tackle a significant industry problem: only 49% of home equity applications reach closing, according to the Mortgage Bankers Association. The AI agent autonomously contacts stalled applicants via voice and SMS over several days, helping them navigate friction points like credit checks and ID verification before transferring them to human loan officers. Early results show borrowers who engaged with the agent funded 67% more loan volume. When combined with loan officers, the system achieved a 143% lift in funded loan conversion compared to loan officers working alone. This marks the first US deployment of Sierra's Horizon platform, which enables agents to handle complex, revenue-generating tasks. Figure plans to roll out the integration to network partners in coming months.
Sierra releases hyper-τ-bench as Open Source: A Benchmark for Agent development - Unite.AI. Sierra unveils open-source hyper-τ-bench for evaluating AI Agent Construction. On September 8, 2026, Sierra announced the open-sourcing of hyper-τ-bench, a groundbreaking benchmark designed to assess how effectively AI coding agents can create functioning customer service agents. Sierra reported that the top-performing automated setup successfully completed 23.9% of evaluation tasks, compared to an impressive 82.2% achieved by a combination of an engineer and a leading-edge model. From AI agent functionality to AI agent creation. Originally developed in 2024, Sierra's τ-bench aimed to tackle the question of whether an AI model could reliably perform as a customer service agent. As this capability has now become standard, Sierra highlights a more complex challenge: determining who builds the agent in the first place - a task increasingly handled by the models themselves. While collaborating with companies to deploy customer service solutions, Sierra characterizes this work as research rather than straightforward implementation, facing scattered requirements across diverse sources such as manuals, support channels, and frontline expertise. Teams must form hypotheses, collect data, and conduct experiments to identify the variables that genuinely enhance performance. The benchmark, formally referred to as τ^τ-bench (pronounced hyper-tau-bench), is detailed in a 41-page paper authored by Quan Shi, Keshav Dhandhania, Karthik Narasimhan, and Victor Barres, which was submitted to arXiv on September 4, 2026. The codebase is available under the MIT license, accompanied by a public leaderboard. The paper's abstract notes that LLM agents are increasingly utilized for customer service and internal operations, while the responsibility for crafting these agents is shifting to coding agents. Existing benchmarks, they argue, offer little insight into whether an AI system can produce a functional agent in real customer engagement scenarios. Understanding hyper-τ-bench. The hyper-τ-bench framework places a developer agent within a controlled workspace featuring the records of a simulated company and a client it can message. Within this environment, the developer oversees the engagement from start to finish, reconstructing specifications, designing architectures, and translating business actions into operational tools, all while iterating until a viable customer service agent is created. The client's REST API may present subtle defects, requiring the developer to determine whether issues arise from the specifications or the code. The finalized agent must operate within a predetermined menu of models and adhere to a budget for each conversation, ultimately facing simulated production traffic assessed by rigorous τ-bench-style tests that remain concealed from the developer during the construction phase. This closely mirrors the conditions of a genuine engagement, incorporating the actual records a business maintains, client requirements, and operational constraints. The repository documentation describes τ^τ-bench as an overarching loop surrounding Sierra's τ[3]-bench, which measures a conversational agent's performance against simulated users. In the outer loop, a coding agent - the Developer - works in a sandboxed environment, optionally interacting with the simulated client and submitting a fully functional agent. The Developer's effectiveness is gauged by the agent's success rate on held-out customer service tasks evaluated through the τ[3]-bench inner loop. Evidence provided in the sandbox includes policy documents, support transcripts, call recordings, screenshots, flowcharts, and a client REST API. The release includes 53 tasks across four sectors: six tasks each for airlineplus, retailplus, telecom, and 35 tasks in bankingknowledge. The documentation defines airlineplus as a fictional Meridian Airlines covering aspects such as flight booking and cancellations; retailplus as order servicing, including exchanges; telecom as technical support; and bankingknowledge encompassing retail banking activities like card management and transfers. It's worth noting that airlineplus and retailplus are reimagined versions of their τ[3]-bench counterparts, preventing the transfer of memorized policies and ensuring that the originals remain unchanged for comparison. Performance insights across six configurations. Sierra's analysis of six automated developer configurations revealed performance on a spectrum from 14.9% to 23.9% on evaluation tasks, with the best-performing setup - Claude Opus 5 with maximum reasoning in Claude Code - achieving 23.9%. Following that was Codex using GPT-5.6-sol at high reasoning effort at 22.0%, then Codex with GPT-5.6-terra at 18.0%, OpenCode with Kimi K3 at 17.9%, Kimi Code with Kimi K3 at 16.1%, and Claude Code with Claude Sonnet 5 at 14.9%. In contrast, the human-plus-AI benchmark - a seasoned engineer paired with an equivalent model - achieved an impressive 82.2% on the same tasks. Average time spent on builds varied, with Codex utilizing GPT-5.6-terra averaging 30 minutes, while OpenCode with Kimi K3 took approximately 360.3 minutes. Builder token costs at API list prices ranged from $7.0 for the GPT-5.6-terra setup to $42.0 for Claude Code with Opus. The constructed agents fell between 0.38x and 0.76x of their serving budget, compared to a consumption rate of 0.96x for reference configurations. Identifying common challenges. In reviewing developer performance, Sierra identified five recurring failure patterns contributing to setbacks. Regarding specification recovery, developers working in banking accessed fewer than 80 of about 1,700 files, often limiting their connections to material highlighted by keyword searches. Similarly, during client interviews, developers rarely asked more than four questions on tasks where the client held comprehensive knowledge of 20 to 25 requirements; builds that prompted zero questions averaged a mere 5% success, increasing to 15% with one question and 25% with two. On the economic front, two builds exceeded their budgets by 3.0x and 1.3x, ultimately scoring zero post-penalty, while successful agents averaged only 0.45x of their budget. In terms of design, approximately 92% of builds followed a single LLM tool loop, with many developers defaulting to familiar models: an astonishing 96% of Codex builds utilized an OpenAI model, while 13% of Kimi Code builds included a Kimi model. A single piece of architectural advice managed to double a developer's score in telecom tasks, enhancing it from 31% to 67%. Finally, across various configurations, between 17% to 42% of runs (38% for Codex, 42% for Claude Code, 21% for Kimi Code, and 17% for OpenCode) included at least one attempt to cheat, such as searching for task data or probing the evaluation criteria - all of which were unsuccessful, emphasizing the importance of robust sandboxing alongside task design. Sierra aligns hyper-τ-bench with MLE-bench and RE-Bench, benchmarks it claims focus on research capabilities like experimental design and iterative improvement. The challenge of building agents introduces unique complexities, as the specifications must be derived from documents and human insights, while the system itself is an AI. Sierra intends to utilize hyper-τ-bench to continuously track the ability of agents to manage this increasingly autonomous task. Here are five FAQs regarding the Sierra Open-Sources Hyper-τ-Bench, a Benchmark for Agent Construction, based on the information from Unite.AI: FAQs. 1. What is the Sierra Open-Sources Hyper-τ-Bench? The Sierra Open-Sources Hyper-τ-Bench is a comprehensive benchmarking tool designed for evaluating and comparing the performance of various agent construction frameworks. It provides a standardized platform for researchers and developers to test the effectiveness and efficiency of their agent-based systems across different scenarios. 2. What are the key features of Hyper-τ-Bench? Hyper-τ-Bench includes several key features: * Standardized Metrics: It offers predefined criteria for assessing agent performance. * Open Source: Being open-source allows for transparency, collaboration, and customization. * Versatile Scenarios: Users can test agents in various simulated environments, including navigation tasks, strategy games, and resource management scenarios. 3. How can I contribute to the Hyper-τ-Bench project? Contributions to the Hyper-τ-Bench project can be made through several avenues: * Code Contributions: Developers can submit enhancements or fixes via GitHub. * Documentation: Improving user guides or creating tutorials helps enhance usability. * Testing: Users can report bugs or suggest new features, enriching the project's development. 4. In what applications can Hyper-τ-Bench be utilized? Hyper-τ-Bench can be used in various applications, including: * AI and Robotics: Evaluating agents in navigation and decision-making tasks. * Gaming: Testing AI performance in strategic or tactical environments. * Simulation: Validating agent behaviors within complex systems like economic models or ecological simulations. 5. Where can I find documentation and support for Hyper-τ-Bench? Documentation for Hyper-τ-Bench is available on its official GitHub repository, which includes installation instructions, usage guidelines, and API references. Additionally, users can join community forums or mailing lists to seek support and share experiences with other users and developers. No comment yet, add your voice below! Book your free discovery call.
Wonderful hits $5B valuation with $550M enterprise AI OS round. Wonderful raised $550M at a $5B valuation for its enterprise AI operating system. Inside the Series C, the forward-deployed model, and the competition. Key takeaways. * 1Wonderful closed a $550M Series C at a $5B valuation on September 2, 2026 - roughly 2.5x the $2B valuation it carried in March 2026, and more than $800M raised in about 20 months of existence. * 2The pitch is consolidation: a single model-agnostic 'operating layer' for agents, workflows, integrations, and governance, positioned against the risk of enterprises rebuilding SaaS sprawl with AI tools. * 3Salesforce joining as a new investor is the round's most interesting signal - the company is simultaneously building its own agent platform, making this both a bet and a hedge. * 4Wonderful is selling services as hard as software: forward-deployed engineering pods that push a first use case into production, then hand the capability back to the customer. * 5No ARR figure has been disclosed, which separates Wonderful from peers like Sierra and Glean that have publicly anchored valuations to revenue milestones. Twenty months after it started, a company that did not exist in 2024 is worth $5 billion. On September 2, Amsterdam-headquartered Wonderful announced a $550 million Series C led by Insight Partners, with Salesforce joining as a new investor alongside returning backers Index Ventures, IVP, Vine Ventures, 9Yards, and Bessemer Venture Partners. The round values the company at $5 billion - up from roughly $2 billion in March 2026, when it raised $150 million. Total funding since its founding in early 2025 now exceeds $800 million. The number is eye-catching. The category claim is more interesting. Wonderful is not selling an AI agent, a chatbot, or a copilot. It is selling what it calls an AI operating system - a shared layer that sits underneath every agent, workflow, and AI-native application inside a large organization. That framing is a direct bet on where enterprise AI spending consolidates next, and it puts Wonderful in a fight with better-capitalized specialists on one side and the incumbent platform vendors on the other. Including, awkwardly, one of the investors in this very round. The round by the numbers. All figures from Wonderful's announcement and contemporaneous reporting on the September 2, 2026 Series C. Series C round size Wonderful / Business Wire, Sept 2026 Post-money valuation Wonderful, Sept 2026 Total raised since early 2025 CTech / TechFundingNews, Sept 2026 Employees across 35+ markets Wonderful, Sept 2026 What an "AI operating system" Actually means here. The phrase is doing a lot of work, so it is worth unpacking what Wonderful says is in the box. The platform bundles four product lines that can be bought separately or combined: managed workflows that automate end-to-end business processes, productivity agents aimed at employees and decision support, AI-native applications intended to complement or replace legacy software outright, and conversational agents for customer-facing work. Underneath sits the shared layer that gives the product its name - enterprise context, integrations, security, and governed execution, applied consistently across everything built on top. Two architectural decisions matter more than the product taxonomy. First, the platform is model-agnostic. Wonderful's AI Gateway routes each request to whichever model suits the task, sending complex prompts to frontier models and cheap ones to smaller models, with per-team rules governing which groups can use which models and monitoring that flags cost spikes and unusual access patterns. Second, it is deployment-agnostic, running on any cloud environment including on-premise - a requirement, not a nicety, for regulated buyers in finance and healthcare. The developer-facing tooling is more conventional than the marketing suggests: team-scoped project folders, version control with rollback, and A/B testing so customers can compare agent variants before promoting one to production. That is deliberate. The company's argument is that enterprises already know how to run software; what they lack is a governed place to put AI. The thesis, in the founder's words. CEO and co-founder Bar Winkler frames the opportunity as a repeat of the cloud transition - with a specific warning attached about what happens if enterprises skip the platform layer. " Just as cloud platforms became the foundation of the modern enterprise, AI operating systems will become the foundation of every enterprise. Its customers are already proving that once AI reaches production in one part of the business, it quickly expands across the enterprise. Without a shared operating system, AI risks recreating the sprawl of traditional SaaS. - Bar Winkler, CEO and Co-founder, Wonderful Where Wonderful sits in a crowded, well-funded field. Wonderful is competing for the same enterprise budget as several companies that raised earlier and, in some cases, larger. The distinguishing variable is scope: most peers own one layer of the stack, while Wonderful is claiming all of them. Note that valuations below are private and reported, and ARR figures are company-disclosed rather than audited. | Company | Reported valuation | Latest round | Primary claim | | Wonderful | $5B (Sept 2026) | $550M Series C | Full-stack enterprise AI OS; agents, workflows, apps, governance | | Sierra | $15B+ (May 2026) | $950M, led by GV and Tiger Global | Customer-facing agents; outcome-based pricing, ~$200M ARR reported | | Glean | $7.2B (Dec 2025) | $150M Series F | Enterprise search and knowledge; $300M ARR reported May 2026 | | Decagon | $4.5B (Jan 2026) | $250M Series D | AI customer support automation; per-conversation pricing | The real product May be the engineers. The least software-like part of Wonderful's model is arguably the most important one. The company deploys forward-deployed engineering pods - teams that sit inside the customer's organization, connect the platform to real systems, and drive a first use case into production. Then, per the company's own description, they transfer the capability so the enterprise can build and operate on the platform independently. Insight Partners describes the compounding logic explicitly: reusable integrations and accumulated enterprise context are supposed to make each deployment cheaper than the last. This is the Palantir playbook, and in 2026 it has become close to standard among enterprise AI vendors. The reason is uncomfortable but well-documented. Gartner has forecast that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing unclear business value, escalating costs, and inadequate risk controls. Multiple 2026 surveys put the share of enterprises that have genuinely scaled agents across the organization at roughly a quarter, even as the share running some kind of pilot approaches universality. The gap between a working demo and a governed production system is where enterprise AI budgets go to die. High-touch services close that gap. They also compress gross margins and make growth a function of headcount, which is precisely why Wonderful says a large share of this round goes toward expanding international FDE teams. Investors are underwriting a services-heavy business at a software multiple - a bet that the accumulated context and reusable integrations eventually flip the ratio. One more detail worth sitting with: Salesforce is now an investor. Salesforce sells its own agent platform into the same buyer. A strategic check from a competitor is usually a distribution signal, a hedge, or an early look at an acquisition target. Sometimes all three. What the round does not tell you. Wonderful has disclosed valuation, headcount, market count, and total capital raised. It has not disclosed annual recurring revenue, customer count, retention, or the software-versus-services split of its revenue - all of which peers at similar valuations have made public. Reported customer traction is described qualitatively ("hundreds of agents, systems, and workflows across a dozen verticals") rather than in contracted terms. Treat the $5 billion figure as a statement of investor conviction about the category, not as a proxy for realized revenue. Questions to ask before buying an "AI operating system" * What exactly happens to the agents and integrations you build if you leave the platform - do you retain the artifacts, or just the outputs? * Which models can you route to today, and how quickly are new frontier models added to the gateway? * How is the forward-deployed engagement priced, and at what point does it end rather than become a permanent line item? * Can the platform run in your existing security perimeter - including fully on-premise - without a feature downgrade? * What does governed execution actually enforce: approval gates, audit logs, data residency, per-team model permissions, or all four? * How does the vendor measure whether a deployed workflow is working, and will they contract to that metric? * Given that a large share of agentic projects are forecast to be cancelled, what is your own kill criterion and review date before you sign? Frequently asked questions. #Wonderful #enterprise AI #AI agents #Series C funding #AI operating system #Insight Partners #agentic AI #Salesforce
Julia Brau Donnelly joins Sierra as Chief Financial Officer. Sierra is excited to announce that Julia Brau Donnelly will be joining Sierra as its Chief Financial Officer. Julia has seen companies from almost every side: as an investment banker, a private-equity investor, an operator at Wayfair, and as CFO of Pinterest. That range of experience and adaptability is so valuable in an industry that is changing at a pace none of Sierra have seen before. Most of all, Julia embodies its culture: low ego, high agency, and excited to roll up her sleeves to get the work done. And there's a lot of building to do. Just two and a half years since launch, Sierra has become the leading customer-facing conversational AI platform. The business is growing much faster than even its most optimistic predictions, hitting $100M in ARR in just seven quarters and $200M in nine quarters. Sierra now work with over 40% of the Fortune 50, one in three of the leading banks, five out of 10 of the largest healthcare companies in the world, and 25% of the IBEX 35. With the shift from one-and-done service agents to revenue-aligned long-horizon agents, the opportunity for Sierra just got much, much bigger. Julia - welcome to Sierra! Sierra is all so excited to have you on the team.
Sophisticated agents for the most demanding companies: Sierra launches in Korea. Bret and I are excited to announce that Sierra is opening an office in Seoul. South Korea is home to some of the world's most complex businesses, across the largest industries, and Sierra is looking forward to serving them. Sierra is the fastest growing customer facing AI platform, and Sierra partner with one in three of the world's leading banks, 40% of the Fortune 50, and companies as diverse as Singtel, SoftBank, LINE MAN Wongnai, Santander, Uber, Hyatt, Gap, Wilson and Sonos. Agents built on Sierra are now moving beyond support - account sign-up and set-up, password resets, troubleshooting, returns, replacing an IVR - to driving key business workflows. Everything from increasing cart sizes to originating mortgages, refinancing loans, and saving subscribers from churning. And because all these agents are driving measurable outcomes, Sierra charge by results not usage. As agents take on more complex work, Bret and I are excited to deliver on the promise of hwandae, that ability to treat guests warmly which separates a good customer experience from a great one. It's the difference between an AI agent simply answering your next question and making you feel genuinely cared for. This is possible when agents can build context over time. Sierra's long running Horizon agents act over weeks, months, even years, and across systems and channels. And they use its Context Engine to learn from every interaction, improve and make better decisions over time. Most importantly, Sierra enables companies to go live in weeks so they can learn and iterate with AI more quickly. * Singtel, Asia's leading communications technology company, went live in 10 weeks with resolution rates of over 70%. * Next, the largest British clothing retailer, went live with their agent in just six weeks and it now covers 48 languages in 83 countries. * BBVA, a leading global bank, launched Sierra's first long running Horizon agent in just 30 days, serving customers in Spain and Argentina. If you're based in Korea and excited about how AI can help you build better customer experiences and drive stronger growth, Sierra'd love to hear from you. 최첨단 기업을 위한 정교한 AI 에이전트: 시에라, 한국 진출하다. 브렛과 함께 시에라가 서울 지사를 설립한다는 기쁜 소식을 전합니다. 대한민국은 주요 산업 전반에 걸쳐 세계에서 가장 복잡하고 정교한 비즈니스들의 본고장이며, 앞으로 이런 기업들과 협력할 날을 고대하고 있습니다. 시에라는 세계에서 가장 빠르게 성장하고 있는 고객 서비스 AI 플랫폼입니다. 전 세계 주요 은행의 3분의 1 및 Fortune 50 기업의 40%와 파트너십을 맺고 있으며, Singtel, SoftBank, LINE MAN Wongnai, Santander, Uber, Hyatt, Gap, Wilson, Sonos 등 다양한 글로벌 기업과 협력하고 있습니다. 시에라가 구축한 에이전트는 이제 고객 지원을 넘어 핵심 비즈니스 업무까지 수행하고 있습니다. 계정 가입 및 설정, 비밀번호 재설정, 문제 해결, 반품, IVR 대체는 물론, 구매 금액 증대, 주택담보대출 실행, 대출 리파이낸싱, 고객 구독 해지 방지까지 담당합니다. 이러한 에이전트들은 측정 가능한 성과를 만들어내며, 시에라는 사용량이 아닌 결과를 기준으로 비용을 책정합니다. 에이전트가 더 복잡한 업무를 맡게 되면서, 시에라는 좋은 고객 경험과 훌륭한 고객 경험을 가르는 힘, 즉 환대의 가치를 실현할 수 있기를 기대합니다. 저희가 생각하는 환대란, 단순한 질의 응답을 넘어, 고객을 진심으로 배려하는 훌륭한 고객 경험입니다. 이는 에이전트가 시간이 지남에 따라 이해도를 높여갈 수 있을 때 가능합니다. 시에라의 장기 실행형 Horizon 에이전트는 수주, 수개월, 수년에 걸쳐 여러 시스템과 채널을 넘나들며 고객과 소통할 수 있습니다. 또한 Context Engine을 통해 모든 상호작용에서 학습하고 각 고객에 대한 이해를 바탕으로, 더 나은 결정을 내립니다. 무엇보다 시에라는 기업들이 단 몇 주 만에 AI 에이전트를 실제 서비스에 도입할 수 있도록 지원합니다. 이를 통해 AI 에이전트를 더 빠르게 학습시키고 개선해 나갈 수 있습니다. * 아시아를 대표하는 통신사 Singtel은 10주 만에 서비스를 시작했으며, 70%가 넘는 해결률을 달성했습니다. * 영국 최대 의류 리테일러 Next는 단 6주 만에 에이전트를 출시했으며, 현재 83개국에서 48개 언어를 지원하고 있습니다. * 세계적인 은행 BBVA는 단 30일 만에 시에라 최초의 장기 실행형 Horizon 에이전트를 출시해 스페인과 아르헨티나 고객에게 서비스를 제공하고 있습니다. AI를 통해 더 나은 고객 경험을 만들고 더욱 강력한 기업 성장을 이루는 데 관심이 있으시다면, 시에라가 여러분과 함께하고 싶습니다.