Compensation & Total Rewards

We just closed an employee tender offer which values ElevenLabs at $22BN, up 2x from our Series D in February. The tender was led by Wellington and T. Rowe Price, and the growth is driven by demand from enterprises to deploy ElevenAgents across sales, support, and operations. In 2022, we raised our first round at a $9 million valuation, which we announced alongside Eleven v1, our first Text to Speech model, and the first model to generate human-level speech. Four years later, as we reach our new valuation, we just released two new state-of-the-art Text to Speech models. Eleven v4 and v4 Turbo are our fastest and most emotive models yet, and are leading independent benchmarks. Alongside models, we have built a full interaction platform for AI. It allows organizations to deploy conversational agents, connected to their knowledge and systems, that interact naturally in any industry, geography, or context. This shift is driving much of our growth, with enterprise now accounting for 55% of our revenue, as businesses adopt ElevenAgents to support their customers and employees. Our technology is now used in daily operations at five of the world’s ten largest tech companies, five of the ten largest insurers, and four of the ten largest telecoms. The round was co-led by Wellington Management and T. Rowe Price, along with participation from Goldman Sachs, GIC, Ontario Teachers'​ Pension Plan, EQT Group, Sapphire Ventures, and BDT & MSD Partners. And grateful for ongoing support from existing investors including Andreessen Horowitz, Alkeon Capital, The D. E. Shaw Group, Disruptive, Evantic Capital, ICONIQ, and Lightspeed. Most of all, this round is for the team that built the last four years. We're just getting started! | 89 comments on LinkedIn

ElevenLabs opens Brussels office and plans to triple its Belgian team. The voice AI company, valued at $11 billion, already works with Mediafin, Corilus and the European Union in Belgium. September 30, 2026 - 2:51 pm ElevenLabs has launched in Belgium and opened an office in Brussels, the voice AI company said on Wednesday. The office is in the European District, close to the main EU buildings. ElevenLabs has named Falke Van Onacker to lead its Belgian business and plans to triple its local team this year. Most new staff will work in Brussels. The company said Belgium is a good fit because the country has three official languages and that more than 100 languages are spoken in Brussels every day. Its models can speak, listen, and translate in over 90 languages. "Belgium, and in particular Brussels, is one of the most interesting places in the world for voice and language," said Mati Staniszewski, ElevenLabs' cofounder and chief executive. Several Belgian firms already use its tools. Staffing group Glowi uses ElevenLabs voice agents to screen job seekers and book calls in Dutch, French and English. Meanwhile, Corilus, which develops software for doctors, uses the ElevenScribe tool to transcribe Flemish patient calls. The data stays in the EU and is not kept. Cavell, an AI helper for doctors, uses ElevenLabs to turn speech into medical notes and is rolling out to 8,000 doctors. In media, Mediafin, which owns De Tijd and L'Echo, turns up to 10 million characters of text a month into Dutch and French audio. The EU has also used ElevenLabs to dub press conferences into several languages. In addition, about 20 Belgian groups get ElevenLabs grants for work in schools, the arts and access for disabled people. "Demand for care, assistance, and human expertise continues to rise. We're building localized voice agents to extend expertise in every language and at every hour here," said Van Onacker. The Belgian launch follows a $500 million Series D in February that valued ElevenLabs at $11 billion. Earlier this month, the company also signed a licensing deal with Universal Music, its first with a major label. ElevenLabs is now hiring in Belgium for sales, tech, and marketing jobs, according to its careers page. Most of the jobs ask for English, French, and Dutch.

ElevenLabs' Scribe v2 now edits transcripts without a separate AI call. ElevenLabs adds natural-language instructions to Scribe v2 transcription, letting you reformat, redact, or annotate transcripts in one API call for a 30% surcharge. · 17 hrs ago Read 4 min * ElevenLabs launched Transcript Editing for Scribe v2 and Scribe v2 Realtime speech-to-text. * Pass a natural-language transcript_edit instruction, up to 2,000 characters, per request. * API returns both original transcript with word timestamps and an edited_transcript field. * Supports date/time normalization, abbreviation expansion, redaction, sentiment tagging, and full reformatting. * Costs 30% on top of base transcription, with a 10-second minimum billed per request. * Cannot combine with entity detection, entity redaction, or multi-channel transcription requests. ElevenLabs has added instruction-based post-processing to its speech-to-text API. The experimental Transcript Editing feature accepts a plain-English editing instruction with a Scribe v2 or Scribe v2 Realtime request, then returns the original transcript alongside a rewritten version. Developers can use the feature to normalize dates, expand abbreviations, mask sensitive language, add labels, or restructure text without making a separate large language model call. That removes a network round trip and reduces the prompt, retry, and billing logic required for a separate post-processing service. One request, two outputs. A batch request includes a transcript_edit parameter of up to 2,000 characters. Scribe first transcribes the audio, then applies the instruction across the completed transcript wherever it is relevant. | Output | Contents | Typical use | | text | Original transcript | Search, auditing, and source retention | | Word timestamps | Original timing data | Captions, alignment, and indexing | | edited_transcript | Instruction-shaped text and status information | Display, export, or downstream processing | The editing pass leaves the original text and word-level timestamps unchanged. Applications that require exact alignment should continue using those original fields because rewritten text can differ in wording and structure. Edits suited to transcript cleanup. ElevenLabs documents several editing patterns that fit the feature's transcript-focused scope: * Normalize spoken dates as YYYY-MM-DD or convert times to 24-hour notation. * Expand abbreviations on first use, such as changing ETA to estimated time of arrival (ETA). * Mask profane or sensitive words with a specified replacement pattern. * Attach sentiment labels selected from a fixed list. * Restructure a transcript as a bulleted list with one sentence per item. One instruction can combine several operations, and the editor applies each applicable rule throughout the transcript rather than stopping after the first match. A batch request in Python. The Python SDK exposes transcript editing as an additional argument on the standard conversion call: transcription = elevenlabs.speech_to_text.convert( file=audio_file, model_id="scribe_v2", transcript_edit="Write all dates in ISO 8601 format (YYYY-MM-DD)",) print("Original:", transcription.text) print("Edited:", transcription.edited_transcript) The response includes an edited_transcript object with a kind field. A successful edit contains the rewritten text. If editing fails after transcription succeeds, the API still returns the original transcript, allowing the application to preserve or process the unedited result. Realtime editing waits for committed text. Scribe v2 Realtime accepts the instruction when the WebSocket connection opens. Each committed transcript is then followed by an edited_transcript event, as described in the realtime guide. Partial transcripts are excluded from the editing pass. Processing only committed text reduces repeated work and prevents an interface from displaying rewrites of incomplete utterances. The surcharge and hard limits. Transcript editing adds 30% to the base transcription cost, with billing subject to a minimum of 10 seconds of audio per request. Teams evaluating the feature should also account for these constraints: * The edit begins after transcription, so total latency increases with transcript length. * Instructions can use any language, although ElevenLabs says English performs best. * The edited output remains in the source language. * Spoken audio is treated as data rather than as editing instructions, limiting prompt-injection attempts embedded in the recording. * A request cannot combine transcript editing with entity_detection, entity_redaction, or use_multi_channel. Use cheaper controls first. Several deterministic transcription options cover common cases at lower cost and with more predictable behavior: * keyterm prompting biases recognition toward names, product terms, and specialized vocabulary. * no_verbatim removes filler words and disfluencies. * numbers_format, where supported, controls whether numbers appear as digits or words. Transcript editing is better suited to transformations those controls cannot express, including schema-ready dates, fixed sentiment labels, profanity masking, and UI-specific formatting. Where it fits in a pipeline. Applications that already send every transcript to a separate language model may gain little from moving the edit into Scribe. A dedicated post-processing stage provides control over model selection, prompt versioning, caching, validation, and intermediate output. Applications with narrow, repeatable cleanup requirements can remove that extra orchestration step by using the integrated editor. The decision depends on whether the 30% surcharge costs less than operating a separate model call and whether Scribe's editing behavior provides enough control for the required output. Normalization, redaction, labeling, and structural reformatting fit the feature's design. Summarization, inference, and multi-step reasoning remain stronger candidates for a dedicated downstream model.